@article{Baumanns1836113, author = {Baumanns, Lukas and Pitta-Pantazi, Demetra and Demosthenous, Eleni and Lilienthal, Achim J. and Christou, Constantinos and Schindler, Maike}, institution = {Örebro University, School of Science and Technology}, institution = {Technical University Dortmund, Dortmund, Germany}, institution = {University of Cyprus, Nicosia, Cyprus}, institution = {University of Cyprus, Nicosia, Cyprus}, institution = {TU Munich, Munich, Germany}, institution = {University of Cyprus, Nicosia, Cyprus}, institution = {University of Cologne, Cologne, Germany}, journal = {International Journal of Science and Mathematics Education}, note = {Open Access funding enabled and organized by Projekt DEAL. This publication has received funding from the Erasmus + grant program of the European Union under grant agreement No 2020–1-DE03-KA201-077597.}, title = {Pattern-Recognition Processes of First-Grade Students : An Explorative Eye-Tracking Study}, DOI = {10.1007/s10763-024-10441-x}, keywords = {Pattern recognition, Eye tracking, Mathematical difficulties, First-grade students}, abstract = {Recognizing patterns is an essential skill in early mathematics education. However, first graders often have difficulties with tasks such as extending patterns of the form ABCABC. Studies show that this pattern-recognition ability is a good predictor of later pre-algebraic skills and mathematical achievement in general, or the development of mathematical difficulties on the other hand. To be able to foster children's pattern-recognition ability, it is crucial to investigate and understand their pattern-recognition processes early on. However, only a few studies have investigated the processes used to recognize patterns and how these processes are adapted to different patterns. These studies used external observations or relied on children's self-reports, yet young students often lack the ability to properly report their strategies. This paper presents the results of an empirical study using eye-tracking technology to investigate the pattern-recognition processes of 22 first-grade students. In particular, we investigated students with and without the risk of developing mathematical difficulties. The analyses of the students' eye movements reveal that the students used four different processes to recognize patterns-a finding that refines knowledge about pattern-recognition processes from previous research. In addition, we found that for patterns with different units of repeat (i.e. ABABAB versus ABCABCABC), the pattern-recognition processes used differed significantly for students at risk of developing mathematical difficulties but not for students without such risk. Our study contributes to a better understanding of the pattern-recognition processes of first-grade students, laying the foundation for enhanced, targeted support, especially for students at risk of developing mathematical difficulties. }, year = {2024} } @article{Pitta-Pantazi1836032, author = {Pitta-Pantazi, Demetra and Demosthenous, Eleni and Schindler, Maike and Lilienthal, Achim J. and Christou, Constantinos}, institution = {Örebro University, School of Science and Technology}, institution = {University of Cyprus, Nicosia, Cyprus}, institution = {University of Cyprus, Nicosia, Cyprus}, institution = {University of Cologne, Cologne, Germany}, institution = {TU München, Munich, Germany}, institution = {University of Cyprus, Nicosia, Cyprus}, journal = {Educational Studies in Mathematics}, title = {Structure sense in students' quantity comparison and repeating pattern extension tasks : an eye-tracking study with first graders}, DOI = {10.1007/s10649-023-10290-5}, keywords = {Eye tracking, Quantity comparison, Repeating pattern extension, Structure sense, Serial strategies}, abstract = {There is growing evidence that the ability to perceive structure is essential for students' mathematical development. Looking at students' structure sense in basic numerical and patterning tasks seems promising for understanding how these tasks set the foundation for the development of later mathematical skills. Previous studies have shown how students use structure sense in enumeration tasks. However, little is known about students' use of structure sense in other early mathematical tasks. The main aim of this study is to investigate the ways in which structure sense is manifested in first-grade students' work across tasks, in quantity comparison and repeating pattern extension tasks. We investigated students' strategies in quantity comparison and pattern extension tasks and how students employ structure sense. We conducted an eye-tracking study with 21 first-grade students, which provided novel insights into commonalities among strategies for these types of tasks. We found that for both tasks, quantity comparison and repeating pattern extension tasks, strategies can be distinguished into those employing structure sense and serial strategies. }, year = {2024} } @inproceedings{Schreiter1830088, author = {Schreiter, Tim and Morillo-Mendez, Lucas and Chadalavada, Ravi T. and Rudenko, Andrey and Billing, Erik and Magnusson, Martin and Arras, Kai O. and Lilienthal, Achim J.}, booktitle = {2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) : Proceedings}, institution = {Örebro University, School of Science and Technology}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany}, institution = {Interaction Lab, University of Skövde, Skövde, Sweden}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany}, institution = {TU Munich, Germany}, pages = {293--300}, title = {Advantages of Multimodal versus Verbal-Only Robot-to-Human Communication with an Anthropomorphic Robotic Mock Driver}, series = {IEEE RO-MAN}, DOI = {10.1109/RO-MAN57019.2023.10309629}, abstract = {Robots are increasingly used in shared environments with humans, making effective communication a necessity for successful human-robot interaction. In our work, we study a crucial component: active communication of robot intent. Here, we present an anthropomorphic solution where a humanoid robot communicates the intent of its host robot acting as an "Anthropomorphic Robotic Mock Driver" (ARMoD). We evaluate this approach in two experiments in which participants work alongside a mobile robot on various tasks, while the ARMoD communicates a need for human attention, when required, or gives instructions to collaborate on a joint task. The experiments feature two interaction styles of the ARMoD: a verbal-only mode using only speech and a multimodal mode, additionally including robotic gaze and pointing gestures to support communication and register intent in space. Our results show that the multimodal interaction style, including head movements and eye gaze as well as pointing gestures, leads to more natural fixation behavior. Participants naturally identified and fixated longer on the areas relevant for intent communication, and reacted faster to instructions in collaborative tasks. Our research further indicates that the ARMoD intent communication improves engagement and social interaction with mobile robots in workplace settings. }, ISBN = {9798350336702}, ISBN = {9798350336719}, year = {2023} } @inproceedings{Zhu1832141, author = {Zhu, Yufei and Rudenko, Andrey and Kucner, Tomasz and Palmieri, Luigi and Arras, Kai and Lilienthal, Achim and Magnusson, Martin}, booktitle = {2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 01-05 October 2023, Detroit, MI, USA : }, institution = {Örebro University, School of Science and Technology}, institution = {Bosch Corporate Research, Robert Bosch GmbH, Stuttgart, Germany}, institution = {Finnish Center for Artificial Intelligence, School of Electrical Engineering, Aalto University, Finland}, institution = {Bosch Corporate Research, Robert Bosch GmbH, Stuttgart, Germany}, institution = {Bosch Corporate Research, Robert Bosch GmbH, Stuttgart, Germany}, institution = {TU Munich, Germany}, pages = {3795--3802}, title = {CLiFF-LHMP : Using Spatial Dynamics Patterns for Long-Term Human Motion Prediction}, series = {IEEE International Conference on Intelligent Robots and Systems. Proceedings}, DOI = {10.1109/IROS55552.2023.10342031}, abstract = {Human motion prediction is important for mobile service robots and intelligent vehicles to operate safely and smoothly around people. The more accurate predictions are, particularly over extended periods of time, the better a system can, e.g., assess collision risks and plan ahead. In this paper, we propose to exploit maps of dynamics (MoDs, a class of general representations of place-dependent spatial motion patterns, learned from prior observations) for long-term human motion prediction (LHMP). We present a new MoD-informed human motion prediction approach, named CLiFF-LHMP, which is data efficient, explainable, and insensitive to errors from an upstream tracking system. Our approach uses CLiFF -map, a specific MoD trained with human motion data recorded in the same environment. We bias a constant velocity prediction with samples from the CLiFF-map to generate multi-modal trajectory predictions. In two public datasets we show that this algorithm outperforms the state of the art for predictions over very extended periods of time, achieving 45 % more accurate prediction performance at 50s compared to the baseline. }, ISBN = {9781665491914}, ISBN = {9781665491907}, year = {2023} } @article{Adolfsson1727222, author = {Adolfsson, Daniel and Magnusson, Martin and Alhashimi, Anas and Lilienthal, Achim and Andreasson, Henrik}, institution = {Örebro University, School of Science and Technology}, institution = {Örebro University, Örebro, Sweden; Computer Engineering Department, University of Baghdad, Baghdad, Iraq}, journal = {IEEE Transactions on robotics}, number = {2}, pages = {1476--1495}, title = {Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large-Scale Radar Odometry in Diverse Environments}, volume = {39}, DOI = {10.1109/tro.2022.3221302}, keywords = {Radar, Sensors, Spinning, Azimuth, Simultaneous localization and mapping, Estimation, Location awareness, Localization, radar odometry, range sensing, SLAM}, abstract = {This article presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments—outdoors, from urban to woodland, and indoors in warehouses and mines—without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach conservative filtering for efficient and accurate radar odometry (CFEAR), we present an in-depth investigation on a wider range of datasets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar simultaneous localization and mapping (SLAM) and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5 Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160 Hz. }, URL = {https://doi.org/10.48550/arXiv.2211.02445}, year = {2023} } @article{Gupta1761421, author = {Gupta, Himanshu and Lilienthal, Achim and Andreasson, Henrik and Kurtser, Polina}, institution = {Örebro University, School of Science and Technology}, institution = {Perception for Intelligent Systems, TechnicalUniversity of Munich, Munich, Germany}, institution = {Centre for Applied Autonomous SensorSystems, Institutionen för naturvetenskap &teknik, Örebro University, Örebro, Sweden; Department of Radiation Science, RadiationPhysics, Umeå University, Umeå, Sweden}, journal = {Journal of Field Robotics}, number = {6}, pages = {1603--1619}, title = {NDT-6D for color registration in agri-robotic applications}, volume = {40}, DOI = {10.1002/rob.22194}, keywords = {agricultural robotics, color pointcloud, in-field sensing, machine perception, RGB-D registration, stereo IR, vineyard}, abstract = {Registration of point cloud data containing both depth and color information is critical for a variety of applications, including in-field robotic plant manipulation, crop growth modeling, and autonomous navigation. However, current state-of-the-art registration methods often fail in challenging agricultural field conditions due to factors such as occlusions, plant density, and variable illumination. To address these issues, we propose the NDT-6D registration method, which is a color-based variation of the Normal Distribution Transform (NDT) registration approach for point clouds. Our method computes correspondences between pointclouds using both geometric and color information and minimizes the distance between these correspondences using only the three-dimensional (3D) geometric dimensions. We evaluate the method using the GRAPES3D data set collected with a commercial-grade RGB-D sensor mounted on a mobile platform in a vineyard. Results show that registration methods that only rely on depth information fail to provide quality registration for the tested data set. The proposed color-based variation outperforms state-of-the-art methods with a root mean square error (RMSE) of 1.1-1.6 cm for NDT-6D compared with 1.1-2.3 cm for other color-information-based methods and 1.2-13.7 cm for noncolor-information-based methods. The proposed method is shown to be robust against noises using the TUM RGBD data set by artificially adding noise present in an outdoor scenario. The relative pose error (RPE) increased similar to 14% for our method compared to an increase of similar to 75% for the best-performing registration method. The obtained average accuracy suggests that the NDT-6D registration methods can be used for in-field precision agriculture applications, for example, crop detection, size-based maturity estimation, and growth modeling. }, year = {2023} } @inproceedings{Gupta1812049, author = {Gupta, Himanshu and Andreasson, Henrik and Magnusson, Martin and Julier, Simon and Lilienthal, Achim J.}, booktitle = {2023 European Conference on Mobile Robots (ECMR) : }, institution = {Örebro University, School of Science and Technology}, institution = {Department of Computer Science, University College London, London, England}, institution = {Perception for Intelligent Systems, Technical University of Munich, Germany }, pages = {43--48}, publisher = {IEEE}, title = {Revisiting Distribution-Based Registration Methods}, series = {European Conference on Mobile Robots}, DOI = {10.1109/ECMR59166.2023.10256416}, abstract = {Normal Distribution Transformation (NDT) registration is a fast, learning-free point cloud registration algorithm that works well in diverse environments. It uses the compact NDT representation to represent point clouds or maps as a spatial probability function that models the occupancy likelihood in an environment. However, because of the grid discretization in NDT maps, the global minima of the registration cost function do not always correlate to ground truth, particularly for rotational alignment. In this study, we examined the NDT registration cost function in-depth. We evaluated three modifications (Student-t likelihood function, inflated covariance/heavily broadened likelihood curve, and overlapping grid cells) that aim to reduce the negative impact of discretization in classical NDT registration. The first NDT modification improves likelihood estimates for matching the distributions of small population sizes; the second modification reduces discretization artifacts by broadening the likelihood tails through covariance inflation; and the third modification achieves continuity by creating the NDT representations with overlapping grid cells (without increasing the total number of cells). We used the Pomerleau Dataset evaluation protocol for our experiments and found significant improvements compared to the classic NDT D2D registration approach (27.7% success rate) using the registration cost functions "heavily broadened likelihood NDT" (HBL-NDT) (34.7% success rate) and "overlapping grid cells NDT" (OGC-NDT) (33.5% success rate). However, we could not observe a consistent improvement using the Student-t likelihood-based registration cost function (22.2% success rate) over the NDT P2D registration cost function (23.7% success rate). A comparative analysis with other state-of-art registration algorithms is also presented in this work. We found that HBL-NDT worked best for easy initial pose difficulties scenarios making it suitable for consecutive point cloud registration in SLAM application. }, ISBN = {9798350307047}, ISBN = {9798350307054}, year = {2023} } @article{Gupta1770024, author = {Gupta, Himanshu and Andreasson, Henrik and Lilienthal, Achim J. and Kurtser, Polina}, institution = {Örebro University, School of Science and Technology}, institution = {Perception for Intelligent Systems, Technical University of Munich, Munich, Germany}, institution = {Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden; Department of Radiation Science, Radiation Physics, Umeå University, Umeå, Sweden}, journal = {Sensors}, number = {10}, eid = {4736}, title = {Robust Scan Registration for Navigation in Forest Environment Using Low-Resolution LiDAR Sensors}, volume = {23}, DOI = {10.3390/s23104736}, keywords = {tree segmentation, LiDAR mapping, forest inventory, SLAM, forestry robotics, scan registration}, abstract = {Automated forest machines are becoming important due to human operators' complex and dangerous working conditions, leading to a labor shortage. This study proposes a new method for robust SLAM and tree mapping using low-resolution LiDAR sensors in forestry conditions. Our method relies on tree detection to perform scan registration and pose correction using only low-resolution LiDAR sensors (16Ch, 32Ch) or narrow field of view Solid State LiDARs without additional sensory modalities like GPS or IMU. We evaluate our approach on three datasets, including two private and one public dataset, and demonstrate improved navigation accuracy, scan registration, tree localization, and tree diameter estimation compared to current approaches in forestry machine automation. Our results show that the proposed method yields robust scan registration using detected trees, outperforming generalized feature-based registration algorithms like Fast Point Feature Histogram, with an above 3 m reduction in RMSE for the 16Chanel LiDAR sensor. For Solid-State LiDAR the algorithm achieves a similar RMSE of 3.7 m. Additionally, our adaptive pre-processing and heuristic approach to tree detection increased the number of detected trees by 13% compared to the current approach of using fixed radius search parameters for pre-processing. Our automated tree trunk diameter estimation method yields a mean absolute error of 4.3 cm (RSME = 6.5 cm) for the local map and complete trajectory maps. }, year = {2023} } @article{Kucner1792782, author = {Kucner, Tomasz Piotr and Magnusson, Martin and Mghames, Sariah and Palmieri, Luigi and Verdoja, Francesco and Swaminathan, Chittaranjan Srinivas and Krajnik, Tomas and Schaffernicht, Erik and Bellotto, Nicola and Hanheide, Marc and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Mobile Robotics Group, School of Electrical Engineering, Aalto University, Finland; Finnish Center for Artificial Intelligence, Finland}, institution = {L-CAS, School of Computer Science, University of Lincoln, Lincoln, UK}, institution = {BOSCH Corporate Research, Renningen, Germany}, institution = {Intelligent Robotics Group, School of Electrical Engineering, Aalto University, Finland}, institution = {Artificial Intelligence Center, Czech Technical University, Praha, Czechia}, institution = {L-CAS, School of Computer Science, University of Lincoln, Lincoln, UK; Department of Information Engineering, Univeristy of Padua, Padova, Italy}, institution = {L-CAS, School of Computer Science, University of Lincoln, Lincoln, UK}, institution = {Technical Univeristy of Munich, Munich, Germany}, journal = {The international journal of robotics research}, note = {Funding agencies:Czech Ministry of Education by OP VVV CZ.02.1.01/0.0/0.0/16 019/0000765Business Finland 9249/31/2021 }, number = {11}, pages = {977--1006}, title = {Survey of maps of dynamics for mobile robots}, volume = {42}, DOI = {10.1177/02783649231190428}, keywords = {mapping, maps of dynamics, localization and mapping, acceptability and trust, human-robot interaction, human-aware motion planning}, abstract = {Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area. }, year = {2023} } @article{Molina1797296, author = {Molina, Sergi and Mannucci, Anna and Magnusson, Martin and Adolfsson, Daniel and Andreasson, Henrik and Hamad, Mazin and Abdolshah, Saeed and Chadalavada, Ravi Teja and Palmieri, Luigi and Linder, Timm and Swaminathan, Chittaranjan Srinivas and Kucner, Tomasz Piotr and Hanheide, Marc and Fernandez-Carmona, Manuel and Cielniak, Grzegorz and Duckett, Tom and Pecora, Federico and Bokesand, Simon and Arras, Kai O. and Haddadin, Sami and Lilienthal, Achim J}, institution = {Örebro University, School of Science and Technology}, institution = {University of Lincoln, Lincoln, U.K}, institution = {Robert Bosch GmbH, Renningen, Germany}, institution = {Technical University of Munich, Munich, Germany}, institution = {Technical University of Munich, Munich, Germany}, institution = {Robert Bosch GmbH, Renningen, Germany}, institution = {Robert Bosch GmbH, Renningen, Germany}, institution = {Aalto University, Aalto, Finland}, institution = {University of Lincoln, Lincoln, U.K.}, institution = {University of Lincoln, Lincoln, U.K.}, institution = {University of Lincoln, Lincoln, U.K.}, institution = {University of Lincoln, Lincoln, U.K.}, institution = {Kollmorgen Automation AB, Mölndal, Sweden}, institution = {Robert Bosch GmbH, Renningen, Germany}, institution = {Technical University of Munich, Munich, Germany}, journal = {IEEE robotics & automation magazine}, title = {The ILIAD Safety Stack : Human-Aware Infrastructure-Free Navigation of Industrial Mobile Robots}, DOI = {10.1109/MRA.2023.3296983}, keywords = {Robots, Safety, Navigation, Mobile robots, Human-robot interaction, Hidden Markov models, Trajectory}, abstract = {Current intralogistics services require keeping up with e-commerce demands, reducing delivery times and waste, and increasing overall flexibility. As a consequence, the use of automated guided vehicles (AGVs) and, more recently, autonomous mobile robots (AMRs) for logistics operations is steadily increasing. }, year = {2023} } @inproceedings{Almeida1808690, author = {Almeida, Tiago and Rudenko, Andrey and Schreiter, Tim and Zhu, Yufei and Guti{\’e;}rrez Maestro, Eduardo and Morillo-Mendez, Lucas and Kucner, Tomasz P. and Martinez Mozos, Oscar and Magnusson, Martin and Palmieri, Luigi and Arras, Kai O. and Lilienthal, Achim}, booktitle = {2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) : }, institution = {Örebro University, School of Science and Technology}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany}, institution = {Mobile Robotics Group, Department of Electrical Engineering and Automation, Aalto University, Finland; FCAI, Finnish Center for Artificial Intelligence, Finland}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany}, pages = {2192--2201}, title = {TH{\"O}R-Magni : Comparative Analysis of Deep Learning Models for Role-Conditioned Human Motion Prediction}, series = {IEEE International Conference on Computer Vision Workshop (ICCVW)}, DOI = {10.1109/ICCVW60793.2023.00234}, abstract = {Autonomous systems, that need to operate in human environments and interact with the users, rely on understanding and anticipating human activity and motion. Among the many factors which influence human motion, semantic attributes, such as the roles and ongoing activities of the detected people, provide a powerful cue on their future motion, actions, and intentions. In this work we adapt several popular deep learning models for trajectory prediction with labels corresponding to the roles of the people. To this end we use the novel THOR-Magni dataset, which captures human activity in industrial settings and includes the relevant semantic labels for people who navigate complex environments, interact with objects and robots, work alone and in groups. In qualitative and quantitative experiments we show that the role-conditioned LSTM, Transformer, GAN and VAE methods can effectively incorporate the semantic categories, better capture the underlying input distribution and therefore produce more accurate motion predictions in terms of Top-K ADE/FDE and log-likelihood metrics. }, URL = {https://openaccess.thecvf.com/content/ICCV2023W/JRDB/papers/de_Almeida_THOR-Magni_Comparative_Analysis_of_Deep_Learning_Models_for_Role-Conditioned_Human_ICCVW_2023_paper.pdf}, ISBN = {9798350307450}, ISBN = {9798350307443}, year = {2023} } @article{Swaminathan1713186, author = {Swaminathan, Chittaranjan Srinivas and Kucner, Tomasz Piotr and Magnusson, Martin and Palmieri, Luigi and Molina, Sergi and Mannucci, Anna and Pecora, Federico and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Finnish Centre for Artificial Intelligence (FCAI), Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland}, institution = {Robert Bosch GmbH Corporate Research, Stuttgart, Germany}, institution = {Lincoln Centre for Autonomous Systems, School of Computer Science, University of Lincoln, Lincoln, United Kingdom}, institution = {School of Science and Technology, Örebro University, Örebro, Sweden}, journal = {Frontiers in Robotics and AI}, eid = {916153}, title = {Benchmarking the utility of maps of dynamics for human-aware motion planning}, volume = {9}, DOI = {10.3389/frobt.2022.916153}, keywords = {ATC, benchmarking, dynamic environments, human-aware motion planning, human-populated environments, maps of dynamics}, abstract = {Robots operating with humans in highly dynamic environments need not only react to moving persons and objects but also to anticipate and adhere to patterns of motion of dynamic agents in their environment. Currently, robotic systems use information about dynamics locally, through tracking and predicting motion within their direct perceptual range. This limits robots to reactive response to observed motion and to short-term predictions in their immediate vicinity. In this paper, we explore how maps of dynamics (MoDs) that provide information about motion patterns outside of the direct perceptual range of the robot can be used in motion planning to improve the behaviour of a robot in a dynamic environment. We formulate cost functions for four MoD representations to be used in any optimizing motion planning framework. Further, to evaluate the performance gain through using MoDs in motion planning, we design objective metrics, and we introduce a simulation framework for rapid benchmarking. We find that planners that utilize MoDs waste less time waiting for pedestrians, compared to planners that use geometric information alone. In particular, planners utilizing both intensity (proportion of observations at a grid cell where a dynamic entity was detected) and direction information have better task execution efficiency. }, year = {2022} } @article{Adolfsson1689786, author = {Adolfsson, Daniel and Castellano-Quero, Manuel and Magnusson, Martin and Lilienthal, Achim J. and Andreasson, Henrik}, institution = {Örebro University, School of Science and Technology}, journal = {Robotics and Autonomous Systems}, eid = {104136}, title = {CorAl : Introspection for robust radar and lidar perception in diverse environments using differential entropy}, volume = {155}, DOI = {10.1016/j.robot.2022.104136}, keywords = {Radar, Introspection, Localization}, abstract = {Robust perception is an essential component to enable long-term operation of mobile robots. It depends on failure resilience through reliable sensor data and pre-processing, as well as failure awareness through introspection, for example the ability to self-assess localization performance. This paper presents CorAl: a principled, intuitive, and generalizable method to measure the quality of alignment between pairs of point clouds, which learns to detect alignment errors in a self-supervised manner. CorAl compares the differential entropy in the point clouds separately with the entropy in their union to account for entropy inherent to the scene. By making use of dual entropy measurements, we obtain a quality metric that is highly sensitive to small alignment errors and still generalizes well to unseen environments. In this work, we extend our previous work on lidar-only CorAl to radar data by proposing a two-step filtering technique that produces high-quality point clouds from noisy radar scans. Thus, we target robust perception in two ways: by introducing a method that introspectively assesses alignment quality, and by applying it to an inherently robust sensor modality. We show that our filtering technique combined with CorAl can be applied to the problem of alignment classification, and that it detects small alignment errors in urban settings with up to 98% accuracy, and with up to 96% if trained only in a different environment. Our lidar and radar experiments demonstrate that CorAl outperforms previous methods both on the ETH lidar benchmark, which includes several indoor and outdoor environments, and the large-scale Oxford and MulRan radar data sets for urban traffic scenarios. The results also demonstrate that CorAl generalizes very well across substantially different environments without the need of retraining. }, year = {2022} } @article{Fan1655127, author = {Fan, Han and Schaffernicht, Erik and Lilienthal, Achim}, institution = {Örebro University, School of Science and Technology}, institution = {Mobile Robotics & Olfaction Lab}, institution = {Mobile Robotics & Olfaction Lab}, institution = {Mobile Robotics & Olfaction Lab}, journal = {Frontiers in Chemistry}, eid = {863838}, title = {Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications}, volume = {10}, DOI = {10.3389/fchem.2022.863838}, keywords = {electronic nose, metal oxide semiconductor sensor, gas detection, gas sensing, open sampling systems, ensemble learning, robotic olfaction}, abstract = {Detecting chemical compounds using electronic noses is important in many gas sensing related applications. A gas detection system is supposed to indicate a significant event, such as the presence of new chemical compounds or a noteworthy change of concentration levels. Existing gas detection methods typically rely on prior knowledge of target analytes to prepare a dedicated, supervised learning model. However, in some scenarios, such as emergency response, not all the analytes of concern are a priori known and their presence are unlikely to be controlled. In this paper, we take a step towards addressing this issue by proposing an ensemble learning-based approach (ELBA) that integrates several one-class classifiers and learns online. The proposed approach is initialized by training several one-class models using clean air only. During the sampling process, the initialized system detects the presence of chemicals, allowing to learn another one-class model and update existing models with self-labelled data. We validated the proposed approach with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an uncontrolled environment. We demonstrated that the ELBA algorithm not only can detect gas exposures but also recognize baseline responses under a suspect short-term sensor drift condition. Depending on the problem setups in practical applications, the present work can be easily hybridized to integrate other supervised learning models when the prior knowledge of target analytes is partially available. }, year = {2022} } @inproceedings{Wiedemann1698762, author = {Wiedemann, Thomas and Schaab, Marius and Gomez, Juan Marchal and Shutin, Dmitriy and Scheibe, Monika and Lilienthal, Achim J.}, booktitle = {2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) : }, institution = {Örebro University, School of Science and Technology}, institution = {Institute of Communications and Navigation, German Aerospace Center (DLR) Oberpfaffenhofen, Germany}, institution = {Institute of Communications and Navigation, German Aerospace Center (DLR) Oberpfaffenhofen, Germany}, institution = {Institute of Communications and Navigation, German Aerospace Center (DLR) Oberpfaffenhofen, Germany}, institution = {Institute of Communications and Navigation, German Aerospace Center (DLR) Oberpfaffenhofen, Germany}, institution = {Institute of Atmospheric Physics, German Aerospace Center (DLR), Oberpfaffenhofen, Germany}, publisher = {IEEE}, title = {Gas Source Localization Based on Binary Sensing with a UAV}, DOI = {10.1109/ISOEN54820.2022.9789553}, keywords = {gas source localization, unmanned aerial vehicle, gas dispersion model, airborne sensing}, abstract = {Precise gas concentration measurements are often difficult, especially by in-situ sensors mounted on an Unmanned Aerial Vehicle (UAV). Simple gas detection, on the other hand, is more robust and reliable, yet brings significantly less information for gas source localization. In this paper, we compensate for the lack of information by a physical model of gas propagation based on the advection-diffusion Partial Differential Equation (PDE). By linking binary gas detection measurements to computed gas concentration using the physical model and an appropriately designed likelihood function, it becomes possible to identify the most likely gas source distribution. The approach was validated in two experiments with ethanol and smoke as "toy" gasses. It is shown that the method is able to successfully localize the source locations in experiments based on gas detection measurements taken by a UAV. }, ISBN = {9781665458603}, ISBN = {9781665458610}, year = {2022} } @inproceedings{Winkler1720220, author = {Winkler, Nicolas P. and Neumann, Patrick P. and Schaffernicht, Erik and Lilienthal, Achim and Poikkim{\"a}ki, Mikko and Kangas, Anneli and S{\"a}{\"a}m{\"a}nen, Arto}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Finnish Institute of Occupational Health, Työterveyslaitos, Finland}, institution = {Finnish Institute of Occupational Health, Työterveyslaitos, Finland}, institution = {Finnish Institute of Occupational Health, Työterveyslaitos, Finland}, title = {Gather Dust and Get Dusted : Long-Term Drift and Cleaning of Sharp GP2Y1010AU0F Dust Sensor in a Steel Factory}, keywords = {Dust sensor, Low-cost, Sensor drift, Sensor network}, abstract = {The Sharp GP2Y1010AU0F is a widely used low-cost dust sensor, but despite its popularity, the manufacturer provides little information on the sensor. We installed 16 sensing nodes with Sharp dust sensors in a hot rolling mill of a steel factory. Our analysis shows a clear correlation between sensor drift and accumulated production of the steel factory. An eye should be kept on the long-term drift of the sensors to prevent early saturation. Two of 16 sensors experienced full saturation, each after around eight and ten months of operation. }, year = {2022} } @inproceedings{Winkler1720225, author = {Winkler, Nicolas P. and Kotlyar, Oleksandr and Schaffernicht, Erik and Fan, Han and Matsukura, Haruka and Ishida, Hiroshi and Neumann, Patrick P. and Lilienthal, Achim}, booktitle = {ROBOT2022 : Fifth Iberian Robotics Conference: Advances in Robotics, Volume 2}, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, Japan}, institution = {Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei, Tokyo, Japan}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, note = {Funding agency:Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science 22H04952}, pages = {178--188}, title = {Learning From the Past : Sequential Deep Learning for Gas Distribution Mapping}, series = {Lecture Notes in Networks and Systems}, number = {590}, volume = {590}, DOI = {10.1007/978-3-031-21062-4_15}, keywords = {Convolutional LSTM, Gas Distribution Mapping, Sequential Learning, Spatial Interpolation}, abstract = {To better understand the dynamics in hazardous environments, gas distribution mapping aims to map the gas concentration levels of a specified area precisely. Sampling is typically carried out in a spatially sparse manner, either with a mobile robot or a sensor network and concentration values between known data points have to be interpolated. In this paper, we investigate sequential deep learning models that are able to map the gas distribution based on a multiple time step input from a sensor network. We propose a novel hybrid convolutional LSTM - transpose convolutional structure that we train with synthetic gas distribution data. Our results show that learning the spatial and temporal correlation of gas plume patterns outperforms a non-sequential neural network model. }, ISBN = {9783031210617}, ISBN = {9783031210624}, year = {2022} } @article{Schindler1694158, author = {Schindler, Maike and Doderer, Jan H. and Simon, Anna L. and Schaffernicht, Erik and Lilienthal, Achim J. and Sch{\"a}fer, Karolin}, institution = {Örebro University, School of Science and Technology}, institution = {Department of Special Education and Rehabilitation, Faculty of Human Sciences, University of Cologne, Cologne, Germany}, institution = {Department of Special Education and Rehabilitation, Faculty of Human Sciences, University of Cologne, Cologne, Germany}, institution = {Department of Special Education and Rehabilitation, Faculty of Human Sciences, University of Cologne, Cologne, Germany}, institution = {Department of Special Education and Rehabilitation, Faculty of Human Sciences, University of Cologne, Cologne, Germany}, journal = {Frontiers in Psychology}, eid = {909775}, title = {Small number enumeration processes of deaf or hard-of-hearing students : A study using eye tracking and artificial intelligence}, volume = {13}, DOI = {10.3389/fpsyg.2022.909775}, keywords = {Artificial Intelligence, deaf or hard-of-hearing students, eye tracking, mathematical difficulties, mathematics education, small number enumeration}, abstract = {Students who are deaf or hard-of-hearing (DHH) often show significant difficulties in learning mathematics. Previous studies have reported that students who are DHH lag several years behind in their mathematical development compared to hearing students. As possible reasons, limited learning opportunities due to a lesser incidental exposure to numerical ideas, delays in language and speech development, and further idiosyncratic difficulties of students who are DHH are discussed; however, early mathematical skills and their role in mathematical difficulties of students who are DHH are not explored sufficiently. In this study, we investigate whether students who are DHH differ from hearing students in their ability to enumerate small sets (1-9)-an ability that is associated with mathematical difficulties and their emergence. Based on a study with N = 63 who are DHH and N = 164 hearing students from third to fifth grade attempting 36 tasks, we used eye tracking, the recording of students' eye movements, to qualitatively investigate student enumeration processes. To reduce the effort of qualitative analysis of around 8,000 student enumeration processes (227 students x 36 tasks), we used Artificial Intelligence, in particular, a clustering algorithm, to identify student enumeration processes from the heatmaps of student gaze distributions. Based on the clustering, we found that gaze distributions of students who are DHH and students with normal hearing differed significantly on a group level, indicating differences in enumeration processes, with students who are DHH using advantageous processes (e.g., enumeration "at a glance") more often than hearing students. The results indicate that students who are DHH do not lag behind in small number enumeration as compared to hearing students but, rather, appear to perform better than their hearing peers in small number enumeration processes, as well as when conceptual knowledge about the part-whole relationship is involved. Our study suggests that the mathematical difficulties of students who are DHH are not related to difficulties in the small number enumeration, which offers interesting perspectives for further research. }, year = {2022} } @article{Schindler1633896, author = {Schindler, Maike and Lilienthal, Achim}, institution = {Örebro University, School of Science and Technology}, institution = {University of Cologne, Cologne, Germany}, journal = {ZDM - the International Journal on Mathematics Education}, note = {Funding agency:Projekt DEAL}, number = {1}, pages = {163--178}, title = {Students’ collaborative creative process and its phases in mathematics : an explorative study using dual eye tracking and stimulated recall interviews}, volume = {54}, DOI = {10.1007/s11858-022-01327-9}, keywords = {Mathematics Education, Creativity, Collaboration, Eye Tracking, Stimulated Recall Interviews}, abstract = {In the age of artificial intelligence where standard problems are increasingly processed by computers, creative problem solving, the ability to think outside the box is in high demand. Collaboration is also increasingly significant, which makes creative collaboration an important twenty-first-century skill. In the research described in this paper, we investigated students’ collaborative creative process in mathematics and explored the collaborative creative process in its phases. Since little is known about the collaborative creative process, we conducted an explorative case study, where two students jointly worked on a multiple solution task. For in-depth insight into the dyad’s collaborative creative process, we used a novel research design in mathematics education, DUET SRI: both students wore eye-tracking glasses during their collaborative work for dual eye-tracking (DUET) and they each participated in a subsequent stimulated recall interview (SRI) where eye-tracking videos from their joint work served as stimulus. Using an inductive data analysis method, we then identified the phases of the students’ collaborative creative process. We found that the collaborative creative process and its phases had similarities to those previously found for solo creative work, yet the process was more complex and volatile and involved different branches. Based on our findings, we present a tentative model of the dyad’s collaborative process in its phases, which can help researchers and educators trace and foster the collaborative creative process more effectively. }, year = {2022} } @inproceedings{Winkler1698792, author = {Winkler, Nicolas P. and Matsukura, Haruka and Neumann, Patrick P. and Schaffernicht, Erik and Ishida, Hiroshi and Lilienthal, Achim J.}, booktitle = {2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {University of Electro-Communications, Tokyo, Japan}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Tokyo University of Agriculture and Technology, Tokyo, Japan}, note = {Funding agency:Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) Japan Society for the Promotion of Science 19H02103}, publisher = {IEEE}, title = {Super-Resolution for Gas Distribution Mapping : Convolutional Encoder-Decoder Network}, DOI = {10.1109/ISOEN54820.2022.9789555}, keywords = {gas distribution mapping, spatial interpolation, deep learning, super-resolution, sensor network}, abstract = {Gas distribution mapping is important to have an accurate understanding of gas concentration levels in hazardous environments. A major problem is that in-situ gas sensors are only able to measure concentrations at their specific location. The gas distribution in-between the sampling locations must therefore be modeled. In this research, we interpret the task of spatial interpolation between sparsely distributed sensors as a task of enhancing an image's resolution, namely super-resolution. Because autoencoders are proven to perform well for this super-resolution task, we trained a convolutional encoder-decoder neural network to map the gas distribution over a spatially sparse sensor network. Due to the difficulty to collect real-world gas distribution data and missing ground truth, we used synthetic data generated with a gas distribution simulator for training and evaluation of the model. Our results show that the neural network was able to learn the behavior of gas plumes and outperforms simpler interpolation techniques. }, ISBN = {9781665458603}, ISBN = {9781665458610}, year = {2022} } @inproceedings{Rudenko1718542, author = {Rudenko, Andrey and Palmieri, Luigi and Huang, Wanting and Lilienthal, Achim J. and Arras, Kai O.}, booktitle = {2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) : }, institution = {Örebro University, School of Science and Technology}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany; Mobile Robotics and Olfaction Lab, Örebro University, Örebro, Sweden}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany; TU München, Germany}, institution = {Mobile Robotics and Olfaction Lab}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany}, pages = {636--643}, publisher = {IEEE}, title = {The Atlas Benchmark : an Automated Evaluation Framework for Human Motion Prediction}, series = {IEEE RO-MAN proceedings}, DOI = {10.1109/RO-MAN53752.2022.9900656}, abstract = {Human motion trajectory prediction, an essential task for autonomous systems in many domains, has been on the rise in recent years. With a multitude of new methods proposed by different communities, the lack of standardized benchmarks and objective comparisons is increasingly becoming a major limitation to assess progress and guide further research. Existing benchmarks are limited in their scope and flexibility to conduct relevant experiments and to account for contextual cues of agents and environments. In this paper we present Atlas, a benchmark to systematically evaluate human motion trajectory prediction algorithms in a unified framework. Atlas offers data preprocessing functions, hyperparameter optimization, comes with popular datasets and has the flexibility to setup and conduct underexplored yet relevant experiments to analyze a method's accuracy and robustness. In an example application of Atlas, we compare five popular model- and learning-based predictors and find that, when properly applied, early physics-based approaches are still remarkably competitive. Such results confirm the necessity of benchmarks like Atlas. }, ISBN = {9781728188591}, ISBN = {9781665406802}, year = {2022} } @inproceedings{Schreiter1720267, author = {Schreiter, Tim and Morillo-Mendez, Lucas and Chadalavada, Ravi Teja and Rudenko, Andrey and Billing, Erik Alexander and Lilienthal, Achim J.}, booktitle = {SCRITA Workshop Proceedings (arXiv:2208.11090) : }, institution = {Örebro University, School of Science and Technology}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany}, institution = {Interaction Lab, University of Skövde, Sweden}, title = {The Effect of Anthropomorphism on Trust in an Industrial Human-Robot Interaction}, DOI = {10.48550/arXiv.2208.14637}, abstract = {Robots are increasingly deployed in spaces shared with humans, including home settings and industrial environments. In these environments, the interaction between humans and robots (HRI) is crucial for safety, legibility, and efficiency. A key factor in HRI is trust, which modulates the acceptance of the system. Anthropomorphism has been shown to modulate trust development in a robot, but robots in industrial environments are not usually anthropomorphic. We designed a simple interaction in an industrial environment in which an anthropomorphic mock driver (ARMoD) robot simulates to drive an autonomous guided vehicle (AGV). The task consisted of a human crossing paths with the AGV, with or without the ARMoD mounted on the top, in a narrow corridor. The human and the system needed to negotiate trajectories when crossing paths, meaning that the human had to attend to the trajectory of the robot to avoid a collision with it. There was a significant increment in the reported trust scores in the condition where the ARMoD was present, showing that the presence of an anthropomorphic robot is enough to modulate the trust, even in limited interactions as the one we present here.  }, year = {2022} } @inproceedings{Schreiter1720261, author = {Schreiter, Tim and Almeida, Tiago Rodrigues de and Zhu, Yufei and Guti{\’e;}rrez Maestro, Eduardo and Morillo-Mendez, Lucas and Rudenko, Andrey and Kucner, Tomasz P. and Martinez Mozos, Oscar and Magnusson, Martin and Palmieri, Luigi and Arras, Kai O. and Lilienthal, Achim}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany }, institution = {Mobile Robotics Group, Department of Electrical Engineering and Automation, Aalto University, Finland}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany }, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany }, title = {The Magni Human Motion Dataset : Accurate, Complex, Multi-Modal, Natural, Semantically-Rich and Contextualized}, DOI = {10.48550/arXiv.2208.14925}, keywords = {Dataset, Human Motion Prediction, Eye Tracking}, abstract = {Rapid development of social robots stimulates active research in human motion modeling, interpretation and prediction, proactive collision avoidance, human-robot interaction and co-habitation in shared spaces. Modern approaches to this end require high quality datasets for training and evaluation. However, the majority of available datasets suffers from either inaccurate tracking data or unnatural, scripted behavior of the tracked people. This paper attempts to fill this gap by providing high quality tracking information from motion capture, eye-gaze trackers and on-board robot sensors in a semantically-rich environment. To induce natural behavior of the recorded participants, we utilise loosely scripted task assignment, which induces the participants navigate through the dynamic laboratory environment in a natural and purposeful way. The motion dataset, presented in this paper, sets a high quality standard, as the realistic and accurate data is enhanced with semantic information, enabling development of new algorithms which rely not only on the tracking information but also on contextual cues of the moving agents, static and dynamic environment.  }, year = {2022} } @inproceedings{Fan1696626, author = {Fan, Han and Jonsson, Daniel and Schaffernicht, Erik and Lilienthal, Achim}, booktitle = {2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) : Proceedings}, institution = {Örebro University, School of Science and Technology}, institution = {Örebro University, Örebro, Sweden}, title = {Towards Gas Identification in Unknown Mixtures Using an Electronic Nose with One-Class Learning}, DOI = {10.1109/ISOEN54820.2022.9789607}, keywords = {gas identification, gas mixture, unknown interferent, one-class learning, electronic nose}, abstract = {Gas identification using an electronic nose (e-nose) typically relies on a multi-class classifier trained with extensive data of a limited set of target analytes. Usually, classification performance degrades in the presence of mixtures that include interferents not represented in the training data. This issue limits the applicability of e-noses in real-world scenarios where interferents are a priori unknown. This paper investigates the feasibility of tackling this particular gas identification problem using one-class learning. We propose several training strategies for a one-class support vector machine to deal with gas mixtures composed of a target analyte and an interferent at different concentration levels. Our evaluation indicates that accurate identification of the presence of a target analyte is achievable if it is dominant in a mixture. For interferent-dominant mixtures, extensive training is required, which implies that an improvement in the generalization ability of the one-class model is needed. }, ISBN = {9781665458610}, ISBN = {9781665458603}, year = {2022} } @article{Montazeri1620582, author = {Montazeri, Amir and Lilienthal, Achim and Albertson, John D.}, institution = {Örebro University, School of Science and Technology}, institution = {Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca NY, USA }, institution = {School of Civil and Environmental Engineering, Cornell University, Ithaca NY, USA }, journal = {Atmospheric Environment: X}, note = {Funding agencies:David R. Atkinson Center for a Sustainable Future (ACSF) at Cornell UniversityUnited States Department of Energy (DOE) DE-AR0000749}, eid = {100126}, title = {A spatial land use clustering framework for investigating the role of land use in mediating the effect of meteorology on urban air quality}, volume = {12}, DOI = {10.1016/j.aeaoa.2021.100126}, keywords = {Air pollution profiles, Cluster analysis, Mobile monitoring, Land use effects, K-means, Exceedance probabilities, Unsupervised learning, Machine learning}, abstract = {Air pollution in urban areas is driven by emission sources and modulated by local meteorology, including the effects of urban form on wind speed and ventilation, and thus varies markedly in space and time. Recently, mobile measurement campaigns have been conducted in urban areas to measure the spatial distribution of air pollutant concentrations. While the main focus of these studies has been revealing spatial patterns in mean (or median) concentrations, they have mostly ignored the temporal aspects of air pollution. However, assessing the temporal variability of air pollution is essential in understanding the integrated exposure of individuals to pollutants above critical thresholds. Here, we examine the role of urban land use in mediating the effect of regional meteorology on Nitrogen Dioxide (NO2) concentrations measured in different regions of Oakland, CA. Inspired by Land Use Regression (LUR) models, we cluster 30-m road segments in the urban area based on their land use. The concentration data from the resulting clusters are stratified based on seasonality and conditionally averaged based on concurrent wind speeds. The clustering analysis yielded 7 clusters, with 4 of them chosen for further statistical analysis due to their large sample sizes. Two of the four clusters demonstrated in winter a strong negative linear relationship between NO2 concentration and wind speed (R-2 > 0.87) with a slope of approximately 3 ppb/m s(-1). A weaker correlation and flatter slope was found for the cluster representing road segments belonging to interstate highways (R-2 > 0.73 and slope < 2 ppb/m s(-1)). No significant relationship was found during the summer season. These findings are consistent with the concept of strong vertical mixing due to highway traffic and increased surface heat fluxes during summer weakening the relationship between wind speed and NO2 concentrations. In summary, the clustering analysis framework presented here provides a novel tool for use with large-scale mobile measurements to reveal the effect of urban land form on the temporal dynamics of pollutant concentrations and ultimately human exposure. }, year = {2021} } @inproceedings{Machado1633897, author = {Machado, Tyrone and Fassbender, David and Taheri, Abdolreza and Eriksson, Daniel and Gupta, Himanshu and Molaei, Amirmasoud and Forte, Paolo and Rai, Prashant and Ghabcheloo, Reza and M{\"a}kinen, Saku and Lilienthal, Achim and Andreasson, Henrik and Geimer, Marcus}, booktitle = {Proceedings of the IEEE ICTE Leading Digital Transformation in Business and Society Conference : }, institution = {Örebro University, School of Science and Technology}, institution = {Bosch Rexroth AG, Elchingen, Germany}, institution = {Bosch Rexroth AG, Elchingen, Germany}, institution = {Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland}, institution = {R&D Wheel Loader-Emerging Technologies, Liebherr-Werk Bischofshofen GmbH, Bischofshofen, Austria}, institution = {Institute of Vehicle System Technology, Karlsruhe Institute of Technology, Karlsruhe, Germany}, institution = {Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland}, institution = {Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland}, institution = {Faculty of Management and Business, Tampere University, Tampere, Finland}, institution = {Institute of Vehicle System, Technology Karlsruhe Institute of Technology, Karlsruhe, Germany}, title = {Autonomous Heavy-Duty Mobile Machinery : A Multidisciplinary Collaborative Challenge}, DOI = {10.1109/ICTE51655.2021.9584498}, keywords = {automation, augmentation, autonomous, collaboration, mobile machinery, transaction cost economics}, abstract = {Heavy-duty mobile machines (HDMMs), are a wide range of off-road machinery used in diverse and critical application areas which are currently facing several issues like skilled labor shortage, safety requirements, and harsh work environments in general. Consequently, efforts are underway to increase automation in HDMMs for increased productivity and safety, eventually transitioning to operator-less autonomous HDMMs to address skilled labor shortages. However, HDMM are complex machines requiring continuous physical and cognitive inputs from human operators. Thus, developing autonomous HDMM is a huge challenge, with current research and developments being performed in several independent research domains. Through this study, we use the bounded rationality concept to propose multidisciplinary collaborations for new autonomous HDMMs and apply the transaction cost economics framework to suggest future implications in the HDMM industry. Furthermore, we introduce and provide a conceptual understanding of the autonomous HDMM industry collaborations as a unified approach, while highlighting the practical implications and challenges of the complex nature of such multidisciplinary collaborations. The collaborative challenges and potentials are mapped out between the following topics: mechanical systems, AI methods, software systems, sensors, data and connectivity, simulations and process optimization, business cases, organization theories, and finally, regulatory frameworks. }, ISBN = {9781665438957}, ISBN = {9781665445986}, year = {2021} } @inproceedings{Alhashimi1803369, author = {Alhashimi, Anas and Adolfsson, Daniel and Magnusson, Martin and Andreasson, Henrik and Lilienthal, Achim}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {School of Science and Technology, Örebro University, Örebro, Sweden; Computer Engineering Department, University of Baghdad, Baghdad, Iraq}, title = {BFAR – Bounded False Alarm Rate detector for improved radar odometry estimation}, abstract = {This paper presents a new detector for filtering noise from true detections in radar data, which improves the state of the art in radar odometry. Scanning Frequency-Modulated Continuous Wave (FMCW) radars can be useful for localisation and mapping in low visibility, but return a lot of noise compared to (more commonly used) lidar, which makes the detection task more challenging. Our Bounded False-Alarm Rate (BFAR) detector is different from the classical Constant False-Alarm Rate (CFAR) detector in that it applies an affine transformation on the estimated noise level after which the parameters that minimize the estimation error can be learned. BFAR is an optimized combination between CFAR and fixed-level thresholding. Only a single parameter needs to be learned from a training dataset. We apply BFAR tothe use case of radar odometry, and adapt a state-of-the-art odometry pipeline (CFEAR), replacing its original conservative filtering with BFAR. In this way we reduce the state-of-the-art translation/rotation odometry errors from 1.76%/0.5◦/100 m to 1.55%/0.46◦/100 m; an improvement of 12.5%. }, URL = {https://doi.org/10.48550/arXiv.2109.09669}, year = {2021} } @inproceedings{Adolfsson1595903, author = {Adolfsson, Daniel and Magnusson, Martin and Alhashimi, Anas and Lilienthal, Achim and Andreasson, Henrik}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021) : }, institution = {Örebro University, School of Science and Technology}, pages = {5462--5469}, title = {CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry}, series = {IEEE International Conference on Intelligent Robots and Systems. Proceedings}, DOI = {10.1109/IROS51168.2021.9636253}, keywords = {Localization SLAM Mapping Radar}, abstract = {This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for large-scale radar odometry estimation. By using a filtering technique that keeps the k strongest returns per azimuth and by additionally filtering the radar data in Cartesian space, we are able to compute a sparse set of oriented surface points for efficient and accurate scan matching. Registration is carried out by minimizing a point-to-line metric and robustness to outliers is achieved using a Huber loss. We were able to additionally reduce drift by jointly registering the latest scan to a history of keyframes and found that our odometry method generalizes to different sensor models and datasets without changing a single parameter. We evaluate our method in three widely different environments and demonstrate an improvement over spatially cross-validated state-of-the-art with an overall translation error of 1.76% in a public urban radar odometry benchmark, running at 55Hz merely on a single laptop CPU thread. }, URL = {https://doi.org/10.48550/arXiv.2105.01457}, ISBN = {9781665417143}, ISBN = {9781665417150}, year = {2021} } @inproceedings{Adolfsson1596301, author = {Adolfsson, Daniel and Magnusson, Martin and Liao, Qianfang and Lilienthal, Achim and Andreasson, Henrik}, booktitle = {10th European Conference on Mobile Robots (ECMR 2021) : }, institution = {Örebro University, School of Science and Technology}, title = {CorAl – Are the point clouds Correctly Aligned?}, volume = {10}, DOI = {10.1109/ECMR50962.2021.9568846}, abstract = {In robotics perception, numerous tasks rely on point cloud registration. However, currently there is no method that can automatically detect misaligned point clouds reliably and without environment-specific parameters. We propose "CorAl", an alignment quality measure and alignment classifier for point cloud pairs, which facilitates the ability to introspectively assess the performance of registration. CorAl compares the joint and the separate entropy of the two point clouds. The separate entropy provides a measure of the entropy that can be expected to be inherent to the environment. The joint entropy should therefore not be substantially higher if the point clouds are properly aligned. Computing the expected entropy makes the method sensitive also to small alignment errors, which are particularly hard to detect, and applicable in a range of different environments. We found that CorAl is able to detect small alignment errors in previously unseen environments with an accuracy of 95% and achieve a substantial improvement to previous methods. }, URL = {https://doi.org/10.48550/arXiv.2109.09820}, year = {2021} } @inproceedings{Winkler1631751, author = {Winkler, Nicolas P. and Neumann, Patrick P. and Kohlhoff, Harald and Erdmann, Jessica and Schaffernicht, Erik and Lilienthal, Achim}, booktitle = {SMSI 2021 Proceedings : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany}, pages = {260--261}, title = {Development of a Low-Cost Sensing Node with Active Ventilation Fan for Air Pollution Monitoring}, DOI = {10.5162/SMSI2021/D3.5}, keywords = {wireless sensing node, environmental monitoring, air pollution, sensor network}, abstract = {A fully designed low-cost sensing node for air pollution monitoring and calibration results for several low-cost gas sensors are presented. As the state of the art is lacking information on the importance of an active ventilation system, the effect of an active fan is compared to the passive ventilation of a lamellar structured casing. Measurements obtained in an urban outdoor environment show that readings of the low-cost dust sensor (Sharp GP2Y1010AU0F) are distorted by the active ventilation system. While this behavior requires further research, a correlation with temperature and humidity inside the node shown. }, year = {2021} } @article{Cheng1582071, author = {Cheng, Lu and Meng, Qing-Hao and Lilienthal, Achim and Qi, Pei-Feng}, institution = {Örebro University, School of Science and Technology}, institution = {School of Electrical and Information Engineering, Tianjin University, Tianjin, People's Republic of China }, institution = {School of Electrical and Information Engineering, Tianjin University, Tianjin, People's Republic of China }, institution = {National Computer Network Emergency Response Technical Team/Coordination Center of China (CNCERT/CC), Beijing, People's Republic of China }, journal = {Measurement science and technology}, note = {Funding Agencies:National Key R{\&}amp;D Program of China 2017YFC0306200Natural Science Foundation of Tianjin20JCZDJC00150 20JCYBJC00320}, number = {6}, eid = {062002}, publisher = {IOP Publishing}, title = {Development of compact electronic noses : a review}, volume = {32}, DOI = {10.1088/1361-6501/abef3b}, keywords = {compact electronic nose (e-nose), gas sensor array, hardware circuit, gas path and sampling, on-chip calculation, wearable e-nose, mobile e-nose}, abstract = {An electronic nose (e-nose) is a measuring instrument that mimics human olfaction and outputs 'fingerprint' information of mixed gases or odors. Generally speaking, an e-nose is mainly composed of two parts: a gas sensing system (gas sensor arrays, gas transmission paths) and an information processing system (microprocessor and related hardware, pattern recognition algorithms). It has been more than 30 years since the e-nose concept was introduced in the 1980s. Since then, e-noses have evolved from being large in size, expensive, and power-hungry instruments to portable, low cost devices with low power consumption. This paper reviews the development of compact e-nose design and calculation over the last few decades, and discusses possible future trends. Regarding the compact e-nose design, which is related to its size and weight, this paper mainly summarizes the development of sensor array design, hardware circuit design, gas path (i.e. the path through which the mixed gases to be measured flow inside the e-nose system) and sampling design, as well as portable design. For the compact e-nose calculation, which is directly related to its rapidity of detection, this review focuses on the development of on-chip calculation and wireless computing. The future trends of compact e-noses include the integration with the internet of things, wearable e-noses, and mobile e-nose systems. }, year = {2021} } @inproceedings{Wiedemann1633892, author = {Wiedemann, Thomas and Shutin, Dmitriy and Lilienthal, Achim}, booktitle = {2021 IEEE International Conference on Autonomous Systems (ICAS) : }, institution = {Örebro University, School of Science and Technology}, institution = {German Aerospace Center, Institute of Communications and Navigation, Oberpfaffenhofen, Germany}, institution = {German Aerospace Center, Institute of Communications and Navigation, Oberpfaffenhofen, Germany}, title = {Experimental Validation of Domain Knowledge Assisted Robotic Exploration and Source Localization}, DOI = {10.1109/ICAS49788.2021.9551145}, keywords = {mobile robot olfaction, gas source localization, Bayesian inference, swarm exploration}, abstract = {In situations where toxic or dangerous airborne material is leaking, mobile robots equipped with gas sensors are a safe alternative to human reconnaissance. This work presents the Domain Knowledge Assisted Robotic Exploration and Source Localization (DARES) approach. It allows a multi-robot system to localize multiple sources or leaks autonomously and independently of a human operator. The probabilistic approach builds upon domain knowledge in the form of a physical model of gas dispersion and the a priori assumption that the dispersion process is driven by multiple but sparsely distributed sources. A formal criterion is used to guide the robots to informative measurement locations and enables inference of the source distribution based on gas concentration measurements. Small-scale indoor experiments under controlled conditions are presented to validate the approach. In all three experiments, three rovers successfully localized two ethanol sources. }, ISBN = {9781728172897}, ISBN = {9781728172903}, year = {2021} } @incollection{Palm1633895, author = {Palm, Rainer and Lilienthal, Achim}, booktitle = {Computational Intelligence : 11th International Joint Conference, IJCCI 2019, Vienna, Austria, September 17–19, 2019, Revised Selected Papers}, institution = {Örebro University, School of Science and Technology}, pages = {191--221}, title = {Fuzzy Geometric Approach to Collision Estimation Under Gaussian Noise in Human-Robot Interaction}, series = {Studies in Computational Intelligence}, number = {922}, DOI = {10.1007/978-3-030-70594-7_8}, keywords = {Human-robot systems, Navigation, Gaussian noise, Kalman filters, Fuzzy modeling}, abstract = {Humans and mobile robots while sharing the same work areas require a high level of safety especially at possible intersections of trajectories. An issue of the human-robot navigation is the computation of the intersection point in the presence of noisy measurements or fuzzy information. For Gaussian distributions of positions/orientations (inputs) of robot and human agent and their parameters the corresponding parameters at the intersections (outputs) are computed by analytical and fuzzy methods.This is done both for the static and the dynamic case using Kalman filters for robot/human positions and orientations and thus for the estimation of the intersection positions. For the overdetermined case (6 inputs, 2 outputs) a so-called ’energetic’ approach is used for the estimation of the point of intersection. The inverse task is discussed, specifying the parameters of the output distributions and looking for the parameters of the input distributions. For larger standard deviations (stds) mixed Gaussian models are suggested as approximation of non-Gaussian distributions. }, ISBN = {9783030705930}, ISBN = {9783030705947}, ISBN = {9783030705961}, year = {2021} } @article{Palmieri1581852, author = {Palmieri, Luigi and Rudenko, Andrey and Mainprice, Jim and Hanheide, Marc and Alahi, Alexandre and Lilienthal, Achim and Arras, Kai O.}, institution = {Örebro University, School of Science and Technology}, institution = {Robert Bosch GmbH Corp Res, Gerlingen, Germany}, institution = {Robert Bosch GmbH Corp Res, Gerlingen, Germany}, institution = {University of Stuttgart, Stuttgart, Germany}, institution = {University of Lincoln, Lincoln, England}, institution = {Ecole Polytech Fed Lausanne, Lausanne, Switzerland}, institution = {Robert Bosch GmbH Corp Res, Robot Program, Gerlingen, Germany}, journal = {IEEE Robotics and Automation Letters}, number = {3}, pages = {5613--5617}, title = {Guest Editorial : Introduction to the Special Issue on Long-Term Human Motion Prediction}, volume = {6}, DOI = {10.1109/LRA.2021.3077964}, keywords = {Human-robot interaction, Human motion prediction, motion planning}, abstract = {The articles in this special section focus on long term human motion prediction. This represents a key ability for advanced autonomous systems, especially if they operate in densely crowded and highly dynamic environments. In those settings understanding and anticipating human movements is fundamental for robust long-term operation of robotic systems and safe human-robot collaboration. Foreseeing how a scene with multiple agents evolves over time and incorporating predictions in a proactive manner allows for novel ways of planning and control, active perception, or humanrobot interaction. Recent planning and control approaches use predictive techniques to better cope with the dynamics of the environment, thus allowing the generation of smoother and more legible robot motion. Predictions can be provided as input to the planning or optimization algorithm (e.g. as a cost term or heuristic function), or as additional dimension to consider in the problem formulation (leading to an increased computational complexity). Recent perception techniques deeply interconnect prediction modules with detection, segmentation and tracking, to generally increase the accuracy of different inference tasks, i.e. filtering, predicting. As also indicated by some of the scientific works accepted in this special issue, novel deep learning architectures allow better interleaving of the aforementioned units. }, year = {2021} } @article{Rudenko1546418, author = {Rudenko, Andrey and Palmieri, Luigi and Doellinger, Johannes and Lilienthal, Achim and Arras, Kai O.}, institution = {Örebro University, School of Science and Technology}, institution = {Bosch Corporate Research, Renningen, Germany}, institution = {Bosch Corporate Research, Renningen, Germany}, institution = {Bosch Center for Artificial Intelligence, Renningen, Germany}, institution = {Bosch Corporate Research, Renningen, Germany}, journal = {IEEE Robotics and Automation Letters}, note = {Funding Agency:European Commission 732737}, number = {2}, pages = {3248--3255}, title = {Learning Occupancy Priors of Human Motion From Semantic Maps of Urban Environments}, volume = {6}, DOI = {10.1109/LRA.2021.3062010}, keywords = {Deep learning for visual perception, human detection and tracking, human motion analysis, human motion prediction, semantic scene understanding}, abstract = {Understanding and anticipating human activity is an important capability for intelligent systems in mobile robotics, autonomous driving, and video surveillance. While learning from demonstrations with on-site collected trajectory data is a powerful approach to discover recurrent motion patterns, generalization to new environments, where sufficient motion data are not readily available, remains a challenge. In many cases, however, semantic information about the environment is a highly informative cue for the prediction of pedestrian motion or the estimation of collision risks. In this work, we infer occupancy priors of human motion using only semantic environment information as input. To this end, we apply and discuss a traditional Inverse Optimal Control approach, and propose a novel approach based on Convolutional Neural Networks (CNN) to predict future occupancy maps. Our CNN method produces flexible context-aware occupancy estimations for semantically uniform map regions and generalizes well already with small amounts of training data. Evaluated on synthetic and real-world data, it shows superior results compared to several baselines, marking a qualitative step-up in semantic environment assessment. }, year = {2021} } @inproceedings{Adolfsson1803356, author = {Adolfsson, Daniel and Magnusson, Martin and Alhashimi, Anas and Lilienthal, Achim and Andreasson, Henrik}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {School of Science and Technology, Örebro University, Örebro, Sweden}, title = {Oriented surface points for efficient and accurate radar odometry}, abstract = {This paper presents an efficient and accurate radar odometry pipeline for large-scale localization. We propose a radar filter that keeps only the strongest reflections per-azimuth that exceeds the expected noise level. The filtered radar data is used to incrementally estimate odometry by registering the current scan with a nearby keyframe. By modeling local surfaces, we were able to register scans by minimizing a point-to-line metric and accurately estimate odometry from sparse point sets, hence improving efficiency. Specifically, we found that a point-to-line metric yields significant improvements compared to a point-to-point metric when matching sparse sets of surface points. Preliminary results from an urban odometry benchmark show that our odometry pipeline is accurate and efficient compared to existing methods with an overall translation error of 2.05%, down from 2.78% from the previously best published method, running at 12.5ms per frame without need of environmental specific training.  }, URL = {https://doi.org/10.48550/arXiv.2109.09994}, year = {2021} } @inproceedings{Kucner1633891, author = {Kucner, Tomasz Piotr and Luperto, Matteo and Lowry, Stephanie and Magnusson, Martin and Lilienthal, Achim}, booktitle = {2021 IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, institution = {Applied Intelligent System Lab (AISLab), Università degli Studi di Milano, Milano, Italy}, pages = {1715--1721}, title = {Robust Frequency-Based Structure Extraction}, series = {IEEE International Conference on Robotics and Automation (ICRA)}, DOI = {10.1109/ICRA48506.2021.9561381}, keywords = {Mapping, semantic understanding, indoor environments}, abstract = {State of the art mapping algorithms can produce high-quality maps. However, they are still vulnerable to clutter and outliers which can affect map quality and in consequence hinder the performance of a robot, and further map processing for semantic understanding of the environment. This paper presents ROSE, a method for building-level structure detection in robotic maps. ROSE exploits the fact that indoor environments usually contain walls and straight-line elements along a limited set of orientations. Therefore metric maps often have a set of dominant directions. ROSE extracts these directions and uses this information to segment the map into structure and clutter through filtering the map in the frequency domain (an approach substantially underutilised in the mapping applications). Removing the clutter in this way makes wall detection (e.g. using the Hough transform) more robust. Our experiments demonstrate that (1) the application of ROSE for decluttering can substantially improve structural feature retrieval (e.g., walls) in cluttered environments, (2) ROSE can successfully distinguish between clutter and structure in the map even with substantial amount of noise and (3) ROSE can numerically assess the amount of structure in the map. }, ISBN = {9781728190778}, ISBN = {9781728190785}, year = {2021} } @article{Arain1504014, author = {Arain, Muhammad Asif and Hernandez Bennetts, Victor and Schaffernicht, Erik and Lilienthal, Achim}, institution = {Örebro University, School of Science and Technology}, institution = {Mobile Robotics and Olfaction (MRO) Lab, Center for Applied Autonomous Sensor Systems (AASS), School of Science and Technology, Örebro University, Örebro, Sweden}, journal = {The international journal of robotics research}, note = {Funding Agencies:European Commission ICT-23-2014 645101SURVEYOR (Vinnova) 2017-05468project RAISE 20130196}, number = {4-5}, pages = {782--814}, title = {Sniffing out fugitive methane emissions : autonomous remote gas inspection with a mobile robot}, volume = {40}, DOI = {10.1177/0278364920954907}, keywords = {Environmental monitoring, autonomous exploration, remote gas inspection, mobile robot olfaction, fugitivemethane emissions}, abstract = {Air pollution causes millions of premature deaths every year, and fugitive emissions of, e.g., methane are major causes of global warming. Correspondingly, air pollution monitoring systems are urgently needed. Mobile, autonomous monitoring can provide adaptive and higher spatial resolution compared with traditional monitoring stations and allows fast deployment and operation in adverse environments. We present a mobile robot solution for autonomous gas detection and gas distribution mapping using remote gas sensing. Our ‘‘Autonomous Remote Methane Explorer’’ (ARMEx) is equipped with an actuated spectroscopy-based remote gas sensor, which collects integral gas measurements along up to 30 m long optical beams. State-of-the-art 3D mapping and robot localization allow the precise location of the optical beams to be determined, which then facilitates gas tomography (tomographic reconstruction of local gas distributions from sets of integral gas measurements). To autonomously obtain informative sampling strategies for gas tomography, we reduce the search space for gas inspection missions by defining a sweep of the remote gas sensor over a selectable field of view as a sensing configuration. We describe two different ways to find sequences of sensing configurations that optimize the criteria for gas detection and gas distribution mapping while minimizing the number of measurements and distance traveled. We evaluated anARMExprototype deployed in a large, challenging indoor environment with eight gas sources. In comparison with human experts teleoperating the platform from a distant building, the autonomous strategy produced better gas maps with a lower number of sensing configurations and a slightly longer route. }, year = {2021} } @inproceedings{Winkler1631754, author = {Winkler, Nicolas P. and Neumann, Patrick P. and Schaffernicht, Erik and Lilienthal, Achim}, booktitle = {2021 IEEE SENSORS : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, title = {Using Redundancy in a Sensor Network to Compensate Sensor Failures}, keywords = {Environmental monitoring, wireless sensor network, sensor placement, machine learning}, abstract = {Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks. }, year = {2021} } @inproceedings{Winkler1640648, author = {Winkler, Nicolas P. and Neumann, Patrick P. and Schaffernicht, Erik and Lilienthal, Achim J.}, booktitle = {2021 IEEE SENSORS : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, note = {Funding agency:SAFeRA}, publisher = {IEEE}, title = {Using Redundancy in a Sensor Network to Compensate Sensor Failures}, series = {Proceedings of IEEE Sensors}, DOI = {10.1109/SENSORS47087.2021.9639479}, keywords = {environmental monitoring, wireless sensor network, sensor placement, machine learning}, abstract = {Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks. }, ISBN = {9781728195018}, ISBN = {9781728195025}, year = {2021} } @inproceedings{Rudenko1524236, author = {Rudenko, Andrey and Kucner, Tomasz Piotr and Swaminathan, Chittaranjan Srinivas and Chadalavada, Ravi Teja and Arras, Kai Oliver and Lilienthal, Achim}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {Bosch Corporate Research, Renningen, Germany}, title = {Benchmarking Human Motion Prediction Methods}, keywords = {human motion prediction, benchmarking, datasets}, abstract = {In this extended abstract we present a novel dataset for benchmarking motion prediction algorithms. We describe our approach to data collection which generates diverse and accurate human motion in a controlled weakly-scripted setup. We also give insights for building a universal benchmark for motion prediction. }, year = {2020} } @article{Chadalavada1374911, author = {Chadalavada, Ravi Teja and Andreasson, Henrik and Schindler, Maike and Palm, Rainer and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Faculty of Human Sciences, University of Cologne, Germany}, journal = {Robotics and Computer-Integrated Manufacturing}, note = {Funding Agencies:KKS SIDUS project AIR: "Action and Intention Recognition in Human Interaction with Autonomous Systems"  20140220H2020 project ILIAD: "Intra-Logistics with Integrated Automatic Deployment: Safe and Scalable Fleets in Shared Spaces"  732737}, eid = {101830}, title = {Bi-directional navigation intent communication using spatial augmented reality and eye-tracking glasses for improved safety in human-robot interaction}, volume = {61}, DOI = {10.1016/j.rcim.2019.101830}, keywords = {Human-robot interaction (HRI), Mobile robots, Intention communication, Eye-tracking, Intention recognition, Spatial augmented reality, Stimulated recall interview, Obstacle avoidance, Safety, Logistics}, abstract = {Safety, legibility and efficiency are essential for autonomous mobile robots that interact with humans. A key factor in this respect is bi-directional communication of navigation intent, which we focus on in this article with a particular view on industrial logistic applications. In the direction robot-to-human, we study how a robot can communicate its navigation intent using Spatial Augmented Reality (SAR) such that humans can intuitively understand the robot's intention and feel safe in the vicinity of robots. We conducted experiments with an autonomous forklift that projects various patterns on the shared floor space to convey its navigation intentions. We analyzed trajectories and eye gaze patterns of humans while interacting with an autonomous forklift and carried out stimulated recall interviews (SRI) in order to identify desirable features for projection of robot intentions. In the direction human-to-robot, we argue that robots in human co-habited environments need human-aware task and motion planning to support safety and efficiency, ideally responding to people's motion intentions as soon as they can be inferred from human cues. Eye gaze can convey information about intentions beyond what can be inferred from the trajectory and head pose of a person. Hence, we propose eye-tracking glasses as safety equipment in industrial environments shared by humans and robots. In this work, we investigate the possibility of human-to-robot implicit intention transference solely from eye gaze data and evaluate how the observed eye gaze patterns of the participants relate to their navigation decisions. We again analyzed trajectories and eye gaze patterns of humans while interacting with an autonomous forklift for clues that could reveal direction intent. Our analysis shows that people primarily gazed on that side of the robot they ultimately decided to pass by. We discuss implications of these results and relate to a control approach that uses human gaze for early obstacle avoidance. }, year = {2020} } @incollection{Kucner1430083, author = {Kucner, Tomasz Piotr and Lilienthal, Achim and Magnusson, Martin and Palmieri, L. and Swaminathan, Chittaranjan Srinivas}, booktitle = {Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots : }, institution = {Örebro University, School of Science and Technology}, institution = {Corporate Research, Robert Bosch GmbH, Renningen, Germany}, pages = {143--151}, title = {Closing Remarks}, series = {Cognitive Systems Monographs}, number = {40}, DOI = {10.1007/978-3-030-41808-3_6}, abstract = {Dynamics is an inherent feature of reality. In spite of that, the domain of maps of dynamics has not received a lot of attention yet. In this book, we present solutions for building maps of dynamics and outline how to make use of them for motion planning. In this chapter, we present discuss related research question that as of yet remain to be answered, and derive possible future research directions.  }, ISBN = {978-3-030-41807-6}, ISBN = {978-3-030-41808-3}, year = {2020} } @inproceedings{Lindner1458344, author = {Lindner, Helen and Hill, Wendy and Norling Hermansson, Liselotte and Lilienthal, Achim J.}, booktitle = {MEC20 Symposium Proceedings : }, institution = {Örebro University, School of Health Sciences}, institution = {Örebro University, School of Science and Technology}, institution = {Institute of Biomedical engineering, UNB, Fredericton, Canda}, institution = {University Health Care Research Centre, Faculty of Medicine and Health, Örebro University, Örebro, Sweden; Dept. of Prosthetics and Orthotics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden}, publisher = {University of New Brunswick}, title = {Cognitive load in learning to use a multi-function hand}, keywords = {eye tracking, cognitive load, multi-function prosthetic hand}, abstract = {Despite the promising functions of a multi-function hand, it is challenging to learn to use a hand that has up to 36grip patterns. If it requires too much cognitive load to learn to operate a prosthetic hand, the user may eventually stopusing it. Measurement of cognitive load while learning to use a bionic hand will help the therapist to adjust the trainingpace and help the user to achieve success.An innovative, non-obtrusive method for measuring cognitive load is by tracking eye gaze. Gaze measuresprovide pupil diameters that indicate subjective task difficulty and mental effort. Three subjects wore a pair of Tobiieye-tracking glasses during control training and performed eight activities. Eye-tracking data were imported in TobiiPro Lab software for extracting pupil diameter during the activities. Pupil diameter (normal range: 2-4mm duringnormal light) was used to indicate the amount of cognitive load.Pupil diameters were below 4mm in 9 out of 23 training activities. Pupil diameters were above 4mm in all threesubjects when they used precision pinch to perform the activities “stack 4 1-inch wooden blocks” and “pick up smallobjects”. Subject 3 had pupil diameters over 4mm in all training activities. Pupil diameters were largest when thesubjects were adjusting the grip and when they had difficulties in initiating the grip.It seems appropriate to introduce no more than four grips during the first control training session. Further studyis required to determine if pupil diameters will decrease over time when adequate prosthetic training is given. }, URL = {https://conferences.lib.unb.ca/index.php/mec/article/view/65}, year = {2020} } @article{Burgues1380687, author = {Burgues, Javier and Hernandez Bennetts, Victor and Lilienthal, Achim J. and Marco, Santiago}, institution = {Örebro University, School of Science and Technology}, institution = {Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain; Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Barcelona, Spain}, institution = {Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain; Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Barcelona, Spain}, journal = {Sensors and actuators. B, Chemical}, note = {Funding Agencies:Spanish MINECO program  BES-2015-071698 TEC2014-59229-RH2020-ICT by the European Commission  645101}, eid = {127309}, title = {Gas Distribution Mapping and Source Localization Using a 3D Grid of Metal Oxide Semiconductor Sensors}, volume = {304}, DOI = {10.1016/j.snb.2019.127309}, keywords = {Mobile robotic olfaction, Metal oxide gas sensors, Signal processing, Sensor networks, Gas source localization, Gas distribution mapping}, abstract = {The difficulty to obtain ground truth (i.e. empirical evidence) about how a gas disperses in an environment is one of the major hurdles in the field of mobile robotic olfaction (MRO), impairing our ability to develop efficient gas source localization strategies and to validate gas distribution maps produced by autonomous mobile robots. Previous ground truth measurements of gas dispersion have been mostly based on expensive tracer optical methods or 2D chemical sensor grids deployed only at ground level. With the ever-increasing trend towards gas-sensitive aerial robots, 3D measurements of gas dispersion become necessary to characterize the environment these platforms can explore. This paper presents ten different experiments performed with a 3D grid of 27 metal oxide semiconductor (MOX) sensors to visualize the temporal evolution of gas distribution produced by an evaporating ethanol source placed at different locations in an office room, including variations in height, release rate and air flow. We also studied which features of the MOX sensor signals are optimal for predicting the source location, considering different lengths of the measurement window. We found strongly time-varying and counter-intuitive gas distribution patterns that disprove some assumptions commonly held in the MRO field, such as that heavy gases disperse along ground level. Correspondingly, ground-level gas distributions were rarely useful for localizing the gas source and elevated measurements were much more informative. We make the dataset and the code publicly available to enable the community to develop, validate, and compare new approaches related to gas sensing in complex environments. }, year = {2020} } @article{Hou1391197, author = {Hou, Hui-Rang and Lilienthal, Achim J. and Meng, Qing-Hao}, institution = {Örebro University, School of Science and Technology}, institution = {Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin, China}, institution = {Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin, China}, journal = {IEEE Access}, note = {Funding Agencies:National Natural Science Foundation of China61573253 National Key Research and Development Program of China  2017YFC0306200}, pages = {7227--7235}, eid = {8945323}, title = {Gas Source Declaration with Tetrahedral Sensing Geometries and Median Value Filtering Extreme Learning Machine}, volume = {8}, DOI = {10.1109/ACCESS.2019.2963059}, keywords = {Gas source declaration, tetrahedron, gas concentration measurement, wind information, extreme learning machine, median value filtering}, abstract = {Gas source localization (including gas source declaration) is critical for environmental monitoring, pollution control and chemical safety. In this paper we approach the gas source declaration problem by constructing a tetrahedron, each vertex of which consists of a gas sensor and a three-dimensional (3D) anemometer. With this setup, the space sampled around a gas source can be divided into two categories, i.e. inside (“source in”) and outside (“source out”) the tetrahedron, posing gas source declaration as a classification problem. For the declaration of the “source in” or “source out” cases, we propose to directly take raw gas concentration and wind measurement data as features, and apply a median value filtering based extreme learning machine (M-ELM) method. Our experimental results show the efficacy of the proposed method, yielding accuracies of 93.2% and 100% for gas source declaration in the regular and irregular tetrahedron experiments, respectively. These results are better than that of the ELM-MFC (mass flux criterion) and other variants of ELM algorithms. }, year = {2020} } @inproceedings{Winkler1506465, author = {Winkler, Nicolas P. and Neumann, Patrick P. and S{\"a}{\"a}m{\"a}nen, Arto and Schaffernicht, Erik and Lilienthal, Achim J.}, booktitle = {MATERIALS TODAY-PROCEEDINGS : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Occupational Safety, Finnish Institute of Occupational Health, Tampere, Finland}, note = {Funding Agency:SAF(sic)RA}, pages = {250--253}, title = {High-quality meets low-cost : Approaches for hybrid-mobility sensor networks}, series = {Materials Today: Proceedings}, volume = {32}, DOI = {10.1016/j.matpr.2020.05.799}, keywords = {Mobile robot olfaction, Air quality monitoring, Wireless sensor network, Gas distribution mapping, Occupational health}, abstract = {Air pollution within industrial scenarios is a major risk for workers, which is why detailed knowledge about the dispersion of dusts and gases is necessary. This paper introduces a system combining stationary low-cost and high-quality sensors, carried by ground robots and unmanned aerial vehicles. Based on these dense sampling capabilities, detailed distribution maps of dusts and gases will be created. This system enables various research opportunities, especially on the fields of distribution mapping and sensor planning. Standard approaches for distribution mapping can be enhanced with knowledge about the environment's characteristics, while the effectiveness of new approaches, utilizing neural networks, can be further investigated. The influence of different sensor network setups on the predictive quality of distribution algorithms will be researched and metrics for the quantification of a sensor network's quality will be investigated. }, year = {2020} } @inproceedings{Schindler1523964, author = {Schindler, Maike and Schaffernicht, Erik and Lilienthal, Achim}, booktitle = {Interim Proceedings of the 44th Conference of the International Group for the Psychology of Mathematics Education. Khon Kaen, Thailand: PME : }, institution = {Örebro University, School of Science and Technology}, institution = {University of Cologne, Germany}, pages = {518--527}, title = {Identifying student strategies through eye tracking and unsupervised learning : The case of quantity recognition}, abstract = {Identifying student strategies is an important endeavor in mathematics education research. Eye tracking (ET) has proven to be valuable for this purpose: E.g., analysis of ET videos allows for identification of student strategies, particularly in quantity recognition activities. Yet, “manual”, qualitative analysis of student strategies from ET videos is laborious—which calls for more efficient methods of analysis. Our methodological paper investigates opportunities and challenges of using unsupervised machine learning (USL) in combination with ET data: Based on empirical ET data of N = 164 students and heat maps of their gaze distributions on the task, we used a clustering algorithm to identify student strategies from ET data and investigate whether the clusters are consistent regarding student strategies. }, year = {2020} } @incollection{Kucner1430082, author = {Kucner, Tomasz Piotr and Lilienthal, Achim and Magnusson, Martin and Palmieri, L. and Swaminathan, Chittaranjan Srinivas}, booktitle = {Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots : }, institution = {Örebro University, School of Science and Technology}, institution = {Corporate Research, Robert Bosch GmbH, Renningen, Germany }, pages = {1--13}, title = {Introduction}, series = {Cognitive Systems Monographs}, number = {40}, DOI = {10.1007/978-3-030-41808-3_1}, abstract = {Change and motion are inherent features of reality. The ability to recognise patterns governing changes has allowed humans to thrive in a dynamic reality. Similarly, dynamics awareness can also improve the performance of robots. Dynamics awareness is an umbrella term covering a broad spectrum of concepts. In this chapter, we present the key aspects of dynamics awareness. We introduce two motivating examples presenting the challenges for robots operating in a dynamic environment. We discuss the benefits of using spatial models of dynamics and analyse the challenges of building such models. }, ISBN = {978-3-030-41807-6}, ISBN = {978-3-030-41808-3}, year = {2020} } @incollection{Kucner1429966, author = {Kucner, Tomasz Piotr and Lilienthal, Achim and Magnusson, Martin and Palmieri, L. and Swaminathan, Chittaranjan Srinivas}, booktitle = {Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots : }, institution = {Örebro University, School of Science and Technology}, institution = {Corporate Research, Robert Bosch GmbH, Renningen, Germany}, pages = {15--32}, title = {Maps of Dynamics}, series = {Cognitive Systems Monographs}, DOI = {10.1007/978-3-030-41808-3_2}, abstract = {The task of building maps of dynamics is the key focus of this book, as well as how to use them for motion planning. In this chapter, we present a categorisation and overview of different types of maps of dynamics. Furthermore, we give an overview of approaches to motion planning in dynamic environments, with a focus on motion planning over maps of dynamics.  }, ISBN = {978-3-030-41807-6}, ISBN = {978-3-030-41808-3}, year = {2020} } @incollection{Kucner1429775, author = {Kucner, Tomasz Piotr and Lilienthal, Achim and Magnusson, Martin and Palmieri, L. and Swaminathan, Chittaranjan Srinivas}, booktitle = {Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots : }, institution = {Örebro University, School of Science and Technology}, institution = {Corporate Research, Robert Bosch GmbH, Renningen, Germany}, pages = {65--113}, title = {Modelling Motion Patterns with Circular-Linear Flow Field Maps}, series = {Cognitive Systems Monographs}, number = {40}, DOI = {10.1007/978-3-030-41808-3_4}, abstract = {The shared feature of the flow of discrete objects and continuous media is that they both can be represented as velocity vectors encapsulating direction and speed of motion. In this chapter, we present a method for modelling the flow of discrete objects and continuous media as continuous Gaussian mixture fields. The proposed model associates to each part of the environment a Gaussian mixture model describing the local motion patterns. We also present a learning method, designed to build the model from a set of sparse, noisy and incomplete observations.  }, ISBN = {978-3-030-41807-6}, ISBN = {978-3-030-41808-3}, year = {2020} } @incollection{Kucner1429926, author = {Kucner, Tomasz Piotr and Lilienthal, Achim and Magnusson, Martin and Palmieri, L. and Swaminathan, Chittaranjan Srinivas}, booktitle = {Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots : }, institution = {Örebro University, School of Science and Technology}, institution = {Corporate Research, Robert Bosch GmbH, Renningen, Germany}, pages = {33--64}, title = {Modelling Motion Patterns with Conditional Transition Map}, series = {Cognitive Systems Monographs}, number = {40}, DOI = {10.1007/978-3-030-41808-3_3}, abstract = {The key idea of modelling flow of discrete objects is to capture the way they move through the environment. One method to capture the flow is to observe changes in occupancy caused by the motion of discrete objects. In this chapter, we present a method to model and learn occupancy shifts caused by an object moving through the environment. The key idea is observe temporal changes changes in the occupancy of adjacent cells, and based on the temporal offset infer the direction of the occupancy flow. }, ISBN = {978-3-030-41807-6}, ISBN = {978-3-030-41808-3}, year = {2020} } @incollection{Kucner1430037, author = {Kucner, Tomasz Piotr and Lilienthal, Achim and Magnusson, Martin and Palmieri, L. and Swaminathan, Chittaranjan Srinivas}, booktitle = {Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots : }, institution = {Örebro University, School of Science and Technology}, institution = {Corporate Research, Robert Bosch GmbH, Renningen, Germany}, pages = {115--141}, title = {Motion Planning Using MoDs}, series = {Cognitive Systems Monographs}, number = {40}, DOI = {10.1007/978-3-030-41808-3_5}, abstract = {Maps of dynamics can be beneficial for motion planning. Information about motion patterns in the environment can lead to finding flow-aware paths, allowing robots to align better to the expected motion: either of other agents in the environment or the flow of air or another medium. The key idea of flow-aware motion planning is to include adherence to the flow represented in the MoD into the motion planning algorithm’s sub-units (i.e. cost function, sampling mechanism), thereby biasing the motion planner into obeying local and implicit traffic rules.  }, ISBN = {978-3-030-41807-6}, ISBN = {978-3-030-41808-3}, year = {2020} } @inproceedings{Vintr1524193, author = {Vintr, Tomas and Yan, Zhi and Eyisoy, Kerem and Kubis, Filip and Blaha, Jan and Ulrich, Jiri and Swaminathan, Chittaranjan Srinivas and Molina, Sergi and Kucner, Tomasz Piotr and Magnusson, Martin and Cielniak, Grzegorz and Faigl, Jan and Duckett, Tom and Lilienthal, Achim J. and Krajnik, Tomas}, booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, institution = {Czech Technical University in Prague, Prague, the Czech Republic}, institution = {University of Technology of Belfort-Montbeliard (UTBM), France}, institution = {Department of Computer Engineering, Faculty of Engineering, Marmara University, Turkey}, institution = {Czech Technical University in Prague, Prague, the Czech Republic}, institution = {Czech Technical University in Prague, Prague, the Czech Republic}, institution = {Czech Technical University in Prague, Prague, the Czech Republic}, institution = {University of Lincoln, UK}, institution = {University of Lincoln, UK}, institution = {Czech Technical University in Prague, Prague, the Czech Republic}, institution = {University of Lincoln, UK}, institution = {Czech Technical University in Prague, Prague, the Czech Republic}, note = {Funding agencies:OP VVV CZ.02.101/0.0/0.0/16 019/0000765CSF projects GA18-18858S GC20-27034J SGS19/176/OHK3/3T/13 FR-8J18FR018PHC Barrande programme 40682ZHToyota Partner Robot joint research project (MACPOLO)}, pages = {11197--11204}, title = {Natural Criteria for Comparison of Pedestrian Flow Forecasting Models}, series = {IEEE International Conference on Intelligent Robots and Systems. Proceedings}, DOI = {10.1109/IROS45743.2020.9341672}, abstract = {Models of human behaviour, such as pedestrian flows, are beneficial for safe and efficient operation of mobile robots. We present a new methodology for benchmarking of pedestrian flow models based on the afforded safety of robot navigation in human-populated environments. While previous evaluations of pedestrian flow models focused on their predictive capabilities, we assess their ability to support safe path planning and scheduling. Using real-world datasets gathered continuously over several weeks, we benchmark state-of-theart pedestrian flow models, including both time-averaged and time-sensitive models. In the evaluation, we use the learned models to plan robot trajectories and then observe the number of times when the robot gets too close to humans, using a predefined social distance threshold. The experiments show that while traditional evaluation criteria based on model fidelity differ only marginally, the introduced criteria vary significantly depending on the model used, providing a natural interpretation of the expected safety of the system. For the time-averaged flow models, the number of encounters increases linearly with the percentage operating time of the robot, as might be reasonably expected. By contrast, for the time-sensitive models, the number of encounters grows sublinearly with the percentage operating time, by planning to avoid congested areas and times. }, ISBN = {9781728162133}, ISBN = {9781728162126}, year = {2020} } @article{Hoang1513204, author = {Hoang, Dinh-Cuong and Lilienthal, Achim and Stoyanov, Todor}, institution = {Örebro University, School of Science and Technology}, journal = {Robotics and Autonomous Systems}, eid = {103632}, title = {Object-RPE : Dense 3D Reconstruction and Pose Estimation with Convolutional Neural Networks}, volume = {133}, DOI = {10.1016/j.robot.2020.103632}, keywords = {Object pose estimation, 3D reconstruction, Semantic mapping, 3D registration}, abstract = {We present an approach for recognizing objects present in a scene and estimating their full pose by means of an accurate 3D instance-aware semantic reconstruction. Our framework couples convolutional neural networks (CNNs) and a state-of-the-art dense Simultaneous Localisation and Mapping(SLAM) system, ElasticFusion [1], to achieve both high-quality semantic reconstruction as well as robust 6D pose estimation for relevant objects. We leverage the pipeline of ElasticFusion as a back-bone and propose a joint geometric and photometric error function with per-pixel adaptive weights. While the main trend in CNN-based 6D pose estimation has been to infer an object’s position and orientation from single views of the scene, our approach explores performing pose estimation from multiple viewpoints, under the conjecture that combining multiple predictions can improve the robustness of an object detection system. The resulting system is capable of producing high-quality instance-aware semantic reconstructions of room-sized environments, as well as accurately detecting objects and their 6D poses. The developed method has been verified through extensive experiments on different datasets. Experimental results confirmed that the proposed system achieves improvements over state-of-the-art methods in terms of surface reconstruction and object pose prediction. Our code and video are available at https://sites.google.com/view/object-rpe. }, year = {2020} } @article{Hoang1427623, author = {Hoang, Dinh-Cuong and Lilienthal, Achim and Stoyanov, Todor}, institution = {Örebro University, School of Science and Technology}, journal = {IEEE Robotics and Automation Letters}, number = {2}, pages = {1962--1969}, title = {Panoptic 3D Mapping and Object Pose Estimation Using Adaptively Weighted Semantic Information}, volume = {5}, DOI = {10.1109/LRA.2020.2970682}, keywords = {RGB-D perception, object detection, segmen-tation and categorization, mapping}, abstract = {We present a system capable of reconstructing highly detailed object-level models and estimating the 6D pose of objects by means of an RGB-D camera. In this work, we integrate deep-learning-based semantic segmentation, instance segmentation, and 6D object pose estimation into a state of the art RGB-D mapping system. We leverage the pipeline of ElasticFusion as a backbone and propose modifications of the registration cost function to make full use of the semantic class labels in the process. The proposed objective function features tunable weights for the depth, appearance, and semantic information channels, which are learned from data. A fast semantic segmentation and registration weight prediction convolutional neural network (Fast-RGBD-SSWP) suited to efficient computation is introduced. In addition, our approach explores performing 6D object pose estimation from multiple viewpoints supported by the high-quality reconstruction system. The developed method has been verified through experimental validation on the YCB-Video dataset and a dataset of warehouse objects. Our results confirm that the proposed system performs favorably in terms of surface reconstruction, segmentation quality, and accurate object pose estimation in comparison to other state-of-the-art systems. Our code and video are available at https://sites.google.com/view/panoptic-mope. }, year = {2020} } @incollection{Kucner1430079, author = {Kucner, Tomasz Piotr and Magnusson, Martin and Swaminathan, Chittaranjan Srinivas and Lilienthal, Achim and Palmieri, L.}, booktitle = {Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots : }, institution = {Örebro University, School of Science and Technology}, institution = {Corporate Research Robert Bosch GmbH, Renningen, Germany}, pages = {vii--x}, title = {Preface}, series = {Cognitive Systems Monographs}, number = {40}, ISBN = {978-3-030-41807-6}, ISBN = {978-3-030-41808-3}, year = {2020} } @book{Kucner1427833, author = {Kucner, Tomasz Piotr and Lilienthal, Achim and Magnusson, Martin and Palmieri, Luigi and Swaminathan, Chittaranjan Srinivas}, institution = {Örebro University, School of Science and Technology}, institution = {Corporate Research, Robert Bosch GmbH, Renningen, Germany}, pages = {151}, publisher = {Springer International Publishing}, title = {Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots}, series = {Cognitive Systems Monographs}, number = {40}, DOI = {10.1007/978-3-030-41808-3}, keywords = {Mobile Robots, Probabilistic Mapping, Autonomous Robots, Robots, Cognitive Systems}, abstract = {This book describes how robots can make sense of motion in their surroundings and use the patterns they observe to blend in better in dynamic environments shared with humans.The world around us is constantly changing. Nonetheless, we can find our way and aren’t overwhelmed by all the buzz, since motion often follows discernible patterns. Just like humans, robots need to understand the patterns behind the dynamics in their surroundings to be able to efficiently operate e.g. in a busy airport. Yet robotic mapping has traditionally been based on the static world assumption, which disregards motion altogether. In this book, the authors describe how robots can instead explicitly learn patterns of dynamic change from observations, store those patterns in Maps of Dynamics (MoDs), and use MoDs to plan less intrusive, safer and more efficient paths. The authors discuss the pros and cons of recently introduced MoDs and approaches to MoD-informed motion planning, and provide an outlook on future work in this emerging, fascinating field.  }, ISBN = {978-3-030-41807-6}, ISBN = {978-3-030-41808-3}, year = {2020} } @inproceedings{Swaminathan1524187, author = {Swaminathan, Chittaranjan Srinivas and Kucner, Tomasz Piotr and Magnusson, Martin and Lilienthal, Achim J.}, booktitle = {HRI 2020 Workshop on Test Methods and Metrics for Effective HRI in Real World Human-Robot Teams : }, institution = {Örebro University, School of Science and Technology}, title = {Quantitative Metrics for Execution-Based Evaluation of Human-Aware Global Motion Planning}, URL = {https://hri-methods-metrics.github.io/Prev_years/2020/Swaminathan%20-%20Abstract.pdf}, year = {2020} } @article{Schindler1380651, author = {Schindler, Maike and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Faculty of Human Sciences, University of Cologne, Cologne, Germany}, journal = {International Journal of Science and Mathematics Education}, note = {Funding Agency:{\"O}rebro University}, number = {8}, pages = {1565--1586}, title = {Students' Creative Process in Mathematics : Insights from Eye-Tracking-Stimulated Recall Interview on Students' Work on Multiple Solution Tasks}, volume = {18}, DOI = {10.1007/s10763-019-10033-0}, keywords = {Creative process, Eye tracking (ET), Mathematical creativity, Multiple solution tasks (MSTs), Stimulated recall interview (SRI)}, abstract = {Students' creative process in mathematics is increasingly gaining significance in mathematics education research. Researchers often use Multiple Solution Tasks (MSTs) to foster and evaluate students' mathematical creativity. Yet, research so far predominantly had a product-view and focused on solutions rather than the process leading to creative insights. The question remains unclear how students' process solving MSTs looks like-and if existing models to describe (creative) problem solving can capture this process adequately. This article presents an explorative, qualitative case study, which investigates the creative process of a school student, David. Using eye-tracking technology and a stimulated recall interview, we trace David's creative process. Our findings indicate what phases his creative process in the MST involves, how new ideas emerge, and in particular where illumination is situated in this process. Our case study illustrates that neither existing models on the creative process, nor on problem solving capture David's creative process fully, indicating the need to partially rethink students' creative process in MSTs. }, year = {2020} } @article{Rudenko1387088, author = {Rudenko, Andrey and Kucner, Tomasz Piotr and Swaminathan, Chittaranjan Srinivas and Chadalavada, Ravi Teja and Arras, Kai O. and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Robotics Research, Bosch Corporate Research, Stuttgart, Germany}, institution = {Robotics Research, Bosch Corporate Research, Stuttgart, Germany}, journal = {IEEE Robotics and Automation Letters}, number = {2}, pages = {676--682}, title = {TH{\"O}R : Human-Robot Navigation Data Collection and Accurate Motion Trajectories Dataset}, volume = {5}, DOI = {10.1109/LRA.2020.2965416}, keywords = {Social Human-Robot Interaction, Motion and Path Planning, Human Detection and Tracking}, abstract = {Understanding human behavior is key for robots and intelligent systems that share a space with people. Accordingly, research that enables such systems to perceive, track, learn and predict human behavior as well as to plan and interact with humans has received increasing attention over the last years. The availability of large human motion datasets that contain relevant levels of difficulty is fundamental to this research. Existing datasets are often limited in terms of information content, annotation quality or variability of human behavior. In this paper, we present THÖR, a new dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for position, head orientation, gaze direction, social grouping, obstacles map and goal coordinates. THÖR also contains sensor data collected by a 3D lidar and involves a mobile robot navigating the space. We propose a set of metrics to quantitatively analyze motion trajectory datasets such as the average tracking duration, ground truth noise, curvature and speed variation of the trajectories. In comparison to prior art, our dataset has a larger variety in human motion behavior, is less noisy, and contains annotations at higher frequencies. }, URL = {https://arxiv.org/abs/1909.04403}, year = {2020} } @inproceedings{Mielle1356645, author = {Mielle, Malcolm and Magnusson, Martin and Lilienthal, Achim J.}, booktitle = {2019 European Conference on Mobile Robots (ECMR) : }, institution = {Örebro University, School of Science and Technology}, note = {Funding Agency:EIT Raw Materials project FIREMII  18011}, title = {A comparative analysis of radar and lidar sensing for localization and mapping}, DOI = {10.1109/ECMR.2019.8870345}, abstract = {Lidars and cameras are the sensors most commonly used for Simultaneous Localization And Mapping (SLAM). However, they are not effective in certain scenarios, e.g. when fire and smoke are present in the environment. While radars are much less affected by such conditions, radar and lidar have rarely been compared in terms of the achievable SLAM accuracy. We present a principled comparison of the accuracy of a novel radar sensor against that of a Velodyne lidar, for localization and mapping. We evaluate the performance of both sensors by calculating the displacement in position and orientation relative to a ground-truth reference positioning system, over three experiments in an indoor lab environment. We use two different SLAM algorithms and found that the mean displacement in position when using the radar sensor was less than 0.037 m, compared to 0.011m for the lidar. We show that while producing slightly less accurate maps than a lidar, the radar can accurately perform SLAM and build a map of the environment, even including details such as corners and small walls. }, ISBN = {978-1-7281-3605-9}, ISBN = {978-1-7281-3606-6}, year = {2019} } @inproceedings{Hullmann1350265, author = {H{\"u}llmann, Dino and Neumann, Patrick P. and Monroy, Javier and Lilienthal, Achim J.}, booktitle = {ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Machine Perception and Intelligent Robotics group (MAPIR), Universidad de Malaga, Spain}, eid = {8823330}, title = {A Realistic Remote Gas Sensor Model for Three-Dimensional Olfaction Simulations}, DOI = {10.1109/ISOEN.2019.8823330}, keywords = {gas detector, remote gas sensor, sensor modelling, TDLAS, gas dispersion simulation}, abstract = {Remote gas sensors like those based on the Tunable Diode Laser Absorption Spectroscopy (TDLAS) enable mobile robots to scan huge areas for gas concentrations in reasonable time and are therefore well suited for tasks such as gas emission surveillance and environmental monitoring. A further advantage of remote sensors is that the gas distribution is not disturbed by the sensing platform itself if the measurements are carried out from a sufficient distance, which is particularly interesting when a rotary-wing platform is used. Since there is no possibility to obtain ground truth measurements of gas distributions, simulations are used to develop and evaluate suitable olfaction algorithms. For this purpose several models of in-situ gas sensors have been developed, but models of remote gas sensors are missing. In this paper we present two novel 3D ray-tracer-based TDLAS sensor models. While the first model simplifies the laser beam as a line, the second model takes the conical shape of the beam into account. Using a simulated gas plume, we compare the line model with the cone model in terms of accuracy and computational cost and show that the results generated by the cone model can differ significantly from those of the line model. }, ISBN = {978-1-5386-8327-9}, ISBN = {978-1-5386-8328-6}, year = {2019} } @inproceedings{Adolfsson1391182, author = {Adolfsson, Daniel and Lowry, Stephanie and Magnusson, Martin and Lilienthal, Achim J. and Andreasson, Henrik}, booktitle = {2019 European Conference on Mobile Robots (ECMR) : }, institution = {Örebro University, School of Science and Technology}, title = {A Submap per Perspective : Selecting Subsets for SuPer Mapping that Afford Superior Localization Quality}, DOI = {10.1109/ECMR.2019.8870941}, abstract = {This paper targets high-precision robot localization. We address a general problem for voxel-based map representations that the expressiveness of the map is fundamentally limited by the resolution since integration of measurements taken from different perspectives introduces imprecisions, and thus reduces localization accuracy.We propose SuPer maps that contain one Submap per Perspective representing a particular view of the environment. For localization, a robot then selects the submap that best explains the environment from its perspective. We propose SuPer mapping as an offline refinement step between initial SLAM and deploying autonomous robots for navigation. We evaluate the proposed method on simulated and real-world data that represent an important use case of an industrial scenario with high accuracy requirements in an repetitive environment. Our results demonstrate a significantly improved localization accuracy, up to 46% better compared to localization in global maps, and up to 25% better compared to alternative submapping approaches. }, ISBN = {978-1-7281-3605-9}, year = {2019} } @article{Neumann1347768, author = {Neumann, Patrick P. and H{\"u}llmann, Dino and Krentel, Daniel and Kluge, Martin and Dzierliński, Marcin and Lilienthal, Achim J. and Bartholmai, Matthias}, institution = {Örebro University, School of Science and Technology}, institution = {Department 8 Non-destructive testing, Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Department 8 Non-destructive testing, Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Department 8 Non-destructive testing, Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Department 8 Non-destructive testing, Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Dział Urządzeń Ciśnieniowych, Urząd Dozoru Technicznego (UDT), Poland}, institution = {Department 8 Non-destructive testing, Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, journal = {European Journal of Remote Sensing}, note = {Funding Agencies:German Federal Ministry for Economic Affairs and Energy (BMWi) within the ZIM program  KF2201091HM4BAM }, number = {Sup. 3}, pages = {2--16}, title = {Aerial-based gas tomography : from single beams to complex gas distributions}, volume = {52}, DOI = {10.1080/22797254.2019.1640078}, keywords = {Aerial robot olfaction, mobile robot olfaction, gas tomography, TDLAS, plume}, abstract = {In this paper, we present and validate the concept of an autonomous aerial robot to reconstruct tomographic 2D slices of gas plumes in outdoor environments. Our platform, the so-called Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS), combines a lightweight Tunable Diode Laser Absorption Spectroscopy (TDLAS) gas sensor with a 3-axis aerial stabilization gimbal for aiming at a versatile octocopter. While the TDLAS sensor provides integral gas concentration measurements, it does not measure the distance traveled by the laser diode?s beam nor the distribution of gas along the optical path. Thus, we complement the set-up with a laser rangefinder and apply principles of Computed Tomography (CT) to create a model of the spatial gas distribution from a set of integral concentration measurements. To allow for a fundamental ground truth evaluation of the applied gas tomography algorithm, we set up a unique outdoor test environment based on two 3D ultrasonic anemometers and a distributed array of 10 infrared gas transmitters. We present results showing its performance characteristics and 2D plume reconstruction capabilities under realistic conditions. The proposed system can be deployed in scenarios that cannot be addressed by currently available robots and thus constitutes a significant step forward for the field of Mobile Robot Olfaction (MRO). }, year = {2019} } @article{Wiedemann1284133, author = {Wiedemann, Thomas and Lilienthal, Achim J. and Shutin, Dmitriy}, institution = {Örebro University, School of Science and Technology}, institution = {German Aerospace Center, Oberpfaffenhofen, Germany}, institution = {German Aerospace Center, Oberpfaffenhofen, Germany}, journal = {Sensors}, note = {Funding Agencies:European Commission  645101 Valles Marineris Explorer initiative of DLR (German Aerospace Center) Space Administration }, number = {3}, eid = {520}, title = {Analysis of Model Mismatch Effects for a Model-based Gas Source Localization Strategy Incorporating Advection Knowledge}, volume = {19}, DOI = {10.3390/s19030520}, keywords = {Robotic exploration, gas source localization, mobile robot olfaction, sparse Bayesian learning, multi-agent system, advection-diffusion model}, abstract = {In disaster scenarios, where toxic material is leaking, gas source localization is a common but also dangerous task. To reduce threats for human operators, we propose an intelligent sampling strategy that enables a multi-robot system to autonomously localize unknown gas sources based on gas concentration measurements. This paper discusses a probabilistic, model-based approach for incorporating physical process knowledge into the sampling strategy. We model the spatial and temporal dynamics of the gas dispersion with a partial differential equation that accounts for diffusion and advection effects. We consider the exact number of sources as unknown, but assume that gas sources are sparsely distributed. To incorporate the sparsity assumption we make use of sparse Bayesian learning techniques. Probabilistic modeling can account for possible model mismatch effects that otherwise can undermine the performance of deterministic methods. In the paper we evaluate the proposed gas source localization strategy in simulations using synthetic data. Compared to real-world experiments, a simulated environment provides us with ground truth data and reproducibility necessary to get a deeper insight into the proposed strategy. The investigation shows that (i) the probabilistic model can compensate imperfect modeling; (ii) the sparsity assumption significantly accelerates the source localization; and (iii) a-priori advection knowledge is of advantage for source localization, however, it is only required to have a certain level of accuracy. These findings will help in the future to parameterize the proposed algorithm in real world applications. }, year = {2019} } @article{Lindner1382292, author = {Lindner, Helen Y and Hill, Wendy and Hermansson, Liselotte and Lilienthal, Achim J.}, institution = {Örebro University, School of Health Sciences}, institution = {Örebro University, School of Science and Technology}, institution = {Institute of Biomedical Engineering, UNB, Fredericton, Canada}, institution = {University Health Care Research Centre, Faculty of Medicine and Health, Örebro University, Örebro, Sweden; Dept. of Prosthetics and Orthotics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden}, journal = {Prosthetics and Orthotics International}, number = {1 suppl. 1}, pages = {52--52}, title = {Cognitive load and compensatory movement in learning to use a multi-function hand}, volume = {43}, keywords = {Eye tracking, upper limb prosthetics, cognitive load, compensatory movement}, abstract = {BACKGROUND: Recent technology provides increased dexterity in multi-function hands with the potential to reduce compensatory body movements. However, it is challenging to learn how to operate a hand that has up to 36 grips. While the cognitive load required to use these hands is unknown, it is clear that if the cognitive load is too high, the user may stop using the multi-functional hand or may not take full advantage of its advanced features. AIM: The aim of this project was to compare cognitive load and compensatory movement in using a multi-function hand versus a conventional myo hand. METHOD: An experienced prosthesis user was assessed using his conventional myo hand and an unfamiliar iLimb Ultra hand, with two-site control and the same wrist for both prostheses. He was trained to use power grip, lateral grip and pinch grip and then completed the SHAP test while wearing the Tobii Pro 2 eye-tracking glasses. Pupil diameter (normal range: 2-4mm during normal light) was used to indicate the amount of cognitive load.[1] The number of eye fixations on the prosthesis indicate the need of visual feedback during operation. Dartfish motion capture was used to track the maximum angles for shoulder abduction and elbow flexion. RESULTS: Larger pupils were found in the use of Ilimb ultra (2.6-5.6mm) than in the use of conventional myo hand (2.4-3.5mm) during the SHAP abstract light tests. The pupils dilated most often during changing grips, e.g. switching to pinch grip for the tripod task (from 2.7 to 5.6mm). After training of using power grip and pinch grip repeatedly, the maximum pupil diameter decreased from 5.6 to 3.3mm. The number of eye fixations on the I-limb ultra (295 fixations) were also higher than on the conventional myo-hand (139 fixations). Smaller shoulder abduction and elbow flexion were observed in the use of I-limb ultra (16.6°, 36.1°) than in the use of conventional myo hand (57°, 52.7°). DISCUSSION AND CONCLUSION: Although it is cognitively demanding to learn to use a multi-function hand, it is possible to decrease this demand with adequate prosthetic training. Our results suggest that using a multi-function hand enables reduction of body compensatory movement, however at the cost of a higher cognitive load. Further research with more prosthesis users and other multi-function hands is needed to confirm the study findings. REFERENCES [1] van der Wel P, van Steenbergen H. Psychon Bull Rev 2018; 25(6):2005-15. ACKNOWLEDGEMENTS: This project was supported financially by Norrbacka-Eugenia Foundation, Promobilia Foundation and Örebro University. }, URL = {https://doi.org/10.1177/0309364619883197}, year = {2019} } @inproceedings{Lilienthal1391180, author = {Lilienthal, Achim J. and Schindler, Maike}, booktitle = {43rd Annual Meeting of the International Group for the Psychology of Mathematics Education : }, institution = {Örebro University, School of Science and Technology}, institution = {Department of Special Education, University of Cologne, Germany}, pages = {62--62}, title = {Current Trends in Eye Tracking Research in Mathematics Education: A PME Literature Review : A PME Survey}, volume = {4}, keywords = {Eye Tracking, Mathematics Education Research, Survey, PME}, abstract = {Eye tracking (ET) is a research method that receives growing interest in mathematics education research (MER). This paper aims to give a literature overview, specifically focusing on the evolution of interest in this technology, ET equipment, and analysis methods used in mathematics education. To capture the current state, we focus on papers published in the proceedings of PME, one of the primary conferences dedicated to MER, of the last ten years. We identify trends in interest, methodology, and methods of analysis that are used in the community, and discuss possible future developments. }, URL = {https://arxiv.org/abs/1904.12581}, year = {2019} } @inproceedings{Schindler1390984, author = {Schindler, Maike and Schaffernicht, Erik and Lilienthal, Achim J.}, booktitle = {Proceedings of the 43rd Conference of the International Group for the Psychology of Mathematics Education : }, institution = {Örebro University, School of Science and Technology}, institution = {University of Cologne, Cologne, Germany}, pages = {281--288}, publisher = {PME}, title = {Differences in Quantity Recognition Between Students with and without Mathematical Difficulties Analyzed Through Eye : Analysis Through Eye-Tracking and AI}, volume = {3}, abstract = {Difficulties in mathematics learning are an important topic in practice and research. In particular, researchers and practitioners need to identify students’ needs for support to teach and help them adequately. However, empirical research about group differences of students with and without mathematical difficulties (MD) is still scarce. Previous research suggests that students with MD may differ in their quantity recognition strategies in structured whole number representations from students without MD. This study uses eye-tracking (ET), combined with Artificial Intelligence (AI), in particular pattern recognition methods, to analyze group differences in gaze patterns in quantity recognition of N=164 fifth grade students. }, year = {2019} } @article{Schindler1306375, author = {Schindler, Maike and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Faculty of Human Sciences, University of Cologne, Cologne, Germany}, journal = {Educational Studies in Mathematics}, number = {1}, pages = {123--139}, title = {Domain-specific interpretation of eye tracking data : towards a refined use of the eye-mind hypothesis for the field of geometry}, volume = {101}, DOI = {10.1007/s10649-019-9878-z}, keywords = {Eye tracking, Eye movements, Eye-mind hypothesis, Geometry}, abstract = {Eye tracking is getting increasingly popular in mathematics education research. Studies predominantly rely on the so-called eye-mind hypothesis (EMH), which posits that what persons fixate on closely relates to what they process. Given that the EMH was developed in reading research, we see the risk that implicit assumptions are tacitly adopted in mathematics even though they may not apply in this domain. This article investigates to what extent the EMH applies in mathematics - geometry in particular - and aims to lift the discussion of what inferences can be validly made from eye-tracking data. We use a case study to investigate the need for a refinement of the use of the EMH. In a stimulated recall interview, a student described his original thoughts perusing a gaze-overlaid video recorded when he was working on a geometry problem. Our findings contribute to better a understanding of when and how the EMH applies in the subdomain of geometry. In particular, we identify patterns of eye movements that provide valuable information on students' geometry problem solving: certain patterns where the eye fixates on what the student is processing and others where the EMH does not hold. Identifying such patterns may contribute to an interpretation theory for students' eye movements in geometry - exemplifying a domain-specific theory that may reduce the inherent ambiguity and uncertainty that eye tracking data analysis has. }, year = {2019} } @inproceedings{Hullmann1391191, author = {H{\"u}llmann, Dino and Neumann, Patrick and Scheuschner, Nils and Bartholmai, Matthias and Lilienthal, Achim J.}, booktitle = {2019 IEEE SENSORS : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM) Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM) Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM) Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM) Berlin, Germany}, publisher = {IEEE}, title = {Experimental Validation of the Cone-Shaped Remote Gas Sensor Model}, series = {Proceedings of IEEE Sensors}, DOI = {10.1109/SENSORS43011.2019.8956613}, keywords = {Remote gas sensor model, TDLAS, gas dispersion simulation}, abstract = {Remote gas sensors mounted on mobile robots enable the mapping of gas distributions in large or hardly accessible areas. A challenging task, however, is the generation of three-dimensional distribution maps from these gas measurements. Suitable reconstruction algorithms can be adapted, for instance, from the field of computed tomography (CT), but both their performance and strategies for selecting optimal measuring poses must be evaluated. For this purpose simulations are used, since, in contrast to field tests, they allow repeatable conditions. Although several simulation tools exist, they lack realistic models of remote gas sensors. Recently, we introduced a model for a Tunable Diode Laser Absorption Spectroscopy (TDLAS) gas sensor taking into account the conical shape of its laser beam. However, the novel model has not yet been validated with experiments. In this paper, we compare our model with a real sensor device and show that the assumptions made hold. }, ISBN = {978-1-7281-1634-1}, year = {2019} } @article{Xing1368091, author = {Xing, Yuxin and Vincent, Timothy A. and Fan, Han and Schaffernicht, Erik and Hernandez Bennetts, Victor and Lilienthal, Achim J. and Cole, Marina and Gardner, Julian W.}, institution = {Örebro University, School of Science and Technology}, institution = {School of Engineering, University of Warwick, Coventry, UK}, institution = {School of Engineering, University of Warwick, Coventry, UK}, institution = {School of Engineering, University of Warwick, Coventry, UK}, institution = {School of Engineering, University of Warwick, Coventry, UK}, journal = {IEEE Sensors Journal}, number = {24}, pages = {12418--12431}, title = {FireNose on Mobile Robot in Harsh Environments}, volume = {19}, DOI = {10.1109/JSEN.2019.2939039}, keywords = {FireNose, mobile robot, MOX sensor, gas map, harsh environments}, abstract = {In this work we present a novel multi-sensor unit, a.k.a. FireNose, to detect and discriminate both known and unknown gases in uncontrolled conditions to aid firefighters under harsh conditions. The unit includes three metal oxide (MOX) gas sensors with CMOS micro heaters, a plasmonic enhanced non-dispersive infrared (NDIR) sensor optimized for the detection of CO2, a commercial temperature humidity sensor, and a flow sensor. We developed custom film coatings for the MOX sensors (SnO2, WO3 and NiO) which greatly improved the gas sensitivity, response time and lifetime of the miniature devices. Our proposed system exhibits promising performance for gas sensing in harsh environments, in terms of power consumption (∼ 35 mW at 350°C per MOX sensor), response time (<10 s), robustness and physical size. The sensing unit was evaluated with plumes of gases in both, a laboratory setup on a gas testing rig and on-board a mobile robot operating indoors. These high sensitivity, high-bandwidth sensors, together with online unsupervised gas discrimination algorithms, are able to detect and generate their spatial distribution maps accordingly. In the robotic experiments, the resulting gas distribution maps corresponded well to the actual location of the sources. Therefore, we verified its ability to differentiate gases and generate gas maps in real-world experiments. }, year = {2019} } @incollection{Palm1391193, author = {Palm, Rainer and Chadalavada, Ravi Teja and Lilienthal, Achim}, booktitle = {Computational Intelligence : International Joint Conference, IJCCI2016 Porto, Portugal, November 9–11,2016 Revised Selected Papers}, institution = {Örebro University, School of Science and Technology}, pages = {149--177}, title = {Fuzzy Modeling, Control and Prediction in Human-Robot Systems}, series = {Studies in Computational Intelligence}, number = {792}, DOI = {10.1007/978-3-319-99283-9}, keywords = {Fuzzy control, Fuzzy modeling, Prediction, Human-robot interaction, Human intentions, Obstacle avoidance, Velocity obstacles}, abstract = {A safe and synchronized interaction between human agents and robots in shared areas requires both long distance prediction of their motions and an appropriate control policy for short distance reaction. In this connection recognition of mutual intentions in the prediction phase is crucial to improve the performance of short distance control.We suggest an approach for short distance control inwhich the expected human movements relative to the robot are being summarized in a so-called “compass dial” from which fuzzy control rules for the robot’s reactions are derived. To predict possible collisions between robot and human at the earliest possible time, the travel times to predicted human-robot intersections are calculated and fed into a hybrid controller for collision avoidance. By applying the method of velocity obstacles, the relation between a change in robot’s motion direction and its velocity during an interaction is optimized and a combination with fuzzy expert rules is used for a safe obstacle avoidance. For a prediction of human intentions to move to certain goals pedestrian tracks are modeled by fuzzy clustering, and trajectories of human and robot agents are extrapolated to avoid collisions at intersections. Examples with both simulated and real data show the applicability of the presented methods and the high performance of the results. }, ISBN = {978-3-319-99282-2}, ISBN = {978-3-319-99283-9}, year = {2019} } @inproceedings{Hullmann1360133, author = {H{\"u}llmann, Dino and Neumann, Patrick P. and Lilienthal, Achim J.}, booktitle = {36th Danubia Adria Symposium on Advances in Experimental Mechanics : Extended Abstracts}, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, pages = {49--50}, publisher = {Danubia-Adria Symposium on Advances in Experimental Mechanics}, title = {Gas Dispersion Fluid Mechanics Simulation for Large Outdoor Environments}, keywords = {Gas dispersion simulation, CFD, gas tomography}, abstract = {The development of algorithms for mapping gas distributions and localising gas sources is a challenging task, because gas dispersion is a highly dynamic process and it is impossible to capture ground truth data. Fluid-mechanical simulations are a suitable way to support the development of these algorithms. Several tools for gas dispersion simulation have been developed, but they are not suitable for simulations of large outdoor environments. In this paper, we present a concept of how an existing simulator can be extended to handle both indoor and large outdoor scenarios. }, URL = {https://das2019.zcu.cz/DAS2019_extended_abstracts.pdf}, ISBN = {978-80-261-0876-4}, year = {2019} } @inproceedings{Palm1391173, author = {Palm, Rainer and Lilienthal, Achim J.}, booktitle = {2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) : }, institution = {Örebro University, School of Science and Technology}, pages = {1--6}, eid = {8858796}, title = {Gaussian Noise and the Intersection Problem in Human-Robot Systems : Analytical and Fuzzy Approach}, DOI = {10.1109/FUZZ-IEEE.2019.8858796}, keywords = {Humn Robot Interaction, Human Motion Prediction, Collision Avoidance}, abstract = {In this paper the intersection problem in humanrobot systems with respect to noisy information is discussed. The interaction between humans and mobile robots in shared areas requires a high level of safety especially at the intersections of trajectories. We discuss the intersection problem with respect to noisy information on the basis of an analytic geometrical model and its TS fuzzy version. The transmission of a 2-dimensional Gaussian noise signal, in particular information on human and robot orientations, through a non-linear static system and its fuzzy version, will be described. We discuss the problem: Given the parameters of the input distributions, find the parameters of the output distributions. }, ISBN = {978-1-5386-1729-8}, year = {2019} } @inproceedings{Chadalavada1391172, author = {Chadalavada, Ravi Teja and Andreasson, Henrik and Schindler, Maike and Lilienthal, Achim J.}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {Faculty of Human Sciences, University of Cologne, Germany, Cologne, Gemany}, title = {Implicit intention transference using eye-tracking glasses for improved safety in human-robot interaction}, keywords = {Human-robot interaction, intention communication, eye tracking, spatial augmented reality, electrodermal activity, stress, cognitive load.}, abstract = {Eye gaze can convey information about intentions beyond what can beinferred from the trajectory and head pose of a person. We propose eye-trackingglasses as safety equipment in industrial environments shared by humans androbots. In this work, an implicit intention transference system was developed and implemented. Robot was given access to human eye gaze data, and it responds tothe eye gaze data through spatial augmented reality projections on the sharedfloor space in real-time and the robot could also adapt its path. This allows proactivesafety approaches in HRI for example by attempting to get the human'sattention when they are in the vicinity of a moving robot. A study was conductedwith workers at an industrial warehouse. The time taken to understand the behaviorof the system was recorded. Electrodermal activity and pupil diameter wererecorded to measure the increase in stress and cognitive load while interactingwith an autonomous system, using these measurements as a proxy to quantifytrust in autonomous systems. }, year = {2019} } @article{Wiedemann1339325, author = {Wiedemann, Thomas and Shutin, Dmitriy and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {German Aerospace Center, Oberpfaffenhofen, Germany}, institution = {German Aerospace Center, Oberpfaffenhofen, Germany}, journal = {Robotics and Autonomous Systems}, pages = {66--79}, title = {Model-based gas source localization strategy for a cooperative multi-robot system-A probabilistic approach and experimental validation incorporating physical knowledge and model uncertainties}, volume = {118}, DOI = {10.1016/j.robot.2019.03.014}, keywords = {Robotic exploration, Gas source localization, Multi-agent-system, Partial differential equation, Mobile robot olfaction, Sparse Bayesian learning, Factor graph, Message passing}, abstract = {Sampling gas distributions by robotic platforms in order to find gas sources is an appealing approach to alleviate threats for a human operator. Different sampling strategies for robotic gas exploration exist. In this paper we investigate the benefit that could be obtained by incorporating physical knowledge about the gas dispersion. By exploring a gas diffusion process using a multi-robot system. The physical behavior of the diffusion process is modeled using a Partial Differential Equation (PDE) which is integrated into the exploration strategy. It is assumed that the diffusion process is driven by only a few spatial sources at unknown locations with unknown intensity. The objective of the exploration strategy is to guide the robots to informative measurement locations and by means of concentration measurements estimate the source parameters, in particular, their number, locations and magnitudes. To this end we propose a probabilistic approach towards PDE identification under sparsity constraints using factor graphs and a message passing algorithm. Moreover, message passing schemes permit efficient distributed implementation of the algorithm, which makes it suitable for a multi-robot system. We designed an experimental setup that allows us to evaluate the performance of the exploration strategy in hardware-in-the-loop experiments as well as in experiments with real ethanol gas under laboratory conditions. The results indicate that the proposed exploration approach accelerates the identification of the source parameters and outperforms systematic sampling. (C) 2019 Elsevier B.V. All rights reserved. }, year = {2019} } @article{HernandezBennetts1297002, author = {Hernandez Bennetts, Victor and Kamarudin, Kamarulzaman and Wiedemann, Thomas and Kucner, Tomasz Piotr and Somisetty, Sai Lokesh and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Center of Excellence for Advanced Sensor Technology, School of Mechatronics Engineering, Universiti Malaysia Perlis, Arau Perlis, Malaysia}, institution = {Institute of Communications and Navigation, German Aerospace Center, Oberpfaffenhofen, Germany}, institution = {Department of Mechatronics, Sastra University, Thanjavur, India}, journal = {Sensors}, number = {5}, eid = {E1119}, title = {Multi-Domain Airflow Modeling and Ventilation Characterization Using Mobile Robots, Stationary Sensors and Machine Learning}, volume = {19}, DOI = {10.3390/s19051119}, keywords = {Airflow modeling, environmental monitoring, machine learning, mobile robotics, static sensor networks, ventilation}, abstract = {Ventilation systems are critically important components of many public buildings and workspaces. Proper ventilation is often crucial for preventing accidents, such as explosions in mines and avoiding health issues, for example, through long-term exposure to harmful respirable matter. Validation and maintenance of ventilation systems is thus of key interest for plant operators and authorities. However, methods for ventilation characterization, which allow us to monitor whether the ventilation system in place works as desired, hardly exist. This article addresses the critical challenge of ventilation characterization-measuring and modelling air flow at micro-scales-that is, creating a high-resolution model of wind speed and direction from airflow measurements. Models of the near-surface micro-scale flow fields are not only useful for ventilation characterization, but they also provide critical information for planning energy-efficient paths for aerial robots and many applications in mobile robot olfaction. In this article we propose a heterogeneous measurement system composed of static, continuously sampling sensing nodes, complemented by localized measurements, collected during occasional sensing missions with a mobile robot. We introduce a novel, data-driven, multi-domain airflow modelling algorithm that estimates (1) fields of posterior distributions over wind direction and speed ("ventilation maps", spatial domain); (2) sets of ventilation calendars that capture the evolution of important airflow characteristics at measurement positions (temporal domain); and (3) a frequency domain analysis that can reveal periodic changes of airflow in the environment. The ventilation map and the ventilation calendars make use of an improved estimation pipeline that incorporates a wind sensor model and a transition model to better filter out sporadic, noisy airflow changes. These sudden changes may originate from turbulence or irregular activity in the surveyed environment and can, therefore, disturb modelling of the relevant airflow patterns. We tested the proposed multi-domain airflow modelling approach with simulated data and with experiments in a semi-controlled environment and present results that verify the accuracy of our approach and its sensitivity to different turbulence levels and other disturbances. Finally, we deployed the proposed system in two different real-world industrial environments (foundry halls) with different ventilation regimes for three weeks during full operation. Since airflow ground truth cannot be obtained, we present a qualitative discussion of the generated airflow models with plant operators, who concluded that the computed models accurately depicted the expected airflow patterns and are useful to understand how pollutants spread in the work environment. This analysis may then provide the basis for decisions about corrective actions to avoid long-term exposure of workers to harmful respirable matter. }, year = {2019} } @inproceedings{Hoang1374210, author = {Hoang, Dinh-Cuong and Stoyanov, Todor and Lilienthal, Achim J.}, booktitle = {2019 European Conference on Mobile Robots, ECMR 2019 : Proceedings}, institution = {Örebro University, School of Science and Technology}, eid = {152970}, title = {Object-RPE : Dense 3D Reconstruction and Pose Estimation with Convolutional Neural Networks for Warehouse Robots}, DOI = {10.1109/ECMR.2019.8870927}, abstract = {We present a system for accurate 3D instance-aware semantic reconstruction and 6D pose estimation, using an RGB-D camera. Our framework couples convolutional neural networks (CNNs) and a state-of-the-art dense Simultaneous Localisation and Mapping (SLAM) system, ElasticFusion, to achieve both high-quality semantic reconstruction as well as robust 6D pose estimation for relevant objects. The method presented in this paper extends a high-quality instance-aware semantic 3D Mapping system from previous work [1] by adding a 6D object pose estimator. While the main trend in CNN-based 6D pose estimation has been to infer object's position and orientation from single views of the scene, our approach explores performing pose estimation from multiple viewpoints, under the conjecture that combining multiple predictions can improve the robustness of an object detection system. The resulting system is capable of producing high-quality object-aware semantic reconstructions of room-sized environments, as well as accurately detecting objects and their 6D poses. The developed method has been verified through experimental validation on the YCB-Video dataset and a newly collected warehouse object dataset. Experimental results confirmed that the proposed system achieves improvements over state-of-the-art methods in terms of surface reconstruction and object pose prediction. Our code and video are available at https://sites.google.com/view/object-rpe. }, ISBN = {978-1-7281-3605-9}, year = {2019} } @article{Schindler1391196, author = {Schindler, Maike and Bader, Eveline and Lilienthal, Achim J. and Schindler, Florian and Schabmann, Alfred}, institution = {Örebro University, School of Science and Technology}, institution = {Department of Special Education, University of Cologne, Cologne, Germany}, institution = {Department of Special Education, University of Cologne, Cologne, Germany}, institution = {TU Dortmund University, Dortmund, Germany}, institution = {Department of Special Education, University of Cologne, Cologne, Germany}, journal = {Learning Disabilities: A Contemporary Journal}, number = {1}, pages = {5--28}, publisher = {Learning Disabilities Worldwide}, title = {Quantity Recognition in Structured Whole Number Representations of Students with Mathematical Difficulties : An Eye-Tracking Study}, volume = {17}, keywords = {Mathematical Difficulties, Structured Whole Number Representations, Quantity Recognition, Abacus, Dot-Field, Eye Tracking}, abstract = {Quantity recognition in whole number representations is a fundamental skill children need to acquire in their mathematical development. Despite the observed correlation to mathematics achievement, however, the abil-ity to recognize quantities in structured whole number representations has not been studied extensively. In this article, we investigate how stu-dents with mathematical difficulties (MD) differ from typically develop-ing (TD) students in quantity recognition in structured whole number representations. We use eye tracking (ET), which can help to identify stu-dents’ quantity recognition strategies. In contrast to methods that include collecting verbal answers and reports, ET avoids an additional verbal-ization step, which may be affected by poor language skills and by low meta-cognitive abilities or memory issues when monitoring, recalling,and explaining one’s thoughts. We present an ET study with 20 students of which ten were found to have MD in initial tests (using qualitative and quantitative diagnostics). We used ET glasses, which allow seeing the students’ view of the scene with an augmented visualization of the gaze point projected onto the scene. The obtained gaze-overlaid videos also include audio data and records of students’ answers and utterances. In our study, we did not find significant differences between the error rates of MD and TD students. Response times, however, were longer for students with MD. The analysis of the ET data brought students’ quantity recogni-tion strategies to light, some of which were not found in previous research. Our analyses revealed differences in the use of these quantity recognition strategies between MD and TD students. Our study illustrates the power of ET for investigating students’ quantity recognition strategies and the potential of ET to support MD students. }, year = {2019} } @inproceedings{Fan1360454, author = {Fan, Han and Hernandez Bennetts, Victor and Schaffernicht, Erik and Lilienthal, Achim J.}, booktitle = {18th ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) : }, institution = {Örebro University, School of Science and Technology}, eid = {151773}, title = {Semi-supervised Gas Detection Using an Ensemble of One-class Classifiers}, DOI = {10.1109/ISOEN.2019.8823148}, keywords = {Metal oxide semiconductor sensor, electronic nose, gas detection, gas sensing, open sampling systems}, abstract = {Detecting chemical compounds using electronic noses is important in many gas sensing related applications. Existing gas detection methods typically use prior knowledge of the target analytes. However, in some scenarios, the analytes to be detected are not fully known in advance, and preparing a dedicated model is not possible. To address this issue, we propose a gas detection approach using an ensemble of one-class classifiers. The proposed approach is initialized by learning a Mahalanobis-based and a Gaussian based model using clean air only. During the sampling process, the presence of chemicals is detected by the initialized system, which allows to learn a one-class nearest neighbourhood model without supervision. From then on the gas detection considers the predictions of the three one-class models. The proposed approach is validated with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an open environment. }, year = {2019} } @article{Burgues1284132, author = {Burgu{\’e;}s, Javier and Hernandez Bennetts, Victor and Lilienthal, Achim J. and Marco, Santiago}, institution = {Örebro University, School of Science and Technology}, institution = {Institute for Bioengineering of Catalonia (IBEC),The Barcelona Institute of Science and Technology, Baldiri Reixac, Barcelona, Spain; Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Barcelona, Spain}, institution = {Institute for Bioengineering of Catalonia (IBEC),The Barcelona Institute of Science and Technology, Baldiri Reixac, Barcelona, Spain; Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Barcelona, Spain}, journal = {Sensors}, note = {Funding Agency:Spanish MINECO  BES-2015-071698  TEC2014-59229-R}, number = {3}, eid = {478}, title = {Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping}, volume = {19}, DOI = {10.3390/s19030478}, keywords = {Robotics, signal processing, electronics, gas source localization, gas distribution mapping; gas sensors, drone, UAV, MOX sensor, quadcopter}, abstract = {This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up to two in situ metal oxide semiconductor (MOX) gas sensors. Due to its small form-factor, the SNAV is not a hazard for humans, enabling its use in public areas or inside buildings. It can autonomously carry out gas sensing missions of hazardous environments inaccessible to terrestrial robots and bigger drones, for example searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion. The first contribution of this work is assessing the impact of the nano-propellers on the MOX sensor signals at different distances to a gas source. A second contribution is adapting the ‘bout’ detection algorithm, proposed by Schmuker et al. (2016) to extract specific features from the derivative of the MOX sensor response, for real-time operation. The third and main contribution is the experimental validation of the SNAV for gas source localization (GSL) and mapping in a large indoor environment (160 m2) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. Two GSL strategies are compared, one based on the instantaneous gas sensor response and the other one based on the bout frequency. From the measurements collected (in motion) along a predefined sweeping path we built (in less than 3 min) a 3D map of the gas distribution and identified the most likely source location. Using the bout frequency yielded on average a higher localization accuracy than using the instantaneous gas sensor response (1.38 m versus 2.05 m error), however accurate tuning of an additional parameter (the noise threshold) is required in the former case. The main conclusion of this paper is that a nano-drone has the potential to perform gas sensing tasks in complex environments. }, year = {2019} } @article{Mielle1342185, author = {Mielle, Malcolm and Magnusson, Martin and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Robotics}, note = {Funding Agency:EU  ICT-26-2016 732737  ICT-23-2014 645101}, number = {2}, eid = {40}, publisher = {MDPI}, title = {The Auto-Complete Graph : Merging and Mutual Correction of Sensor and Prior Maps for SLAM}, volume = {8}, DOI = {10.3390/robotics8020040}, keywords = {SLAM, prior map, emergency map, layout map, graph-based SLAM, navigation, search and rescue}, abstract = {Simultaneous Localization And Mapping (SLAM) usually assumes the robot starts without knowledge of the environment. While prior information, such as emergency maps or layout maps, is often available, integration is not trivial since such maps are often out of date and have uncertainty in local scale. Integration of prior map information is further complicated by sensor noise, drift in the measurements, and incorrect scan registrations in the sensor map. We present the Auto-Complete Graph (ACG), a graph-based SLAM method merging elements of sensor and prior maps into one consistent representation. After optimizing the ACG, the sensor map's errors are corrected thanks to the prior map, while the sensor map corrects the local scale inaccuracies in the prior map. We provide three datasets with associated prior maps: two recorded in campus environments, and one from a fireman training facility. Our method handled up to 40% of noise in odometry, was robust to varying levels of details between the prior and the sensor map, and could correct local scale errors of the prior. In field tests with ACG, users indicated points of interest directly on the prior before exploration. We did not record failures in reaching them. }, year = {2019} } @inproceedings{Vintr1391186, author = {Vintr, Tomas and Molina, Sergi and Senanayake, Ransalu and Broughton, George and Yan, Zhi and Ulrich, Jiri and Kucner, Tomasz P. and Swaminathan, Chittaranjan Srinivas and Majer, Filip and Stachova, Maria and Lilienthal, Achim J. and Krajnik, Tomas}, booktitle = {2019 European Conference on Mobile Robots (ECMR) : }, institution = {Örebro University, School of Science and Technology}, institution = {Artificial Intelligence Center, Czech Technical University}, institution = {Lincoln Centre for Autonomous Systems (L-CAS), University of Lincoln}, institution = {Stanford University}, institution = {Artificial Intelligence Center, Czech Technical University}, institution = {Distributed Artificial Intelligence and Knowledge Laboratory (CIAD), University of Technology of Belfort-Montbeliard (UTBM), France}, institution = {Artificial Intelligence Center, Czech Technical University}, institution = {Artificial Intelligence Center, Czech Technical University}, institution = {University of Matej Bel, Banska Bystrica, Slovakia}, institution = {Artificial Intelligence Center, Czech Technical University}, note = {Funding Agencies:CSF project  17-27006Y STRoLLCTU IGA grant  SGS16/235/OHK3/3T/13 FR-8J18FR018PHC Barrande programme  40682ZH (3L4AV)CZ grant  CZ.02.1.01/0.0/0.0/16 019/0000765}, eid = {8870909}, title = {Time-varying Pedestrian Flow Models for Service Robots}, DOI = {10.1109/ECMR.2019.8870909}, keywords = {Long-Term Operation, Flow Models, Spatio-Temporal Models, Human Motion Prediction}, abstract = {We present a human-centric spatio-temporal model for service robots operating in densely populated environments for long time periods. The method integrates observations of pedestrians performed by a mobile robot at different locations and times into a memory efficient model, that represents the spatial layout of natural pedestrian flows and how they change over time. To represent temporal variations of the observed flows, our method does not model the time in a linear fashion, but by several dimensions wrapped into themselves. This representation of time can capture long-term (i.e. days to weeks) periodic patterns of peoples’ routines and habits. Knowledge of these patterns allows making long-term predictions of future human presence and walking directions, which can support mobile robot navigation in human-populated environments. Using datasets gathered by a robot for several weeks, we compare the model to state-of-the-art methods for pedestrian flow modelling. }, ISBN = {978-1-7281-3605-9}, year = {2019} } @article{Fan1287969, author = {Fan, Han and Hernandez Bennetts, Victor and Schaffernicht, Erik and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Sensors}, note = {Funding Agency:European Commission  645101}, number = {3}, eid = {E685}, title = {Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose}, volume = {19}, DOI = {10.3390/s19030685}, keywords = {Emergency response, gas discrimination, gas distribution mapping, mobile robotics olfaction, search and rescue robot, unsupervised learning}, abstract = {Emergency personnel, such as firefighters, bomb technicians, and urban search and rescue specialists, can be exposed to a variety of extreme hazards during the response to natural and human-made disasters. In many of these scenarios, a risk factor is the presence of hazardous airborne chemicals. The recent and rapid advances in robotics and sensor technologies allow emergency responders to deal with such hazards from relatively safe distances. Mobile robots with gas-sensing capabilities allow to convey useful information such as the possible source positions of different chemicals in the emergency area. However, common gas sampling procedures for laboratory use are not applicable due to the complexity of the environment and the need for fast deployment and analysis. In addition, conventional gas identification approaches, based on supervised learning, cannot handle situations when the number and identities of the present chemicals are unknown. For the purpose of emergency response, all the information concluded from the gas detection events during the robot exploration should be delivered in real time. To address these challenges, we developed an online gas-sensing system using an electronic nose. Our system can automatically perform unsupervised learning and update the discrimination model as the robot is exploring a given environment. The online gas discrimination results are further integrated with geometrical information to derive a multi-compound gas spatial distribution map. The proposed system is deployed on a robot built to operate in harsh environments for supporting fire brigades, and is validated in several different real-world experiments of discriminating and mapping multiple chemical compounds in an indoor open environment. Our results show that the proposed system achieves high accuracy in gas discrimination in an online, unsupervised, and computationally efficient manner. The subsequently created gas distribution maps accurately indicate the presence of different chemicals in the environment, which is of practical significance for emergency response. }, year = {2019} } @inproceedings{Palm1391189, author = {Palm, Rainer and Lilienthal, Achim J.}, booktitle = {Proceedings of the 11th International Joint Conference on Computational Intelligence : Volume 1 (FCTA)}, institution = {Örebro University, School of Science and Technology}, note = {Funding Agency:AIR-project, Action and Intention Recognition in Human Interaction with Autonomous Systems}, pages = {296--305}, title = {Uncertainty and Fuzzy Modeling in Human-Robot Navigation}, DOI = {10.5220/0008344902960305}, keywords = {Human-robot Interaction, Navigation, Fuzzy Modeling, Gaussian Noise}, abstract = {The interaction between humans and mobile robots in shared areas requires a high level of safety especially at the crossings of the trajectories of humans and robots. We discuss the intersection calculation and its fuzzy version in the context of human-robot navigation with respect to noise information. Based on known parameters of the Gaussian input distributions at the orientations of human and robot the parameters of the output distributions at the intersection are to be found by analytical and fuzzy calculation. Furthermore the inverse task is discussed where the parameters of the output distributions are given and the parameters of the input distributions are searched. For larger standard deviations of the orientation signals we suggest mixed Gaussian models as approximation of nonlinear distributions. }, ISBN = {978-989-758-384-1}, year = {2019} } @article{Mielle1342184, author = {Mielle, Malcolm and Magnusson, Martin and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Robotics}, note = {Funding Agency:EU  ICT-26-2016 732737}, number = {2}, eid = {43}, title = {URSIM : Unique Regions for Sketch Map Interpretation and Matching}, volume = {8}, DOI = {10.3390/robotics8020043}, keywords = {Map matching, sketch, human-robot interaction, interface, graph matching, segmentation}, abstract = {We present a method for matching sketch maps to a corresponding metric map, with the aim of later using the sketch as an intuitive interface for human-robot interactions. While sketch maps are not metrically accurate and many details, which are deemed unnecessary, are omitted, they represent the topology of the environment well and are typically accurate at key locations. Thus, for sketch map interpretation and matching, one cannot only rely on metric information. Our matching method first finds the most distinguishable, or unique, regions of two maps. The topology of the maps, the positions of the unique regions, and the size of all regions are used to build region descriptors. Finally, a sequential graph matching algorithm uses the region descriptors to find correspondences between regions of the sketch and metric maps. Our method obtained higher accuracy than both a state-of-the-art matching method for inaccurate map matching, and our previous work on the subject. The state of the art was unable to match sketch maps while our method performed only 10% worse than a human expert. }, year = {2019} } @inproceedings{Fan1284105, author = {Fan, Hongqi and Lu, Dawei and Kucner, Tomasz Piotr and Magnusson, Martin and Lilienthal, Achim}, booktitle = {Proceedings of 21st International Conference on Information Fusion (FUSION) : }, institution = {Örebro University, School of Science and Technology}, institution = {National University of Defense Technology, Changsa, P. R. China}, institution = {National University of Defense Technology, Changsa, P. R. China}, pages = {2400--2406}, title = {2D Spatial Keystone Transform for Sub-Pixel Motion Extraction from Noisy Occupancy Grid Map}, DOI = {10.23919/ICIF.2018.8455274}, keywords = {robotics, occupancy grid map, motion extraction, keystone transform, 2DS-KST, sub-pixel}, abstract = {In this paper, we propose a novel sub-pixel motion extraction method, called as Two Dimensional Spatial Keystone Transform (2DS-KST), for the motion detection and estimation from successive noisy Occupancy Grid Maps (OGMs). It extends the KST in radar imaging or motion compensation to 2D real spatial case, based on multiple hypotheses about possible directions of moving obstacles. Simulation results show that 2DS-KST has a good performance on the extraction of sub-pixel motions in very noisy environment, especially for those slowly moving obstacles. }, ISBN = {978-0-9964527-6-2}, ISBN = {978-1-5386-4330-3}, year = {2018} } @article{Burgues1284131, author = {Burgu{\’e;}s, Javier and Hernandez Bennetts, Victor and Lilienthal, Achim and Marco, Santiago}, institution = {Örebro University, School of Science and Technology}, institution = {Department of Electronic and Biomedical Engineering, Universitat de Barcelona, Barcelona, Spain; Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain}, institution = {Department of Electronic and Biomedical Engineering, Universitat de Barcelona, Barcelona, Spain; Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain}, journal = {Proceedings}, number = {13}, eid = {911}, title = {3D Gas Distribution with and without Artificial Airflow : An Experimental Study with a Grid of Metal Oxide Semiconductor Gas Sensors}, volume = {2}, DOI = {10.3390/proceedings2130911}, keywords = {MOX, metal oxide, flow visualization, gas sensors, gas distribution mapping, sensor grid, 3D, gas source localization, indoor}, abstract = {Gas distribution modelling can provide potentially life-saving information when assessing the hazards of gaseous emissions and for localization of explosives, toxic or flammable chemicals. In this work, we deployed a three-dimensional (3D) grid of metal oxide semiconductor (MOX) gas sensors deployed in an office room, which allows for novel insights about the complex patterns of indoor gas dispersal. 12 independent experiments were carried out to better understand dispersion patters of a single gas source placed at different locations of the room, including variations in height, release rate and air flow profiles. This dataset is denser and richer than what is currently available, i.e., 2D datasets in wind tunnels. We make it publicly available to enable the community to develop, validate, and compare new approaches related to gas sensing in complex environments. }, year = {2018} } @article{Fan1167983, author = {Fan, Han and Hernandez Bennetts, Victor and Schaffernicht, Erik and Lilienthal, Achim}, institution = {Örebro University, School of Science and Technology}, journal = {Sensors and actuators. B, Chemical}, pages = {183--203}, title = {A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments}, volume = {259}, DOI = {10.1016/j.snb.2017.10.063}, keywords = {Gas discrimination, environmental monitoring, metal oxide sensors, cluster analysis, unsupervised learning}, abstract = {Gas discrimination in open and uncontrolled environments based on smart low-cost electro-chemical sensor arrays (e-noses) is of great interest in several applications, such as exploration of hazardous areas, environmental monitoring, and industrial surveillance. Gas discrimination for e-noses is usually based on supervised pattern recognition techniques. However, the difficulty and high cost of obtaining extensive and representative labeled training data limits the applicability of supervised learning. Thus, to deal with the lack of information regarding target substances and unknown interferents, unsupervised gas discrimination is an advantageous solution. In this work, we present a cluster-based approach that can infer the number of different chemical compounds, and provide a probabilistic representation of the class labels for the acquired measurements in a given environment. Our approach is validated with the samples collected in indoor and outdoor environments using a mobile robot equipped with an array of commercial metal oxide sensors. Additional validation is carried out using a multi-compound data set collected with stationary sensor arrays inside a wind tunnel under various airflow conditions. The results show that accurate class separation can be achieved with a low sensitivity to the selection of the only free parameter, namely the neighborhood size, which is used for density estimation in the clustering process. }, year = {2018} } @article{Fan1172125, author = {Fan, Hongqi and Kucner, Tomasz Piotr and Magnusson, Martin and Li, Tiancheng and Lilienthal, Achim}, institution = {Örebro University, School of Science and Technology}, institution = {National Laboratory of Science and Technology on Automatic Target Recognition, National University of Defense Technology, Changsha, China}, institution = {School of Sciences, University of Salamanca, Salamanca, Spain}, journal = {IEEE transactions on intelligent transportation systems (Print)}, note = {Funding Agencies:EU Project SPENCER  600877 Marie Sklodowska-Curie Individual Fellowship  709267 National Twelfth Five-Year Plan for Science and Technology Support of China  2014BAK12B03 }, number = {9}, pages = {2977--2993}, title = {A Dual PHD Filter for Effective Occupancy Filtering in a Highly Dynamic Environment}, volume = {19}, DOI = {10.1109/TITS.2017.2770152}, keywords = {Mobile robot, occupancy filtering, PHD filter, BOF, particle filter, random finite set}, abstract = {Environment monitoring remains a major challenge for mobile robots, especially in densely cluttered or highly populated dynamic environments, where uncertainties originated from environment and sensor significantly challenge the robot's perception. This paper proposes an effective occupancy filtering method called the dual probability hypothesis density (DPHD) filter, which models uncertain phenomena, such as births, deaths, occlusions, false alarms, and miss detections, by using random finite sets. The key insight of our method lies in the connection of the idea of dynamic occupancy with the concepts of the phase space density in gas kinetic and the PHD in multiple target tracking. By modeling the environment as a mixture of static and dynamic parts, the DPHD filter separates the dynamic part from the static one with a unified filtering process, but has a higher computational efficiency than existing Bayesian Occupancy Filters (BOFs). Moreover, an adaptive newborn function and a detection model considering occlusions are proposed to improve the filtering efficiency further. Finally, a hybrid particle implementation of the DPHD filter is proposed, which uses a box particle filter with constant discrete states and an ordinary particle filter with a time-varying number of particles in a continuous state space to process the static part and the dynamic part, respectively. This filter has a linear complexity with respect to the number of grid cells occupied by dynamic obstacles. Real-world experiments on data collected by a lidar at a busy roundabout demonstrate that our approach can handle monitoring of a highly dynamic environment in real time. }, year = {2018} } @inproceedings{Mielle1237531, author = {Mielle, Malcolm and Magnusson, Martin and Lilienthal, Achim J.}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, pages = {4993--4999}, title = {A method to segment maps from different modalities using free space layout MAORIS : map of ripples segmentation}, DOI = {10.1109/ICRA.2018.8461128}, keywords = {map segmentation, free space, layout}, abstract = {How to divide floor plans or navigation maps into semantic representations, such as rooms and corridors, is an important research question in fields such as human-robot interaction, place categorization, or semantic mapping. While most works focus on segmenting robot built maps, those are not the only types of map a robot, or its user, can use. We present a method for segmenting maps from different modalities, focusing on robot built maps and hand-drawn sketch maps, and show better results than state of the art for both types. Our method segments the map by doing a convolution between the distance image of the map and a circular kernel, and grouping pixels of the same value. Segmentation is done by detecting ripple-like patterns where pixel values vary quickly, and merging neighboring regions with similar values. We identify a flaw in the segmentation evaluation metric used in recent works and propose a metric based on Matthews correlation coefficient (MCC). We compare our results to ground-truth segmentations of maps from a publicly available dataset, on which we obtain a better MCC than the state of the art with 0.98 compared to 0.65 for a recent Voronoi-based segmentation method and 0.70 for the DuDe segmentation method. We also provide a dataset of sketches of an indoor environment, with two possible sets of ground truth segmentations, on which our method obtains an MCC of 0.56 against 0.28 for the Voronoi-based segmentation method and 0.30 for DuDe. }, year = {2018} } @inproceedings{Canelhas1232362, author = {Canelhas, Daniel Ricão and Stoyanov, Todor and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), : }, institution = {Örebro University, School of Science and Technology}, institution = {Univrses AB, Strängnäs, Sweden}, pages = {6337--6343}, title = {A Survey of Voxel Interpolation Methods and an Evaluation of Their Impact on Volumetric Map-Based Visual Odometry}, keywords = {Voxels, Compression, Interpolation, TSDF, Visual Odometry}, abstract = {Voxel volumes are simple to implement and lend themselves to many of the tools and algorithms available for 2D images. However, the additional dimension of voxels may be costly to manage in memory when mapping large spaces at high resolutions. While lowering the resolution and using interpolation is common work-around, in the literature we often find that authors either use trilinear interpolation or nearest neighbors and rarely any of the intermediate options. This paper presents a survey of geometric interpolation methods for voxel-based map representations. In particular we study the truncated signed distance field (TSDF) and the impact of using fewer than 8 samples to perform interpolation within a depth-camera pose tracking and mapping scenario. We find that lowering the number of samples fetched to perform the interpolation results in performance similar to the commonly used trilinear interpolation method, but leads to higher framerates. We also report that lower bit-depth generally leads to performance degradation, though not as much as may be expected, with voxels containing as few as 3 bits sometimes resulting in adequate estimation of camera trajectories. }, year = {2018} } @inproceedings{Chadalavada1270176, author = {Chadalavada, Ravi Teja and Andreasson, Henrik and Schindler, Maike and Palm, Rainer and Lilienthal, Achim}, booktitle = {Advances in Manufacturing Technology XXXII : Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden}, institution = {Örebro University, School of Science and Technology}, pages = {253--258}, title = {Accessing your navigation plans! Human-Robot Intention Transfer using Eye-Tracking Glasses}, series = {Advances in Transdisciplinary Engineering}, number = {8}, DOI = {10.3233/978-1-61499-902-7-253}, keywords = {Human-Robot Interaction (HRI), Eye-tracking, Eye-Tracking Glasses, Navigation Intent, Implicit Intention Transference, Obstacle avoidance.}, abstract = {Robots in human co-habited environments need human-aware task and motion planning, ideally responding to people’s motion intentions as soon as they can be inferred from human cues. Eye gaze can convey information about intentions beyond trajectory and head pose of a person. Hence, we propose eye-tracking glasses as safety equipment in industrial environments shared by humans and robots. This paper investigates the possibility of human-to-robot implicit intention transference solely from eye gaze data.  We present experiments in which humans wearing eye-tracking glasses encountered a small forklift truck under various conditions. We evaluate how the observed eye gaze patterns of the participants related to their navigation decisions. Our analysis shows that people primarily gazed on that side of the robot they ultimately decided to pass by. We discuss implications of these results and relate to a control approach that uses human eye gaze for early obstacle avoidance. }, ISBN = {978-1-61499-901-0}, ISBN = {978-1-61499-902-7}, year = {2018} } @inproceedings{Swaminathan1262737, author = {Swaminathan, Chittaranjan Srinivas and Kucner, Tomasz Piotr and Magnusson, Martin and Palmieri, Luigi and Lilienthal, Achim}, booktitle = {2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, institution = {Robert Bosch, GmbH Corporate Research, Germany}, pages = {7403--7409}, title = {Down the CLiFF : Flow-Aware Trajectory Planning under Motion Pattern Uncertainty}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2018.8593905}, keywords = {Trajectory, Robots, Planning, Cost function, Uncertainty, Veichle dynamics, Aerospace electronics}, abstract = {In this paper we address the problem of flow-aware trajectory planning in dynamic environments considering flow model uncertainty. Flow-aware planning aims to plan trajectories that adhere to existing flow motion patterns in the environment, with the goal to make robots more efficient, less intrusive and safer. We use a statistical model called CLiFF-map that can map flow patterns for both continuous media and discrete objects. We propose novel cost and biasing functions for an RRT* planning algorithm, which exploits all the information available in the CLiFF-map model, including uncertainties due to flow variability or partial observability. Qualitatively, a benefit of our approach is that it can also be tuned to yield trajectories with different qualities such as exploratory or cautious, depending on application requirements. Quantitatively, we demonstrate that our approach produces more flow-compliant trajectories, compared to two baselines. }, ISBN = {978-1-5386-8094-0}, ISBN = {978-1-5386-8095-7}, year = {2018} } @inproceedings{Lindner1284093, author = {Lindner, Helen Y and Lilienthal, Achim and Karlsson, Gunilla and Lundqvist, Lars-Olov}, booktitle = { : }, institution = {Örebro University, School of Health Sciences}, institution = {Örebro University, School of Science and Technology}, institution = {Adult rehabilitation centre, Region Örebro County, Örebro, Sweden}, institution = {University Health Care Research Centre}, title = {Eye gaze technology to gain access to cognitive processes in individuals with profound intellectual and physical disabilities (PIPD)}, keywords = {Eye-tracking, profound intellectual and physical disabilities}, abstract = {Objective: Individuals with profound intellectual and physical disabilities (PIPD) often cannot speak for themselves and do things for themselves. Their level of cognitive abilities is unclear. Eye gaze technology has the potential to gain access to cognitive processes and eventually enable communication among these individuals. Method: Six individuals with PIPD were given multiple sessions of eye gaze training (9-36 sessions) between February 17 to October 18. They used a screen eye-tracker (Tobii pc eye-mini) to control the objects/icons on the screen. An eye-gaze training program with different levels of activities was used to teach cause and effect, give appropriate response, explore the whole screen, target specific objects, choosing objects AND turn taking. }, year = {2018} } @inproceedings{Schindler1284102, author = {Schindler, Maike and Lilienthal, Achim}, booktitle = {Proceedings of Annual Meeting of the International Group for the Psychology of Mathematics Education (PME-42) : }, institution = {Örebro University, School of Science and Technology}, institution = {University of Cologne, Department of Special Education}, pages = {115--122}, publisher = {PME}, title = {Eye-Tracking For Studying Mathematical Difficulties : Also In Inclusive Settings}, volume = {4}, keywords = {Eye-tracking, Mathematical Difficulties}, abstract = {Eye-Tracking (ET) is a promising tool for mathematics education research. Interest is fue­led by recent theoretical and technical developments, and the potential to identify strategies students use in mathematical tasks. This makes ET in­teresting for studying students with mathematical difficulties (MD), also with a view on inclusive settings. We present a systematic analysis of the opportunities ET may hold for understanding strategies of students with MD. Based on an empirical study with 20 fifth graders (10 with MD), we illustrate that and why ET offers opportunities especially for students with MD and describe main advantages. We also identify limitations of think aloud protocols, using ET as validation method, and present characteristics of students’ strategies in tasks on quantity recognition in structured whole number representations. }, year = {2018} } @inproceedings{Palm1234103, author = {Palm, Rainer and Lilienthal, Achim}, booktitle = {WCCI 2018 : 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)}, institution = {Örebro University, School of Science and Technology}, pages = {827--834}, title = {Fuzzy logic and control in Human-Robot Systems : geometrical and kinematic considerations}, keywords = {Human-robot interaction, fuzzy control, obstacle avoidance, eye tracking}, abstract = {The interaction between humans and mobile robots in shared areas requires adequate control for both humans and robots.The online path planning of the robot depending on the estimated or intended movement of the person is crucial for the obstacle avoidance and close cooperation between them. The velocity obstacles method and its fuzzification optimizes the relationship between the velocities of a robot and a human agent during the interaction. In order to find the estimated intersection between robot and human in the case of positions/orientations disturbed by noise, analytical and fuzzified versions are presented. The orientation of a person is estimated by eye tracking, with the help of which the intersection area is calculated. Eye tracking leads to clusters of fixations that are condensed into cluster centers by fuzzy-time clustering to detect the intention and attention of humans. }, ISBN = {978-1-5090-6020-7}, year = {2018} } @inproceedings{Neumann1279608, author = {Neumann, Patrick P. and H{\"u}llmann, Dino and Krentel, Daniel and Kluge, Martin and Kohlhoff, Harald and Lilienthal, Achim}, booktitle = {Proceedings of the IEEE Sensors 2018 : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, note = {Funding Agency:German Federal Ministry for Economic Affairs and Energy (BMWi) within the ZIM program  KF2201091HM4}, title = {Gas Tomography Up In The Air!}, series = {Proceedings of IEEE Sensors}, DOI = {10.1109/ICSENS.2018.8630293}, keywords = {Aerial robot, gas tomography, plume, TDLAS}, abstract = {In this paper, we present an autonomous aerial robot to reconstruct tomographic 2D slices of gas plumes in outdoor environments. Our platform, the so-called Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS) combines a lightweight Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor with a 3-axis aerial stabilization gimbal for aiming on a versatile octocopter. The TDLAS sensor provides integral gas concentration measurements but no information regarding the distance traveled by the laser diode's beam or the distribution of the gas along the optical path. We complemented the set-up with a laser rangefinder and apply principles of Computed Tomography (CT) to create a model of the spatial gas distribution from these integral concentration measurements. To allow for a rudimentary ground truth evaluation of the applied gas tomography algorithm, we set up a unique outdoor test environment based on two 3D ultrasonic anemometers and a distributed array of 10 infrared gas transmitters. We present first results showing the 2D plume reconstruction capabilities of the system under realistic conditions. }, ISBN = {978-1-5386-4707-3}, year = {2018} } @inproceedings{Rudenko1284106, author = {Rudenko, Andrey and Palmieri, Luigi and Lilienthal, Achim and Arras, Kai O.}, booktitle = {2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, institution = {Bosch Corporate Research, Stuttgart, Germany}, institution = {Bosch Corporate Research, Stuttgart, Germany}, institution = {Bosch Corporate Research, Stuttgart, Germany}, pages = {3358--3364}, title = {Human Motion Prediction under Social Grouping Constraints}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2018.8594258}, keywords = {Human motion prediction, human robot interaction, social forces, human-aware planning}, abstract = {Accurate long-term prediction of human motion inpopulated spaces is an important but difficult task for mobile robots and intelligent vehicles. What makes this task challenging is that human motion is influenced by a large variety offactors including the person’s intention, the presence, attributes, actions, social relations and social norms of other surrounding agents, and the geometry and semantics of the environment. In this paper, we consider the problem of computing human motion predictions that account for such factors. We formulate the task as an MDP planning problem with stochastic policies and propose a weighted random walk algorithm in which each agent is locally influenced by social forces from other nearby agents. The novelty of this paper is that we incorporate social grouping information into the prediction process reflecting the soft formation constraints that groups typically impose to their members’ motion. We show that our method makes more accurate predictions than three state-of-the-art methods in terms of probabilistic and geometrical performance metrics. }, ISBN = {978-1-5386-8094-0}, ISBN = {978-1-5386-8095-7}, year = {2018} } @article{Almqvist1163065, author = {Almqvist, H{\aa}kan and Magnusson, Martin and Kucner, Tomasz Piotr and Lilienthal, Achim}, institution = {Örebro University, School of Science and Technology}, journal = {Journal of Field Robotics}, number = {5}, pages = {662--677}, title = {Learning to detect misaligned point clouds}, volume = {35}, DOI = {10.1002/rob.21768}, keywords = {perception, mapping, position estimation}, abstract = {Matching and merging overlapping point clouds is a common procedure in many applications, including mobile robotics, three-dimensional mapping, and object visualization. However, fully automatic point-cloud matching, without manual verification, is still not possible because no matching algorithms exist today that can provide any certain methods for detecting misaligned point clouds. In this article, we make a comparative evaluation of geometric consistency methods for classifying aligned and nonaligned point-cloud pairs. We also propose a method that combines the results of the evaluated methods to further improve the classification of the point clouds. We compare a range of methods on two data sets from different environments related to mobile robotics and mapping. The results show that methods based on a Normal Distributions Transform representation of the point clouds perform best under the circumstances presented herein. }, year = {2018} } @article{Hullmann1279642, author = {H{\"u}llmann, Dino and Paul, Niels and Kohlhoff, Harald and Neumann, Patrick P. and Lilienthal, Achim}, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, journal = {Materials Today: Proceedings}, number = {13}, pages = {26703--26708}, title = {Measuring rotor speed for wind vector estimation on multirotor aircraft}, volume = {5}, DOI = {10.1016/j.matpr.2018.08.139}, keywords = {Rotor speed, tachometer, UAV, wind vector estimation}, abstract = {For several applications involving multirotor aircraft, it is crucial to know both the direction and speed of the ambient wind. In this paper, an approach to wind vector estimation based on an equilibrium of the principal forces acting on the aircraft is shown. As the thrust force generated by the rotors depends on their rotational speed, a sensor to measure this quantity is required. Two concepts for such a sensor are presented: One is based on tapping the signal carrying the speed setpoint for the motor controllers, the other one uses phototransistors placed underneath the rotor blades. While some complications were encountered with the first approach, the second yields accurate measurement data. This is shown by an experiment comparing the proposed speed sensor to a commercial non-contact tachometer. }, year = {2018} } @inproceedings{Schindler1284092, author = {Schindler, Maike and Lilienthal, Achim}, booktitle = {Dialogue between ontology and epistemology : New perspectives on theory and methodology in research on learning and education}, institution = {Örebro University, School of Science and Technology}, institution = {University of Cologne, Department of Special Education}, title = {Method and Theory in Their Interplay : Using Eye-Tracking for Investigating Mathematical Learning}, keywords = {Eye tracking, mathematics education research}, abstract = {In this presentation, we discuss the interplay between theory and one particular method of data collection: eye-tracking. Eye-tracking promises various opportunities for research, in particular for studying students’ attention, strategies, and even collaboration in so-called dual eye-tracking (DUET), and has gained increased interest as a research method. Still, researchers acknowledge that eye-tracking data interpretation is difficult and ambiguous and often needs to be complemented with other sources. In this talk, we discuss two studies in which we aimed for a triangulation of eye-tracking with other research methods. In both studies, ontological and epistemological questions are intertwined. }, year = {2018} } @inproceedings{Schindler1284095, author = {Schindler, Maike and Schindler, Florian and Lilienthal, Achim and Bader, Eveline}, booktitle = {Beiträge zum Mathematikunterricht 2018 : }, institution = {Örebro University, School of Science and Technology}, institution = {University of Cologne, Department of Special Education, Cologne, Germany}, institution = {Dortmund University, Dortmund, Germany}, institution = {University of Cologne, Cologne, Germany}, pages = {1591--1594}, title = {Vorgehensweisen bei der Anzahlerfassung am 100er Feld und 100er Rahmen : Eine Eye-Tracking Studie bei Kindern mit und ohne Rechenschwierigkeiten sowie sonderp{\"a}dagogischem Unterst{\"u}tzungsbedarf}, keywords = {Eye-tracking, quantity recognition, mathematical difficulties}, abstract = {Arbeitsmittel werden im Mathematikunterricht zum Aufbau von Zahl- und Operationsvorstellungen genutzt. Gerade für Kinder mit Schwierigkeiten im strukturierten Erfassen von Anzahlen undunzureichenden Zahl- und Operationsvorstellungen ist die Nutzung von Darstellungen zentral. Wie gehenjedoch Kinder mit Rechenschwierigkeiten bei der Anzahlerfassung in unterschiedlichen Darstellungenvor und inwiefern erfolgt ein Transfer zwischen strukturell ähnlichen Darstellungen? Die vorgestellteStudie untersucht Vorgehensweisen bei der Anzahlerfassung am 100er Feld und 100er Rahmen bei 20Kindern (davon 11 mit Rechenschwierigkeiten und z.T. sonderpädagogischem Unterstützungsbedarf) zu Beginn der fünften Klasse. Eye-Tracking ermöglicht dabei neue Erkenntnisse gerade bei Kindern, die Schwierigkeiten haben, ihre Vorgehensweisen zu beschreiben. Die Ergebnisse liefern Einblicke in mathematische Kompetenzen und Schwierigkeiten der Kinder sowie die Unterschiede in der Nutzung derbeiden Darstellungen. }, year = {2018} } @inproceedings{Wiedemann1139725, author = {Wiedemann, Thomas and Shutin, Dmitri and Hernandez Bennetts, Victor and Schaffernicht, Erik and Lilienthal, Achim}, booktitle = {2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017) : Proceedings}, institution = {Örebro University, School of Science and Technology}, institution = {Institute of Communications and Navigation, German Aerospace Center (DLR), Wessling, Germany}, institution = {Institute of Communications and Navigation, German Aerospace Center (DLR), Wessling, Germany}, pages = {122--124}, title = {Bayesian Gas Source Localization and Exploration with a Multi-Robot System Using Partial Differential Equation Based Modeling}, DOI = {10.1109/ISOEN.2017.7968884}, abstract = {Here we report on active water sampling devices forunderwater chemical sensing robots. Crayfish generate jetlikewater currents during food search by waving theflagella of their maxillipeds. The jets generated toward theirsides induce an inflow from the surroundings to the jets.Odor sample collection from the surroundings to theirolfactory organs is promoted by the generated inflow.Devices that model the jet discharge of crayfish have beendeveloped to investigate the effectiveness of the activechemical sampling. Experimental results are presented toconfirm that water samples are drawn to the chemicalsensors from the surroundings more rapidly by using theaxisymmetric flow field generated by the jet discharge thanby centrosymmetric flow field generated by simple watersuction. Results are also presented to show that there is atradeoff between the angular range of chemical samplecollection and the sample collection time. }, ISBN = {978-1-5090-2393-6}, ISBN = {978-1-5090-2392-9}, year = {2017} } @inproceedings{Neumann1179677, author = {Neumann, Patrick P. and Kohlhoff, Harald and H{\"u}llmann, Dino and Lilienthal, Achim and Kluge, Martin}, booktitle = {2017 IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, pages = {3910--3916}, title = {Bringing Mobile Robot Olfaction to the Next Dimension - UAV-based Remote Sensing of Gas Clouds and Source Localization}, series = {IEEE International Conference on Robotics and Automation}, DOI = {10.1109/ICRA.2017.7989450}, abstract = {This paper introduces a novel robotic platform for aerial remote gas sensing. Spectroscopic measurement methods for remote sensing of selected gases lend themselves for use on mini-copters, which offer a number of advantages for inspection and surveillance. No direct contact with the target gas is needed and thus the influence of the aerial platform on the measured gas plume can be kept to a minimum. This allows to overcome one of the major issues with gas-sensitive mini-copters. On the other hand, remote gas sensors, most prominently Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensors have been too bulky given the payload and energy restrictions of mini-copters. Here, we introduce and present the Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS), which combines a novel lightweight TDLAS sensor with a 3-axis aerial stabilization gimbal for aiming on a versatile hexacopter. The proposed system can be deployed in scenarios that cannot be addressed by currently available robots and thus constitutes a significant step forward for the field of Mobile Robot Olfaction (MRO). It enables tomographic reconstruction of gas plumes and a localization of gas sources. We also present first results showing the gas sensing and aiming capabilities under realistic conditions. }, ISBN = {978-1-5090-4633-1}, ISBN = {978-1-5090-4634-8}, year = {2017} } @article{Canelhas1175909, author = {Canelhas, Daniel R. and Schaffernicht, Erik and Stoyanov, Todor and Lilienthal, Achim and Davison, Andrew J.}, institution = {Örebro University, School of Science and Technology}, institution = {Department of Computing, Imperial College London, London, United Kingdom}, journal = {Robotics}, note = {Funding Agencies:European Commission  FP7-ICT-270350 H-ICT  732737 }, number = {3}, eid = {15}, publisher = {MDPI AG}, title = {Compressed Voxel-Based Mapping Using Unsupervised Learning}, volume = {6}, DOI = {10.3390/robotics6030015}, keywords = {3D mapping, TSDF, compression, dictionary learning, auto-encoder, denoising}, abstract = {In order to deal with the scaling problem of volumetric map representations, we propose spatially local methods for high-ratio compression of 3D maps, represented as truncated signed distance fields. We show that these compressed maps can be used as meaningful descriptors for selective decompression in scenarios relevant to robotic applications. As compression methods, we compare using PCA-derived low-dimensional bases to nonlinear auto-encoder networks. Selecting two application-oriented performance metrics, we evaluate the impact of different compression rates on reconstruction fidelity as well as to the task of map-aided ego-motion estimation. It is demonstrated that lossily reconstructed distance fields used as cost functions for ego-motion estimation can outperform the original maps in challenging scenarios from standard RGB-D (color plus depth) data sets due to the rejection of high-frequency noise content. }, year = {2017} } @inproceedings{Lilienthal1179666, author = {Lilienthal, Achim and Schindler, Maike}, booktitle = {Proceedings the 41th Conference of the International Group for the Psychology of Mathematics Education : }, institution = {Örebro University, School of Science and Technology}, institution = {University of Cologne, Cologne, Germany}, pages = {233--233}, publisher = {PME}, title = {Conducting Dual Portable Eye-Tracking in Mathematical Creativity Research}, volume = {1}, abstract = {Eye-tracking opens a window to the focus of attention of persons and promises to allow studying, e.g., creative processes “in vivo” (Nüssli, 2011). Most eye-tracking studies in mathematics education research focus on single students. However, following a Vygotskyan notion of learning and development where the individual and the social are dialectically interrelated, eye-tracking studies of collaborating persons appear beneficial for understanding students’ learning in their social facet. Dual eye-tracking, where two persons’ eye-movements are recorded and related to a joint coordinate-system, has hardly been used in mathematics education research. Especially dual portable eye-tracking (DPET) with goggles has hardly been explored due to its technical challenges compared to screen-based eye-tracking.In our interdisciplinary research project between mathematics education and computer science, we conduct DPET for studying collective mathematical creativity (Levenson, 2011) in a process perspective. DPET offers certain advantages, including to carry out paper and pen tasks in rather natural settings. Our research interests are: conducting DPET (technical), investigating opportunities and limitations of DPET for studying students’ collective creativity (methodological), and studying students’ collective creative problem solving (empirical).We carried out experiments with two pairs of university students wearing Pupil Pro eye tracking goggles. The students were given 45 min to solve a geometry problem in as many ways as possible. For our analysis, we first programmed MATLAB code to synchronize data from both participants’ goggles; resulting in a video displaying both students’ eye-movements projected on the task sheet, the sound recorded by the goggles, and additional information, e.g. pupil dilation. With these videos we expect to get insights into how students’ attentions meet, if students’ eye-movements follow one another, or verbal inputs, etc. We expect insights into promotive aspects in students’ collaboration: e.g., if pointing on the figure or intensive verbal communication promote students’ joint attention (cf. Nüssli, 2011). Finally, we think that the expected insights can contribute to existing research on collective mathematical creativity, especially to the question of how to enhance students’ creative collaboration. }, ISBN = {978-138-71-3608-7}, year = {2017} } @article{Kucner1070541, author = {Kucner, Tomasz Piotr and Magnusson, Martin and Schaffernicht, Erik and Hernandez Bennetts, Victor Manuel and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {IEEE Robotics and Automation Letters}, note = {Funding Agencies:EU project SPENCER  ICT-2011-600877 H2020-ICT project SmokeBot  645101 H2020-ICT project ILIAD  732737 }, number = {2}, pages = {1093--1100}, title = {Enabling Flow Awareness for Mobile Robots in Partially Observable Environments}, volume = {2}, DOI = {10.1109/LRA.2017.2660060}, keywords = {Field robots; mapping; social human-robot interaction}, abstract = {Understanding the environment is a key requirement for any autonomous robot operation. There is extensive research on mapping geometric structure and perceiving objects. However, the environment is also defined by the movement patterns in it. Information about human motion patterns can, e.g., lead to safer and socially more acceptable robot trajectories. Airflow pattern information allow to plan energy efficient paths for flying robots and improve gas distribution mapping. However, modelling the motion of objects (e.g., people) and flow of continuous media (e.g., air) is a challenging task. We present a probabilistic approach for general flow mapping, which can readily handle both of these examples. Moreover, we present and compare two data imputation methods allowing to build dense maps from sparsely distributed measurements. The methods are evaluated using two different data sets: one with pedestrian data and one with wind measurements. Our results show that it is possible to accurately represent multimodal, turbulent flow using a set of Gaussian Mixture Models, and also to reconstruct a dense representation based on sparsely distributed locations. }, year = {2017} } @inproceedings{Vuka1139675, author = {Vuka, Mikel and Schaffernicht, Erik and Schmuker, Michael and Hernandez Bennetts, Victor and Amigoni, Francesco and Lilienthal, Achim J}, booktitle = {2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017) : Proceedings}, institution = {Örebro University, School of Science and Technology}, institution = {Dipartitmento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy}, institution = {School of Computer Science, College Lane, University of Hertfordshire, Hatfield, United Kingdom}, institution = {Dipartitmento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy}, pages = {164--166}, title = {Exploration and Localization of a Gas Source with MOX Gas Sensorson a Mobile Robot : A Gaussian Regression Bout Amplitude Approach}, DOI = {10.1109/ISOEN.2017.7968898}, abstract = {Mobile robot olfaction systems combine gas sensorswith mobility provided by robots. They relief humansof dull, dirty and dangerous tasks in applications such assearch & rescue or environmental monitoring. We address gassource localization and especially the problem of minimizingexploration time of the robot, which is a key issue due toenergy constraints. We propose an active search approach forrobots equipped with MOX gas sensors and an anemometer,given an occupancy map. Events of rapid change in the MOXsensor signal (“bouts”) are used to estimate the distance to agas source. The wind direction guides a Gaussian regression,which interpolates distance estimates. The contributions of thispaper are two-fold. First, we extend previous work on gassource distance estimation with MOX sensors and propose amodification to cope better with turbulent conditions. Second,we introduce a novel active search gas source localizationalgorithm and validate it in a real-world environment. }, year = {2017} } @inproceedings{Schindler1179672, author = {Schindler, Maike and Lilienthal, Achim}, booktitle = {Proceedings of the 41st Conference of the International Group for the Psychology of Mathematics Education : }, institution = {Örebro University, School of Science and Technology}, institution = {University of Cologne, Cologne, Germany}, pages = {153--160}, publisher = {PME}, title = {Eye-Tracking and its Domain-Specific Interpretation : A Stimulated Recall Study on Eye Movements in Geometrical Tasks}, volume = {4}, abstract = {Eye-tracking offers various possibilities for mathematics education. Yet, even in suitably visually presented tasks, interpretation of eye-tracking data is non-trivial. A key reason is that the interpretation of eye-tracking data is context-sensitive. To reduce ambiguity and uncertainty, we studied the interpretation of eye movements in a specific domain: geometrical mathematical creativity tasks. We present results from a qualitative empirical study in which we analyzed a Stimulated Recall Interview where a student watched the eye-tracking overlaid video of his work on a task. Our results hint at how eye movements can be interpreted and show limitations and opportunities of eye tracking in the domain of mathematical geometry tasks and beyond. }, ISBN = {978-138-71-3613-1}, year = {2017} } @inproceedings{Schindler1179675, author = {Schindler, Maike and Lilienthal, Achim}, booktitle = {The 10th Mathematical Creativity and Giftedness International Conference : Proceedings}, institution = {Örebro University, School of Science and Technology}, institution = {University of Cologne, Cologne, Germany}, pages = {45--50}, publisher = {Department of Education, University of Cyprus}, title = {Eye-Tracking As A Tool For Investigating Mathematical Creativity}, keywords = {Mathematical Creativity, Eye-Tracking, Eye Movements, MSTs, geometry, proof}, abstract = {Mathematical creativity as a key ability in our increasingly automated and interconnected, high-technology based society and economy is increasingly in the focus of mathematics education research. The recent scientific discussion in this domain is shifting from a product view, on written solutions and drawings, to a process view, which aims to investigate the different stages of how students come up with creative ideas. The latter is, however, a challenge. In this theoretical-methodological paper, we present and discuss the opportunities that eye-tracking offers for studying creativity in a process view. We discuss in which way eye-tracking allows to obtain novel answers to the questions of how original ideas come up, how they evolve and what leads to the so-called Eureka!-moment. We focus on video-based eye tracking approaches, discuss pros and cons of screen-based and mobile eye tracking, and illustrate methods of data analysis and their benefits for research on mathematical creativity. }, ISBN = {978-9963-700-99-8}, year = {2017} } @article{Monroy1179652, author = {Monroy, Javier and Hernandez Bennetts, Victor and Fan, Han and Lilienthal, Achim and Gonzalez-Jimenez, Javier}, institution = {Örebro University, School of Science and Technology}, institution = {Machine Perception and Intelligent Robotics group (MAPIR), Instituto de Investigación Biomedica de Malaga (IBIMA), Universidad de Malaga, Malaga, Spain}, institution = {Machine Perception and Intelligent Robotics group (MAPIR), Instituto de Investigación Biomedica de Malaga (IBIMA), Universidad de Malaga, Malaga, Spain}, journal = {Sensors}, note = {Funding Agencies:Spanish GovermentAndalucia Goverment}, number = {7}, pages = {1479--1494}, publisher = {MPDI AG}, title = {GADEN : A 3D Gas Dispersion Simulator for Mobile Robot Olfaction in Realistic Environments}, volume = {17}, DOI = {10.3390/s17071479}, keywords = {Gas dispersal, robotics olfaction, gas sensing, mobile robotics, Robot Operating System (ROS)}, abstract = {This work presents a simulation framework developed under the widely used Robot Operating System (ROS) to enable the validation of robotics systems and gas sensing algorithms under realistic environments. The framework is rooted in the principles of computational fluid dynamics and filament dispersion theory, modeling wind flow and gas dispersion in 3D real-world scenarios (i.e., accounting for walls, furniture, etc.). Moreover, it integrates the simulation of different environmental sensors, such as metal oxide gas sensors, photo ionization detectors, or anemometers. We illustrate the potential and applicability of the proposed tool by presenting a simulation case in a complex and realistic office-like environment where gas leaks of different chemicals occur simultaneously. Furthermore, we accomplish quantitative and qualitative validation by comparing our simulated results against real-world data recorded inside a wind tunnel where methane was released under different wind flow profiles. Based on these results, we conclude that our simulation framework can provide a good approximation to real world measurements when advective airflows are present in the environment. }, year = {2017} } @inproceedings{Fan1138648, author = {Fan, Han and Arain, Muhammad Asif and Hernandez Bennetts, Victor and Schaffernicht, Erik and Lilienthal, Achim J.}, booktitle = {2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings : }, institution = {Örebro University, School of Science and Technology}, eid = {17013581}, title = {Improving Gas Dispersal Simulation For Mobile Robot Olfaction : Using Robot-Created Occupancy Maps And Remote Gas Sensors In The Simulation Loop}, DOI = {10.1109/ISOEN.2017.7968874}, abstract = {Mobile robot platforms equipped with olfaction systems have been used in many gas sensing applications. However, in-field validation of mobile robot olfaction systems is time consuming, expensive, cumbersome and lacks repeatability. In order to address these issues, simulation tools are used. However, the available mobile robot olfaction simulations lack models for remote gas sensors, and the possibility to import geometrical representations of actual real-world environments in a convenient way. In this paper, we describe extensions to an open-source CFD-based filament gas dispersal simulator. These improvements arrow to use robot-created occupancy maps and offer remote sensing capabilities in the simulation loop. We demonstrate the novel features in an example application: we created a 3D map a complex indoor environment, and performed a gas emission monitoring task with a Tunable Diode Laser Absorption Spectroscopy based remote gas sensor in a simulated version of the environment. }, ISBN = {978-1-5090-2392-9}, ISBN = {978-1-5090-2393-6}, year = {2017} } @inproceedings{Arain1139140, author = {Arain, Muhammad Asif and Fan, Han and Hernandez Bennetts, Victor and Schaffernicht, Erik and Lilienthal, Achim J.}, booktitle = {2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings : }, institution = {Örebro University, School of Science and Technology}, eid = {7968895}, title = {Improving Gas Tomography With Mobile Robots : An Evaluation of Sensing Geometries in Complex Environments}, DOI = {10.1109/ISOEN.2017.7968895}, abstract = {An accurate model of gas emissions is of high importance in several real-world applications related to monitoring and surveillance. Gas tomography is a non-intrusive optical method to estimate the spatial distribution of gas concentrations using remote sensors. The choice of sensing geometry, which is the arrangement of sensing positions to perform gas tomography, directly affects the reconstruction quality of the obtained gas distribution maps. In this paper, we present an investigation of criteria that allow to determine suitable sensing geometries for gas tomography. We consider an actuated remote gas sensor installed on a mobile robot, and evaluated a large number of sensing configurations. Experiments in complex settings were conducted using a state-of-the-art CFD-based filament gas dispersal simulator. Our quantitative comparison yields preferred sensing geometries for sensor planning, which allows to better reconstruct gas distributions. }, ISBN = {978-1-5090-2392-9}, ISBN = {978-1-5090-2393-6}, year = {2017} } @inproceedings{Andreasson1159885, author = {Andreasson, Henrik and Adolfsson, Daniel and Stoyanov, Todor and Magnusson, Martin and Lilienthal, Achim}, booktitle = {2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, pages = {1389--1395}, title = {Incorporating Ego-motion Uncertainty Estimates in Range Data Registration}, series = {Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2017.8202318}, abstract = {Local scan registration approaches commonlyonly utilize ego-motion estimates (e.g. odometry) as aninitial pose guess in an iterative alignment procedure. Thispaper describes a new method to incorporate ego-motionestimates, including uncertainty, into the objective function of aregistration algorithm. The proposed approach is particularlysuited for feature-poor and self-similar environments,which typically present challenges to current state of theart registration algorithms. Experimental evaluation showssignificant improvements in accuracy when using data acquiredby Automatic Guided Vehicles (AGVs) in industrial productionand warehouse environments. }, ISBN = {978-1-5386-2682-5}, ISBN = {978-1-5386-2683-2}, year = {2017} } @inproceedings{Palmieri1070556, author = {Palmieri, Luigi and Kucner, Tomasz and Magnusson, Martin and Lilienthal, Achim J. and Arras, Kai}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA 2017) : }, institution = {Örebro University, School of Science and Technology}, institution = {Computer Science Department, University of Freiburg, Freiburg im Breisgau, Germany}, institution = {Bosch Corporate Research, Stuttgart, Germany}, pages = {6176--6181}, eid = {7989731}, title = {Kinodynamic Motion Planning on Gaussian Mixture Fields}, DOI = {10.1109/ICRA.2017.7989731}, abstract = {We present a mobile robot motion planning ap-proach under kinodynamic constraints that exploits learnedperception priors in the form of continuous Gaussian mixturefields. Our Gaussian mixture fields are statistical multi-modalmotion models of discrete objects or continuous media in theenvironment that encode e.g. the dynamics of air or pedestrianflows. We approach this task using a recently proposed circularlinear flow field map based on semi-wrapped GMMs whosemixture components guide sampling and rewiring in an RRT*algorithm using a steer function for non-holonomic mobilerobots. In our experiments with three alternative baselines,we show that this combination allows the planner to veryefficiently generate high-quality solutions in terms of pathsmoothness, path length as well as natural yet minimum controleffort motions through multi-modal representations of Gaussianmixture fields. }, year = {2017} } @inproceedings{Palm1179669, author = {Palm, Rainer and Lilienthal, Achim}, booktitle = {2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) : }, institution = {Örebro University, School of Science and Technology}, eid = {8015396}, title = {Long distance prediction and short distance control in Human-Robot Systems}, DOI = {10.1109/FUZZ-IEEE.2017.8015396}, abstract = {The study of the interaction between autonomous robots and human agents in common working areas is an emerging field of research. Main points thereby are human safety, system stability, performance and optimality of the whole interaction process. Two approaches to deal with human-robot interaction can be distinguished: Long distance prediction which requires the recognition of intentions of other agents, and short distance control which deals with actions and reactions between agents and mutual reactive control of their motions and behaviors. In this context obstacle avoidance plays a prominent role. In this paper long distance prediction is represented by the identification of human intentions to use specific lanes by using fuzzy time clustering of pedestrian tracks. Another issue is the extrapolation of parts of both human and robot trajectories in the presence of scattered/uncertain measurements to guarantee a collision-free robot motion. Short distance control is represented by obstacle avoidance between agents using the method of velocity obstacles and both analytical and fuzzy control methods. }, ISBN = {978-1-5090-6034-4}, ISBN = {978-1-5090-6035-1}, ISBN = {978-1-5090-6033-7}, year = {2017} } @inproceedings{Xing1176939, author = {Xing, Yuxin and Vincent, Timothy A. and Cole, Marina and Gardner, Julian W. and Fan, Han and Hernandez Bennetts, Victor and Schaffernicht, Erik and Lilienthal, Achim}, booktitle = {IEEE SENSORS 2017 : Conference Proceedings}, institution = {Örebro University, School of Science and Technology}, institution = {School of Engineering, University of Warwick, Coventry, UK}, institution = {School of Engineering, University of Warwick, Coventry, UK}, institution = {School of Engineering, University of Warwick, Coventry, UK}, institution = {School of Engineering, University of Warwick, Coventry, UK}, pages = {1691--1693}, title = {Mobile robot multi-sensor unit for unsupervised gas discrimination in uncontrolled environments}, series = {Proceedings of IEEE Sensors}, DOI = {10.1109/ICSENS.2017.8234440}, keywords = {Gas sensor, mobile robot, MOX, open sampling system, gas discrimination}, abstract = {In this work we present a novel multi-sensor unit to detect and discriminate unknown gases in uncontrolled environments. The unit includes three metal oxide (MOX) sensors with CMOS micro heaters, a plasmonic enhanced non-dispersive infra-red (NDIR) sensor, a commercial temperature humidity sensor, and a flow sensor. The proposed sensing unit was evaluated with plumes of gases (propanol, ethanol and acetone) in both, a laboratory setup on a gas testing bench and on-board a mobile robot operating in an indoor workshop. It offers significantly improved performance compared to commercial systems, in terms of power consumption, response time and physical size. We verified the ability to discriminate gases in an unsupervised manner, with data collected on the robot and high accuracy was obtained in the classification of propanol versus acetone (96%), and ethanol versus acetone (90%). }, ISBN = {978-1-5090-1012-7}, ISBN = {978-1-5090-1013-4}, year = {2017} } @inproceedings{Schaffernicht1170470, author = {Schaffernicht, Erik and Hernandez Bennetts, Victor and Lilienthal, Achim}, booktitle = {2017 IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, pages = {2659--2665}, title = {Mobile robots for learning spatio-temporal interpolation models in sensor networks - The Echo State map approach : The Echo State map approach}, DOI = {10.1109/ICRA.2017.7989310}, keywords = {Gaussian processes, learning (artificial intelligence), mobile robots, neurocontrollers, wireless sensor networks, Gaussian process estimator, echo state map approach, gas concentration, mobile robots, particulate matter measurement, sensor networks, spatio-temporal interpolation model learning, temperature concentration, Foundries, Interpolation, Mobile robots, Robot sensing systems, Wireless sensor networks}, abstract = {Sensor networks have limited capabilities to model complex phenomena occuring between sensing nodes. Mobile robots can be used to close this gap and learn local interpolation models. In this paper, we utilize Echo State Networks in order to learn the calibration and interpolation model between sensor nodes using measurements collected by a mobile robot. The use of Echo State Networks allows to deal with temporal dependencies implicitly, while the spatial mapping with a Gaussian Process estimator exploits the fact that Echo State Networks learn linear combinations of complex temporal dynamics. The resulting Echo State Map elegantly combines spatial and temporal cues into a single representation. We showcase the method in the exposure modeling task of building dust distribution maps for foundries, a challenge which is of great interest to occupational health researchers. Results from simulated data and real world experiments highlight the potential of Echo State Maps. While we focus on particulate matter measurements, the method can be applied for any other environmental variables like temperature or gas concentration. }, year = {2017} } @inproceedings{Hullmann1279636, author = {H{\"u}llmann, Dino and Paul, Niels and Neumann, Patrick P. and Lilienthal, Achim}, booktitle = {34th Danubia-Adria Symposium on Advances in Experimental Mechanics : Book of proceedings}, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, pages = {75--77}, publisher = {EUT Edizioni Università di Trieste}, title = {Motor Speed Transfer Function for Wind Vector Estimation on Multirotor Aircraft}, keywords = {Anemometer, Multicopter, UAV, PWM, Wind}, abstract = {A set of equations is derived to estimate the 3D wind vector with a multirotor aircraft using the aircraft itself as a flying anemometer. Since the thrust component is required to compute the wind vector, the PWM signal controlling the motors of the aircraft is measured and a transfer function describing the relation between the PWM signal and the rotational speed of the motors is derived. }, URL = {https://www.openstarts.units.it/handle/10077/14834}, ISBN = {978-88-8303-863-1}, year = {2017} } @article{HernandezBennetts1078590, author = {Hernandez Bennetts, Victor and Kucner, Tomasz Piotr and Schaffernicht, Erik and Neumann, Patrick P. and Fan, Han and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany}, journal = {IEEE Robotics and Automation Letters}, note = {Funding Agency:H2020-ICT project SmokeBot  645101}, number = {2}, pages = {1117--1123}, title = {Probabilistic Air Flow Modelling Using Turbulent and Laminar Characteristics for Ground and Aerial Robots}, volume = {2}, DOI = {10.1109/LRA.2017.2661803}, keywords = {Aerial systems: perception and autonomy, environment monitoring and management, field robots, mapping}, abstract = {For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling<?brk?> algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback&ndash;Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts. }, year = {2017} } @inproceedings{Wiedemann1179662, author = {Wiedemann, Thomas and Manss, Christoph and Shutin, Dmitriy and Lilienthal, Achim and Karolj, Valentina and Viseras, Alberto}, booktitle = {2017 European Conference on Mobile Robots (ECMR) : }, institution = {Örebro University, School of Science and Technology}, institution = {Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Germany}, institution = {Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Germany}, institution = {Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Germany}, institution = {Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Germany}, institution = {Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Germany}, note = {Funding Agency:H2020-ICT by the European Commission  645101}, eid = {8098707}, title = {Probabilistic modeling of gas diffusion with partial differential equations for multi-robot exploration and gas source localization}, DOI = {10.1109/ECMR.2017.8098707}, keywords = {multi-agent exploration, gas source localization, mobile robot olfaction partial differential equation, factor graph, sparse Bayesian learning, message passing}, abstract = {Employing automated robots for sampling gas distributions and for localizing gas sources is beneficial since it avoids hazards for a human operator. This paper addresses the problem of exploring a gas diffusion process using a multi-agent system consisting of several mobile sensing robots. The diffusion process is modeled using a partial differential equation (PDE). It is assumed that the diffusion process is driven by only a few spatial sources at unknown locations with unknown intensity. The goal of the multi-robot exploration is thus to identify source parameters, in particular, their number, locations and magnitudes. Therefore, this paper develops a probabilistic approach towards PDE identification under sparsity constraint using factor graphs and a message passing algorithm. Moreover, the message passing schemes permits efficient distributed implementation of the algorithm. This brings significant advantages with respect to scalability, computational complexity and robustness of the proposed exploration algorithm. Based on the derived probabilistic model, an exploration strategy to guide the mobile agents in real time to more informative sampling locations is proposed. Hardware- in-the-loop experiments with real mobile robots show that the proposed exploration approach accelerates the identification of the source parameters and outperforms systematic sampling. }, ISBN = {978-1-5386-1096-1}, ISBN = {978-1-5386-1097-8}, year = {2017} } @inproceedings{Magnusson1151027, author = {Magnusson, Martin and Kucner, Tomasz Piotr and Gholami Shahbandi, Saeed and Andreasson, Henrik and Lilienthal, Achim}, booktitle = {2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, institution = {IS lab, Halmstad University, Halmstad, Sweden}, note = {Iliad Project: http://iliad-project.eu}, pages = {620--625}, title = {Semi-Supervised 3D Place Categorisation by Descriptor Clustering}, series = {Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2017.8202216}, abstract = {Place categorisation; i. e., learning to group perception data into categories based on appearance; typically uses supervised learning and either visual or 2D range data. This paper shows place categorisation from 3D data without any training phase. We show that, by leveraging the NDT histogram descriptor to compactly encode 3D point cloud appearance, in combination with standard clustering techniques, it is possible to classify public indoor data sets with accuracy comparable to, and sometimes better than, previous supervised training methods. We also demonstrate the effectiveness of this approach to outdoor data, with an added benefit of being able to hierarchically categorise places into sub-categories based on a user-selected threshold. This technique relieves users of providing relevant training data, and only requires them to adjust the sensitivity to the number of place categories, and provide a semantic label to each category after the process is completed. }, ISBN = {978-1-5386-2682-5}, ISBN = {978-1-5386-2683-2}, year = {2017} } @inproceedings{Mielle1155435, author = {Mielle, Malcolm and Magnusson, Martin and Andreasson, Henrik and Lilienthal, Achim J.}, booktitle = {2017 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR) : }, institution = {Örebro University, School of Science and Technology}, note = {Funding Agency:EU  ICT-23-2014645101}, pages = {35--40}, eid = {8088137}, title = {SLAM auto-complete : completing a robot map using an emergency map}, DOI = {10.1109/SSRR.2017.8088137}, keywords = {SLAM, robotics, graph, graph SLAM, emergency map, rescue, exploration, auto complete, SLAM, robotics, graph, graph SLAM, plan de secours, sauvetage, exploration, auto complete}, abstract = {In search and rescue missions, time is an important factor; fast navigation and quickly acquiring situation awareness might be matters of life and death. Hence, the use of robots in such scenarios has been restricted by the time needed to explore and build a map. One way to speed up exploration and mapping is to reason about unknown parts of the environment using prior information. While previous research on using external priors for robot mapping mainly focused on accurate maps or aerial images, such data are not always possible to get, especially indoor. We focus on emergency maps as priors for robot mapping since they are easy to get and already extensively used by firemen in rescue missions. However, those maps can be outdated, information might be missing, and the scales of rooms are typically not consistent. We have developed a formulation of graph-based SLAM that incorporates information from an emergency map. The graph-SLAM is optimized using a combination of robust kernels, fusing the emergency map and the robot map into one map, even when faced with scale inaccuracies and inexact start poses. We typically have more than 50% of wrong correspondences in the settings studied in this paper, and the method we propose correctly handles them. Experiments in an office environment show that we can handle up to 70% of wrong correspondences and still get the expected result. The robot can navigate and explore while taking into account places it has not yet seen. We demonstrate this in a test scenario and also show that the emergency map is enhanced by adding information not represented such as closed doors or new walls. }, ISBN = {978-1-5386-3923-8}, ISBN = {978-1-5386-3924-5}, year = {2017} } @article{Asadi1159790, author = {Asadi, Sahar and Fan, Han and Hernandez Bennetts, Victor and Lilienthal, Achim}, institution = {Örebro University, School of Science and Technology}, journal = {Robotics and Autonomous Systems}, note = {Funding Agency:EC  FP7-224318-DIADEM}, pages = {157--170}, title = {Time-dependent gas distribution modelling}, volume = {96}, DOI = {10.1016/j.robot.2017.05.012}, keywords = {Mobile robot olfaction, Statistical gas distribution modelling, Temporal sub-sampling, Time-dependent gas distribution modelling}, abstract = {Artificial olfaction can help to address pressing environmental problems due to unwanted gas emissions. Sensor networks and mobile robots equipped with gas sensors can be used for e.g. air pollution monitoring. Key in this context is the ability to derive truthful models of gas distribution from a set of sparse measurements. Most statistical gas distribution modelling methods assume that gas dispersion is a time constant random process. While this assumption approximately holds in some situations, it is necessary to model variations over time in order to enable applications of gas distribution modelling in a wider range of realistic scenarios. Time-invariant approaches cannot model well evolving gas plumes, for example, or major changes in gas dispersion due to a sudden change of the environmental conditions. This paper presents two approaches to gas distribution modelling, which introduce a time-dependency and a relation to a time-scale in generating the gas distribution model either by sub-sampling or by introducing a recency weight that relates measurement and prediction time. We evaluated these approaches in experiments performed in two real environments as well as on several simulated experiments. As expected, the comparison of different sub-sampling strategies revealed that more recent measurements are more informative to derive an estimate of the current gas distribution as long as a sufficient spatial coverage is given. Next, we compared a time-dependent gas distribution modelling approach (TD Kernel DM+V), which includes a recency weight, to the state-of-the-art gas distribution modelling approach (Kernel DM+V), which does not consider sampling times. The results indicate a consistent improvement in the prediction of unseen measurements, particularly in dynamic scenarios. Furthermore, this paper discusses the impact of meta-parameters in model selection and compares the performance of time-dependent GDM in different plume conditions. Finally, we investigated how to set the target time for which the model is created. The results indicate that TD Kernel DM+V performs best when the target time is set to the maximum sampling time in the test set. }, year = {2017} } @inproceedings{Mielle1151040, author = {Mielle, Malcolm and Magnusson, Martin and Andreasson, Henrik and Lilienthal, Achim}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, title = {Using emergency maps to add not yet explored places into SLAM}, keywords = {Search and Rescue Robots, SLAM, Mapping}, abstract = {While using robots in search and rescue missions would help ensure the safety of first responders, a key issue is the time needed by the robot to operate. Even though SLAM is faster and faster, it might still be too slow to enable the use of robots in critical situations. One way to speed up operation time is to use prior information. We aim at integrating emergency-maps into SLAM to complete the SLAM map with information about not yet explored part of the environment. By integrating prior information, we can speed up exploration time or provide valuable prior information for navigation, for example, in case of sensor blackout/failure. However, while extensively used by firemen in their operations, emergency maps are not easy to integrate in SLAM since they are often not up to date or with non consistent scales. The main challenge we are tackling is in dealing with the imperfect scale of the rough emergency maps and integrate it with the online SLAM map in addition to challenges due to incorrect matches between these two types of map. We developed a formulation of graph-based SLAM incorporating information from an emergency map into SLAM, and propose a novel optimization process adapted to this formulation. We extract corners from the emergency map and the SLAM map, in between which we find correspondences using a distance measure. We then build a graph representation associating information from the emergency map and the SLAM map. Corners in the emergency map, corners in the robot map, and robot poses are added as nodes in the graph, while odometry, corner observations, walls in the emergency map, and corner associations are added as edges. To conserve the topology of the emergency map, but correct its possible errors in scale, edges representing the emergency map's walls are given a covariance so that they are easy to extend or shrink but hard to rotate. Correspondences between corners represent a zero transformation for the optimization to match them as close as possible. The graph optimization is done by using a combination robust kernels. We first use the Huber kernel, to converge toward a good solution, followed by Dynamic Covariance Scaling, to handle the remaining errors. We demonstrate our system in an office environment. We run the SLAM online during the exploration. Using the map enhanced by information from the emergency map, the robot was able to plan the shortest path toward a place it has not yet explored. This capability can be a real asset in complex buildings where exploration can take up a long time. It can also reduce exploration time by avoiding exploration of dead-ends, or search of specific places since the robot knows where it is in the emergency map. }, year = {2017} } @inproceedings{Krug1064907, author = {Krug, Robert and Lilienthal, Achim J. and Kragic, Danica and Bekiroglu, Yasemin}, booktitle = {2016 IEEE International Conference on Robotics and Automation, ICRA 2016 : }, institution = {Örebro University, School of Science and Technology}, institution = {Centre for Autonomous Systems, Computer Vision and Active Perception Lab, CSC, KTH Stockholm, Stockholm, Sweden}, institution = {School of Mechanical Engineering, University of Birmingham, Birmingham, United Kingdom}, pages = {165--171}, publisher = {IEEE}, title = {Analytic Grasp Success Prediction with Tactile Feedback}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ICRA.2016.7487130}, abstract = {Predicting grasp success is useful for avoiding failures in many robotic applications. Based on reasoning in wrench space, we address the question of how well analytic grasp success prediction works if tactile feedback is incorporated. Tactile information can alleviate contact placement uncertainties and facilitates contact modeling. We introduce a wrench-based classifier and evaluate it on a large set of real grasps. The key finding of this work is that exploiting tactile information allows wrench-based reasoning to perform on a level with existing methods based on learning or simulation. Different from these methods, the suggested approach has no need for training data, requires little modeling effort and is computationally efficient. Furthermore, our method affords task generalization by considering the capabilities of the grasping device and expected disturbance forces/moments in a physically meaningful way. }, ISBN = {978-1-4673-8026-3}, year = {2016} } @article{Rituerto931985, author = {Rituerto, Alejandro and Andreasson, Henrik and Murillo, Ana C. and Lilienthal, Achim and Jesus Guerrero, Jose}, institution = {Örebro University, School of Science and Technology}, institution = {Instituto de Investigación en Ingeniería de Aragón, Deptartmento de Informática e Ingeniería de Sistemas, University of Zaragoza, Zaragoza, Spain}, institution = {Instituto de Investigación en Ingeniería de Aragón, Deptartmento de Informática e Ingeniería de Sistemas, University of Zaragoza, Zaragoza, Spain}, institution = {Instituto de Investigación en Ingeniería de Aragón, Deptartmento de Informática e Ingeniería de Sistemas, University of Zaragoza, Zaragoza, Spain}, journal = {Sensors}, note = {Funding Agencies:Spanish Government European Union DPI2015-65962-R}, number = {4}, eid = {493}, publisher = {MDPI AG}, title = {Building an Enhanced Vocabulary of the Robot Environment with a Ceiling Pointing Camera}, volume = {16}, DOI = {10.3390/s16040493}, keywords = {visual vocabulary, computer vision, bag of words, robotics, place recognition, environment description}, abstract = {Mobile robots are of great help for automatic monitoring tasks in different environments. One of the first tasks that needs to be addressed when creating these kinds of robotic systems is modeling the robot environment. This work proposes a pipeline to build an enhanced visual model of a robot environment indoors. Vision based recognition approaches frequently use quantized feature spaces, commonly known as Bag of Words (BoW) or vocabulary representations. A drawback using standard BoW approaches is that semantic information is not considered as a criteria to create the visual words. To solve this challenging task, this paper studies how to leverage the standard vocabulary construction process to obtain a more meaningful visual vocabulary of the robot work environment using image sequences. We take advantage of spatio-temporal constraints and prior knowledge about the position of the camera. The key contribution of our work is the definition of a new pipeline to create a model of the environment. This pipeline incorporates (1) tracking information to the process of vocabulary construction and (2) geometric cues to the appearance descriptors. Motivated by long term robotic applications, such as the aforementioned monitoring tasks, we focus on a configuration where the robot camera points to the ceiling, which captures more stable regions of the environment. The experimental validation shows how our vocabulary models the environment in more detail than standard vocabulary approaches, without loss of recognition performance. We show different robotic tasks that could benefit of the use of our visual vocabulary approach, such as place recognition or object discovery. For this validation, we use our publicly available data-set. }, year = {2016} } @inproceedings{Schindler1070809, author = {Schindler, Maike and Lilienthal, Achim and Chadalavada, Ravi and {\"O}gren, Magnus}, booktitle = {Proceedings of the 40th Conference of the International Group for the Psychology of Mathematics Education (PME) : }, institution = {Örebro University, School of Science and Technology}, title = {Creativity in the eye of the student : Refining investigations of mathematical creativity using eye-tracking goggles}, abstract = {Mathematical creativity is increasingly important for improved innovation and problem-solving. In this paper, we address the question of how to best investigate mathematical creativity and critically discuss dichotomous creativity scoring schemes. In order to gain deeper insights into creative problem-solving processes, we suggest the use of mobile, unobtrusive eye-trackers for evaluating students’ creativity in the context of Multiple Solution Tasks (MSTs). We present first results with inexpensive eye-tracking goggles that reveal the added value of evaluating students’ eye movements when investigating mathematical creativity—compared to an analysis of written/drawn solutions as well as compared to an analysis of simple videos. }, year = {2016} } @inproceedings{Chadalavada1070994, author = {Chadalavada, Ravi Teja and Andreasson, Henrik and Krug, Robert and Lilienthal, Achim}, booktitle = {Proceedings of RSS Workshop "Social Trust in Autonomous Robots 2016" : }, institution = {Örebro University, School of Science and Technology}, title = {Empirical evaluation of human trust in an expressive mobile robot}, keywords = {Human robot interaction, hri, mobile robot, trust, evaluation}, abstract = {A mobile robot communicating its intentions using Spatial Augmented Reality (SAR) on the shared floor space makes humans feel safer and more comfortable around the robot. Our previous work [1] and several other works established this fact. We built upon that work by adding an adaptable information and control to the SAR module. An empirical study about how a mobile robot builds trust in humans by communicating its intentions was conducted. A novel way of evaluating that trust is presented and experimentally shown that adaption in SAR module lead to natural interaction and the new evaluation system helped us discover that the comfort levels between human-robot interactions approached those of human-human interactions. }, year = {2016} } @article{Canelhas1044256, author = {Canelhas, Daniel R. and Stoyanov, Todor and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {IEEE Robotics and Automation Letters}, number = {2}, pages = {1148--1155}, title = {From Feature Detection in Truncated Signed Distance Fields to Sparse Stable Scene Graphs}, volume = {1}, DOI = {10.1109/LRA.2016.2523555}, keywords = {Mapping, recognition}, abstract = {With the increased availability of GPUs and multicore CPUs, volumetric map representations are an increasingly viable option for robotic applications. A particularly important representation is the truncated signed distance field (TSDF) that is at the core of recent advances in dense 3D mapping. However, there is relatively little literature exploring the characteristics of 3D feature detection in volumetric representations. In this paper we evaluate the performance of features extracted directly from a 3D TSDF representation. We compare the repeatability of Integral invariant features, specifically designed for volumetric images, to the 3D extensions of Harris and Shi & Tomasi corners. We also study the impact of different methods for obtaining gradients for their computation. We motivate our study with an example application for building sparse stable scene graphs, and present an efficient GPU-parallel algorithm to obtain the graphs, made possible by the combination of TSDF and 3D feature points. Our findings show that while the 3D extensions of 2D corner-detection perform as expected, integral invariants have shortcomings when applied to discrete TSDFs. We conclude with a discussion of the cause for these points of failure that sheds light on possible mitigation strategies. }, year = {2016} } @inproceedings{Neumann950089, author = {Neumann, Patrick P. and Hernandez Bennetts, Victor and Lilienthal, Achim J. and Bartholmai, Matthias}, booktitle = {Intelligent Autonomous Systems 13 : }, institution = {Örebro University, School of Science and Technology}, institution = {BAM Federal Institute for Materials Research and Testing, Berlin, Germany}, institution = {BAM Federal Institute for Materials Research and Testing, Berlin, Germany}, pages = {1533--1548}, title = {From Insects to Micro Air Vehicles : A Comparison of Reactive Plume Tracking Strategies}, series = {Advances in Intelligent Systems and Computing}, number = {302}, DOI = {10.1007/978-3-319-08338-4_110}, keywords = {Autonomous micro UAV, Mobile robot olfaction, Gas source localization, Reactive plume tracking, Biologically inspired robots}, abstract = {Insect behavior is a common source of inspiration for roboticists and computer scientists when designing gas-sensitive mobile robots. More specifically, tracking airborne odor plumes, and localization of distant gas sources are abilities that suit practical applications such as leak localization and emission monitoring. Gas sensing with mobile robots has been mostly addressed with ground-based platforms and under simplified conditions and thus, there exist a significant gap between the outstanding insect abilities and state-of-the-art robotics systems. As a step toward practical applications, we evaluated the performance of three biologically inspired plume tracking algorithms. The evaluation is carried out not only with computer simulations, but also with real-world experiments in which, a quadrocopter-based micro Unmanned Aerial Vehicle autonomously follows a methane trail toward the emitting source. Compared to ground robots, micro UAVs bring several advantages such as their superior steering capabilities and fewer mobility restrictions in complex terrains. The experimental evaluation shows that, under certain environmental conditions, insect like behavior in gas-sensitive UAVs is feasible in real-world environments. }, ISBN = {978-3-319-08338-4}, ISBN = {978-3-319-08337-7}, year = {2016} } @inproceedings{Palm1051090, author = {Palm, Rainer and Chadalavada, Ravi and Lilienthal, Achim}, booktitle = {Proceedings of the 8th International Joint Conference on Computational Intelligence (IJCCI 2016) : }, institution = {Örebro University, School of Science and Technology}, note = {Funding Agency:AIR-project, Action and Intention Recognition in Human Interaction with Autonomous Systems}, pages = {67--74}, title = {Fuzzy Modeling and Control for Intention Recognition in Human-Robot Systems}, volume = {2}, DOI = {10.5220/0006015400670074}, keywords = {Fuzzy control, Fuzzy modeling, Human-Robot interaction, human intentions}, abstract = {The recognition of human intentions from trajectories in the framework of human-robot interaction is a challenging field of research. In this paper some control problems of the human-robot interaction and their intentions to compete or cooperate in shared work spaces are addressed and the time schedule of the information flow is discussed. The expected human movements relative to the robot are summarized in a so-called "compass dial" from which fuzzy control rules for the robot's reactions are derived. To avoid collisions between robot and human very early the computation of collision times at predicted human-robot intersections is discussed and a switching controller for collision avoidance is proposed. In the context of the recognition of human intentions to move to certain goals, pedestrian tracks are modeled by fuzzy clustering, lanes preferred by human agents are identified, and the identification of degrees of membership of a pedestrian track to specific lanes are discussed. Computations based on simulated and experimental data show the applicability of the methods presented. }, ISBN = {978-989-758-201-1}, year = {2016} } @inproceedings{Mosberger1057245, author = {Mosberger, Rafael and Schaffernicht, Erik and Andreasson, Henrik and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, pages = {4131--4136}, title = {Inferring human body posture information from reflective patterns of protective work garments}, DOI = {10.1109/IROS.2016.7759608}, keywords = {Computer Vision, Human Detection, Reflective Clothing, Image Segmentation, Active Illumination, Infrared Vision}, abstract = {We address the problem of extracting human body posture labels, upper body orientation and the spatial location of individual body parts from near-infrared (NIR) images depicting patterns of retro-reflective markers. The analyzed patterns originate from the observation of humans equipped with protective high-visibility garments that represent common safety equipment in the industrial sector. Exploiting the shape of the observed reflectors we adopt shape matching based on the chamfer distance and infer one of seven discrete body posture labels as well as the approximate upper body orientation with respect to the camera. We then proceed to analyze the NIR images on a pixel scale and estimate a figure-ground segmentation together with human body part labels using classification of densely extracted local image patches. Our results indicate a body posture classification accuracy of 80% and figure-ground segmentations with 87% accuracy. }, ISBN = {978-1-5090-3762-9}, year = {2016} } @inproceedings{Palm1051086, author = {Palm, Rainer and Bouguerra, Abdelbaki and Abdullah, Muhammad and Lilienthal, Achim}, booktitle = {SMC 2016 : 2016 IEEE International Conference on Systems, Man, and Cybernetics}, institution = {Örebro University, School of Science and Technology}, institution = {The university of Faisalabad, Faisalabad, Pakistan}, pages = {4489--4494}, title = {Navigation in Human-Robot and Robot-Robot Interaction using Optimization Methods}, keywords = {Human-robot interaction, human intentions, collision avoidance, robot navigation, artificial force fields, market-based optimization}, abstract = {Human-robot interaction and robot-robot interaction and cooperation in shared spatial areas is a challenging field of research regarding safety, stability and performance. In this paper the collision avoidance between human and robot by extrapolation of human intentions and a suitable optimization of tracking velocities is discussed. Furthermore for robot-robot interactions in a shared area traffic rules and artificial force potential fields and their optimization by market-based approach are applied for obstacle avoidance. For testing and verification, the navigation strategy is implemented and tested in simulation of more realistic vehicles. Extensive simulation experiments are performed to examine the improvement of the traditional potential field (PF) method by the MBO strategy. }, ISBN = {978-1-5090-1897-0}, year = {2016} } @article{Stoyanov1044254, author = {Stoyanov, Todor and Vaskevicius, Narunas and Mueller, Christian Atanas and Fromm, Tobias and Krug, Robert and Tincani, Vinicio and Mojtahedzadeh, Rasoul and Kunaschk, Stefan and Ernits, R. Mortensen and Canelhas, Daniel R. and Bonilla, Manuell and Schwertfeger, Soeren and Bonini, Marco and Halfar, Harry and Pathak, Kaustubh and Rohde, Moritz and Fantoni, Gualtiero and Bicchi, Antonio and Birk, Andreas and Lilienthal, Achim J. and Echelmeyer, Wolfgang}, institution = {Örebro University, School of Science and Technology}, institution = {Jacobs University Bremen, Bremen, Germany}, institution = {Jacobs University Bremen, Bremen, Germany}, institution = {Jacobs University Bremen, Bremen, Germany}, institution = {University of Pisa, Pisa, Italy}, institution = {Bremer Institut für Produktion und Logistik (BIBA), Bremen, Germany}, institution = {Bremer Institut für Produktion und Logistik (BIBA), Bremen, Germany}, institution = {University of Pisa, Pisa, Italy}, institution = {ShanghaiTech University, Shanghai, China}, institution = {Reutlingen University, Reutlingen, Germany}, institution = {Reutlingen University, Reutlingen, Germany}, institution = {Jacobs University Bremen, Bremen, Germany}, institution = {Bremer Institut für Produktion und Logistik (BIBA), Bremen, Germany}, institution = {University of Pisa, Pisa, Italy}, institution = {Università di Pisa & Istituto Italiano di Tecnologia, Genova, Italy}, institution = {Jacobs University, Bremen, Germany}, institution = {Reutlingen University, Reutlingen, Germany}, journal = {IEEE robotics & automation magazine}, note = {Funding Agency:EU FP7 project ROBLOG ICT-270350}, number = {4}, pages = {94--106}, title = {No More Heavy Lifting : Robotic Solutions to the Container-Unloading Problem}, volume = {23}, DOI = {10.1109/MRA.2016.2535098}, year = {2016} } @inproceedings{Palm1051078, author = {Palm, Rainer and Chadalavada, Ravi and Lilienthal, Achim}, booktitle = {2016 9th International Conference on Human System Interactions, HSI 2016 : Proceedings}, institution = {Örebro University, School of Science and Technology}, note = {Funding Agency:AIR-project Action and Intention Recognition in Human Interaction with Autonomous Systems}, pages = {229--235}, title = {Recognition of Human-Robot Motion Intentions by Trajectory Observation}, series = {Conference on Human System Interaction}, DOI = {10.1109/HSI.2016.7529636}, keywords = {Human robot interaction, human intentions, obstacle avoidance, fuzzy rules}, abstract = {The intention of humans and autonomous robots to interact in shared spatial areas is a challenging field of research regarding human safety, system stability and performance of the system's behavior. In this paper the intention recognition between human and robot from the control point of view are addressed and the time schedule of the exchanged signals is discussed. After a description of the kinematic and geometric relations between human and robot a so-called 'compass dial' with the relative velocities is presented from which suitable fuzzy control rules are derived. The computation of the collision times at intersections and possible avoidance strategies are further discussed. Computations based on simulated and experimental data show the applicability of the methods presented. }, ISBN = {9781509017294}, year = {2016} } @inproceedings{Bunz1071024, author = {Bunz, Elsa and Chadalavada, Ravi Teja and Andreasson, Henrik and Krug, Robert and Schindler, Maike and Lilienthal, Achim}, booktitle = {Proceedings of RO-MAN 2016 Workshop : Workshop on Communicating Intentions in Human-Robot Interaction}, institution = {Örebro University, School of Science and Technology}, institution = {Örebro University, Örebro, Sweden}, title = {Spatial Augmented Reality and Eye Tracking for Evaluating Human Robot Interaction}, abstract = {Freely moving autonomous mobile robots may leadto anxiety when operating in workspaces shared with humans.Previous works have given evidence that communicating in-tentions using Spatial Augmented Reality (SAR) in the sharedworkspace will make humans more comfortable in the vicinity ofrobots. In this work, we conducted experiments with the robotprojecting various patterns in order to convey its movementintentions during encounters with humans. In these experiments,the trajectories of both humans and robot were recorded witha laser scanner. Human test subjects were also equipped withan eye tracker. We analyzed the eye gaze patterns and thelaser scan tracking data in order to understand how the robot’sintention communication affects the human movement behavior.Furthermore, we used retrospective recall interviews to aid inidentifying the reasons that lead to behavior changes. }, year = {2016} } @inproceedings{Triebel950081, author = {Triebel, Rudolph and Arras, Kai and Alami, Rachid and Beyer, Lucas and Breuers, Stefan and Chatila, Raja and Chetouani, Mohamed and Cremers, Daniel and Evers, Vanessa and Fiore, Michelangelo and Hung, Hayley and Ramirez, Omar A. Islas and Joosse, Michiel and Khambhaita, Harmish and Kucner, Tomasz and Leibe, Bastian and Lilienthal, Achim J. and Linder, Timm and Lohse, Manja and Magnusson, Martin and Okal, Billy and Palmieri, Luigi and Rafi, Umer and van Rooij, Marieke and Zhang, Lu}, booktitle = {Field and Service Robotics : Results of the 10th International Conference}, institution = {Örebro University, School of Science and Technology}, institution = {Department of Computer Science, Technische Universität München, Munich, Germany}, institution = {Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany}, institution = {Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), Toulouse, France}, institution = {Rheinisch-Westfälische Technische Hochschule, Aachen, Germany}, institution = {Rheinisch-Westfälische Technische Hochschule, Aachen, Germany}, institution = {Institute for Intelligent Systems and Robotics (ISIR-CNRS), Paris, France}, institution = {Institute for Intelligent Systems and Robotics (ISIR-CNRS), Paris, France}, institution = {Department of Computer Science, Technische Universität München, Munich, Germany}, institution = {University of Twente, Enschede, Netherlands}, institution = {Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), Toulouse, France}, institution = {Delft University of Technology, Delft, Netherlands}, institution = {Institute for Intelligent Systems and Robotics (ISIR-CNRS), Paris, France}, institution = {University of Twente, Enschede, Netherlands}, institution = {Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), Toulouse, France}, institution = {Rheinisch-Westfälische Technische Hochschule, Aachen, Germany}, institution = {Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany}, institution = {University of Twente, Enschede, Netherlands}, institution = {Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany}, institution = {Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany}, institution = {Rheinisch-Westfälische Technische Hochschule, Aachen, Germany}, institution = {University of Amsterdam, Amsterdam, Netherlands}, institution = {University of Twente, Enschede, Netherlands; Delft University of Technology, Delft, Netherlands}, pages = {607--622}, title = {SPENCER : A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports}, series = {Springer Tracts in Advanced Robotics}, number = {113}, DOI = {10.1007/978-3-319-27702-8_40}, abstract = {We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in real-time for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors. }, ISBN = {978-3-319-27702-8}, ISBN = {978-3-319-27700-4}, year = {2016} } @inproceedings{Kucner1070733, author = {Kucner, Tomasz and Magnusson, Martin and Schaffernicht, Erik and Hernandez Bennetts, Victor and Lilienthal, Achim}, booktitle = {Robotics : Science and Systems Conference (RSS 2016)}, institution = {Örebro University, School of Science and Technology}, title = {Tell me about dynamics! : Mapping velocity fields from sparse samples with Semi-Wrapped Gaussian Mixture Models}, abstract = {Autonomous mobile robots often require informa-tion about the environment beyond merely the shape of thework-space. In this work we present a probabilistic method formappingdynamics, in the sense of learning and representingstatistics about the flow of discrete objects (e.g., vehicles, people)as well as continuous media (e.g., air flow). We also demonstratethe capabilities of the proposed method with two use cases. Onerelates to motion planning in populated environments, whereinformation about the flow of people can help robots to followsocial norms and to learn implicit traffic rules by observingthe movements of other agents. The second use case relates toMobile Robot Olfaction (MRO), where information about windflow is crucial for most tasks, including e.g. gas detection, gasdistribution mapping and gas source localisation. We representthe underlying velocity field as a set of Semi-Wrapped GaussianMixture Models (SWGMM) representing the learnt local PDF ofvelocities. To estimate the parameters of the PDF we employ aformulation of Expectation Maximisation (EM) algorithm specificfor SWGMM. We also describe a data augmentation methodwhich allows to build a dense dynamic map based on a sparseset of measurements. In case only a small set of observations isavailable we employ a hierarchical sampling method to generatevirtual observations from existing mixtures. }, year = {2016} } @article{Krug1044259, author = {Krug, Robert and Stoyanov, Todor and Tincani, Vinicio and Andreasson, Henrik and Mosberger, Rafael and Fantoni, Gualtiero and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {University of Pisa, Pisa, Italy}, institution = { University of Pisa, Pisa, Italy}, journal = {IEEE Robotics and Automation Letters}, number = {1}, pages = {546--553}, title = {The Next Step in Robot Commissioning : Autonomous Picking and Palletizing}, volume = {1}, DOI = {10.1109/LRA.2016.2519944}, keywords = {Logistics, grasping, autonomous vehicle navigation, robot safety, mobile manipulation}, abstract = {So far, autonomous order picking (commissioning) systems have not been able to meet the stringent demands regarding speed, safety, and accuracy of real-world warehouse automation, resulting in reliance on human workers. In this letter, we target the next step in autonomous robot commissioning: automatizing the currently manual order picking procedure. To this end, we investigate the use case of autonomous picking and palletizing with a dedicated research platform and discuss lessons learned during testing in simplified warehouse settings. The main theoretical contribution is a novel grasp representation scheme which allows for redundancy in the gripper pose placement. This redundancy is exploited by a local, prioritized kinematic controller which generates reactive manipulator motions on-the-fly. We validated our grasping approach by means of a large set of experiments, which yielded an average grasp acquisition time of 23.5 s at a success rate of 94.7%. Our system is able to autonomously carry out simple order picking tasks in a humansafe manner, and as such serves as an initial step toward future commercial-scale in-house logistics automation solutions. }, year = {2016} } @inproceedings{Arain938083, author = {Arain, Muhammad Asif and Schaffernicht, Erik and Hernandez Bennetts, Victor and Lilienthal, Achim J.}, booktitle = {2016 IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, pages = {4275--4281}, title = {The Right Direction to Smell : Efficient Sensor Planning Strategies for Robot Assisted Gas Tomography}, DOI = {10.1109/ICRA.2016.7487624}, keywords = {Sensor planning, robot exploration, sensing geometry, robot assisted gas tomography, mobile robot olfaction, coverage planning, surveillance robots}, abstract = {Creating an accurate model of gas emissions is an important task in monitoring and surveillance applications. A promising solution for a range of real-world applications are gas-sensitive mobile robots with spectroscopy-based remote sensors that are used to create a tomographic reconstruction of the gas distribution. The quality of these reconstructions depends crucially on the chosen sensing geometry. In this paper we address the problem of sensor planning by investigating sensing geometries that minimize reconstruction errors, and then formulate an optimization algorithm that chooses sensing configurations accordingly. The algorithm decouples sensor planning for single high concentration regions (hotspots) and subsequently fuses the individual solutions to a global solution consisting of sensing poses and the shortest path between them. The proposed algorithm compares favorably to a template matching technique in a simple simulation and in a real-world experiment. In the latter, we also compare the proposed sensor planning strategy to the sensing strategy of a human expert and find indications that the quality of the reconstructed map is higher with the proposed algorithm. }, year = {2016} } @inproceedings{HernandezBennetts1070802, author = {Hernandez Bennetts, Victor and Schaffernicht, Erik and Lilienthal, Achim J. and Fan, Han and Kucner, Tomasz Piotr and Andersson, Lena and Johansson, Anders}, booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, institution = {Department of Occupational and Environmental Medicine, Örebro University Hospital, Örebro, Sweden}, institution = {Department of Occupational and Environmental Medicine, Örebro University Hospital, Örebro, Sweden}, pages = {131--136}, eid = {7759045}, title = {Towards occupational health improvement in foundries through dense dust and pollution monitoring using a complementary approach with mobile and stationary sensing nodes}, DOI = {10.1109/IROS.2016.7759045}, keywords = {Occupational Health; Mobile Robot Olfaction; Pollution Monitoring}, abstract = {In industrial environments, such as metallurgic facilities, human operators are exposed to harsh conditions where ambient air is often polluted with quartz, dust, lead debris and toxic fumes. Constant exposure to respirable particles can cause irreversible health damages and thus it is of high interest for occupational health experts to monitor the air quality on a regular basis. However, current monitoring procedures are carried out sparsely, with data collected in single day campaigns limited to few measurement locations. In this paper we explore the use and present first experimental results of a novel heterogeneous approach that uses a mobile robot and a network of low cost sensing nodes. The proposed system aims to address the spatial and temporal limitations of current monitoring techniques. The mobile robot, along with standard localization and mapping algorithms, allows to produce short term, spatially dense representations of the environment where dust, gas, ambient temperature and airflow information can be modelled. The sensing nodes on the other hand, can collect temporally dense (and usually spatially sparse) information during long periods of time, allowing in this way to register for example, daily variations in the pollution levels. Using data collected with the proposed system in an steel foundry, we show that a heterogeneous approach provides dense spatio-temporal information that can be used to improve the working conditions in industrial facilities. }, ISBN = {9781509037629}, year = {2016} } @inproceedings{Siddiqui945980, author = {Siddiqui, J. Rafid and Andreasson, Henrik and Driankov, Dimiter and Lilienthal, Achim J.}, booktitle = {2016 IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, pages = {5766--5773}, eid = {7487800}, title = {Towards visual mapping in industrial environments : a heterogeneous task-specific and saliency driven approach}, series = {IEEE International Conference on Robotics and Automation}, DOI = {10.1109/ICRA.2016.7487800}, keywords = {Image color analysis, Object detection, Robot sensing systems, Service robots, Training, Visualization}, abstract = {The highly percipient nature of human mind in avoiding sensory overload is a crucial factor which gives human vision an advantage over machine vision, the latter has otherwise powerful computational resources at its disposal given today’s technology. This stresses the need to focus on methods which extract a concise representation of the environment inorder to approach a complex problem such as visual mapping. This article is an attempt of creating a mapping system, which proposes an architecture that combines task-specific and saliency driven approaches. The proposed method is implemented on a warehouse robot. The proposed solution provide a priority framework which enables an industrial robot to build a concise visual representation of the environment. The method is evaluated on data collected by a RGBD sensor mounted on a fork-lift robot and shows promise for addressing visual mapping problems in industrial environments. }, ISBN = {978-146738026-3}, year = {2016} } @inproceedings{Fan1057307, author = {Fan, Han and Hernandez Bennetts, Victor and Schaffernicht, Erik and Lilienthal, Achim J.}, booktitle = {2016 IEEE SENSORS : }, institution = {Örebro University, School of Science and Technology}, note = {Funding Agency:ICT by the European Commission  645101}, title = {Unsupervised gas discrimination in uncontrolled environments by exploiting density peaks}, series = {Proceedings of IEEE Sensors}, DOI = {10.1109/ICSENS.2016.7808903}, keywords = {gas discrimination, Open Sampling Systems, metal oxide sensors, unsupervised learning}, abstract = {Gas discrimination with Open Sampling Systems based on low-cost electro-chemical sensor arrays is of great interest in several applications, such as exploration of hazardous areas and environmental monitoring. Due to the lack of labeled training data or the high costs of obtaining them, as well as the presence of unknown interferents in the target environments, supervised learning is often not applicable and thus, unsupervised learning is an interesting alternative. In this work, we present a cluster analysis approach that can infer the number of different chemical compounds and label the measurements in a given uncontrolled environment without relying on previously acquired training data. Our approach is validated with data collected in indoor and outdoor environments by a mobile robot equipped with an array of metal oxide sensors. The results show that high classification accuracy can be achieved with a rather low sensitivity to the selection of the only functional parameter of our proposed algorithm.  }, ISBN = {978-1-4799-8287-5}, year = {2016} } @incollection{Ishida1070680, author = {Ishida, Hiroshi and Lilienthal, Achim J. and Matsukura, Haruka and Hernandez Bennetts, Victor and Schaffernicht, Erik}, booktitle = {Essentials of Machine Olfaction and Taste : }, institution = {Örebro University, School of Science and Technology}, institution = {Tokyo University of Agriculture and Technology, Tokyo, Japan}, institution = {Tokyo University of Agriculture and Technology, Tokyo, Japan}, pages = {219--246}, title = {Using Chemical Sensors as 'Noses' for Mobile Robots}, abstract = {Gas sensors detect the presence of gaseous chemical compounds in air. They are often used in the form of gas alarms for detecting dangerous or hazardous gases. However, a limited number of stationary gas alarms may not be always sufficient to cover a large industrial facility. Human workers having a portable gas detector in their hand needs to be sent to thoroughly check gas leaks in the areas not covered by stationary gas alarms. However, making repetitive measurements with a gas detector at a number of different locations is laborious. Moreover, the places where the gas concentration level needs to be checked are often potentially dangerous for human workers. If a portable gas detector is mounted on a mobile robot, the task of patrolling in an industrial facility for checking a gas leak can be automated. Robots are good at doing repetitive tasks, and can be sent into harsh environments. }, ISBN = {9781118768488}, ISBN = {9781118768518}, year = {2016} } @inproceedings{Mielle1054805, author = {Mielle, Malcolm and Magnusson, Martin and Lilienthal, Achim J.}, booktitle = {2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) : }, institution = {Örebro University, School of Science and Technology}, pages = {252--257}, title = {Using sketch-maps for robot navigation : interpretation and matching}, DOI = {10.1109/SSRR.2016.7784307}, keywords = {sketch, sketch-map, human robot interface, HRI, graph matching}, abstract = {We present a study on sketch-map interpretationand sketch to robot map matching, where maps have nonuniform scale, different shapes or can be incomplete. For humans, sketch-maps are an intuitive way to communicate navigation information, which makes it interesting to use sketch-maps forhuman robot interaction; e.g., in emergency scenarios. To interpret the sketch-map, we propose to use a Voronoi diagram that is obtained from the distance image on which a thinning parameter is used to remove spurious branches. The diagram is extracted as a graph and an efficient error-tolerant graph matching algorithm is used to find correspondences, while keeping time and memory complexity low. A comparison against common algorithms for graph extraction shows that our method leads to twice as many good matches. For simple maps, our method gives 95% good matches even for heavily distorted sketches, and for a more complex real-world map, up to 58%. This paper is a first step toward using unconstrained sketch-maps in robot navigation. }, ISBN = {978-1-5090-4349-1}, year = {2016} } @inproceedings{Mojtahedzadeh900560, author = {Mojtahedzadeh, Rasoul and Lilienthal, Achim J.}, booktitle = {2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, pages = {2897--2903}, title = {A principle of minimum translation search approach for object pose refinement}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2015.7353776}, keywords = {Pose estimation, robot vision, search problems, A-star search, PROMTS, cluttered environments, depth-limited search, detected poses, geometrically consistent objects configuration, inaccurate noisy poses, interpenetration-free configuration, minimum translation search, minimum translation search approach, object pose estimation approaches, object pose refinement, overlapping objects, pose estimation accuracy, rigid body assumption, shipping containers, Containers, Search problems, Shape, Solid modeling, Three-dimensional displays, Uncertainty}, abstract = {The state-of-the-art object pose estimation approaches represent the set of detected poses together with corresponding uncertainty. The inaccurate noisy poses may result in a configuration of overlapping objects especially in cluttered environments. Under a rigid body assumption the inter-penetrations between pairs of objects are geometrically inconsistent. In this paper, we propose the principle of minimum translation search, PROMTS, to find an inter-penetration-free configuration of the initially detected objects. The target application is to automate the task of unloading shipping containers, where a geometrically consistent configuration of objects is required for high level reasoning and manipulation. We find that the proposed approach to resolve geometrical inconsistencies improves the overall pose estimation accuracy. We examine the utility of two selected search methods: A-star and Depth-Limited search. The performance of the search algorithms are tested on data sets generated in simulation and from real-world scenarios. The results show overall improvement of the estimated poses and suggest that depth-limited search presents the best overall performance. }, ISBN = {978-1-4799-9994-1}, year = {2015} } @inproceedings{Asadi957520, author = {Asadi, Sahar and Lilienthal, Achim}, booktitle = {2015 European Conference on Mobile Robots (ECMR) : }, institution = {Örebro University, School of Science and Technology}, eid = {7324215}, publisher = {IEEE conference proceedings}, title = {Approaches to Time-Dependent Gas Distribution Modelling}, DOI = {10.1109/ECMR.2015.7324215}, keywords = {Dispersion; Kernel; Pollution measurement; Predictive models; Robot sensing systems; Time measurement; Weight measurement}, abstract = {Mobile robot olfaction solutions for gas distribution modelling offer a number of advantages, among them autonomous monitoring in different environments, mobility to select sampling locations, and ability to cooperate with other systems. However, most data-driven, statistical gas distribution modelling approaches assume that the gas distribution is generated by a time-invariant random process. Such time-invariant approaches cannot model well developing plumes or fundamental changes in the gas distribution. In this paper, we discuss approaches that explicitly consider the measurement time, either by sub-sampling according to a given time-scale or by introducing a recency weight that relates measurement and prediction time. We evaluate the performance of these time-dependent approaches in simulation and in real-world experiments using mobile robots. The results demonstrate that in dynamic scenarios improved gas distribution models can be obtained with time-dependent approaches. }, ISBN = {978-1-4673-9163-4}, year = {2015} } @article{Andreasson807693, author = {Andreasson, Henrik and Bouguerra, Abdelbaki and Cirillo, Marcello and Dimitrov, Dimitar Nikolaev and Driankov, Dimiter and Karlsson, Lars and Lilienthal, Achim J. and Pecora, Federico and Saarinen, Jari Pekka and Sherikov, Aleksander and Stoyanov, Todor}, institution = {Örebro University, School of Science and Technology}, institution = {INRIA - Grenoble, Meylan, France}, institution = {Aalto University, Espo, Finland }, institution = {Centre de recherche Grenoble Rhône-Alpes, Grenoble, France }, journal = {IEEE robotics & automation magazine}, number = {1}, pages = {64--75}, title = {Autonomous transport vehicles : where we are and what is missing}, volume = {22}, DOI = {10.1109/MRA.2014.2381357}, keywords = {Intelligent vehicles; Mobile robots; Resource management; Robot kinematics; Trajectory; Vehicle dynamics}, abstract = {In this article, we address the problem of realizing a complete efficient system for automated management of fleets of autonomous ground vehicles in industrial sites. We elicit from current industrial practice and the scientific state of the art the key challenges related to autonomous transport vehicles in industrial environments and relate them to enabling techniques in perception, task allocation, motion planning, coordination, collision prediction, and control. We propose a modular approach based on least commitment, which integrates all modules through a uniform constraint-based paradigm. We describe an instantiation of this system and present a summary of the results, showing evidence of increased flexibility at the control level to adapt to contingencies. }, year = {2015} } @inproceedings{Khaliq900440, author = {Khaliq, Ali and Pashami, Sepideh and Schaffernicht, Erik and Lilienthal, Achim J. and Hernandez Bennetts, Victor}, booktitle = {Proceedings of the 16th International Symposium on Olfaction and Electronic Noses : }, institution = {Örebro University, School of Science and Technology}, eid = {137}, title = {Bringing Artificial Olfaction and Mobile Robotics Closer Together : An Integrated 3D Gas Dispersion Simulator in ROS}, keywords = {Mobile robot olfaction, gas dispersion simulation, gas sensor simulation, MOX sensors, environmental monitoring}, abstract = {Despite recent achievements, the potential of gas-sensitive mobile robots cannot be realized due to the lack of research on fundamental questions. A key limitation is the difficulty to carry out evaluations against ground truth. To test and compare approaches for gas-sensitive robots a truthful gas dispersion simulator is needed. In this paper we present a unified framework to simulate gas dispersion and to evaluate mobile robotics and gas sensing algorithms using ROS. Gas dispersion is modeled as a set of particles affected by diffusion, turbulence, advection and gravity. Wind information is integrated as time snapshots computed with any fluid dynamics computation tool. In addition, response models for devices such as Metal Oxide (MOX) sensors can be integrated in the framework. }, URL = {https://www.eventing.hu/bazar/ISOEN2015-AbstractBook.pdf}, year = {2015} } @inproceedings{Arain874039, author = {Arain, Muhammad Asif and Cirillo, Marcello and Hernandez Bennetts, Victor and Schaffernicht, Erik and Trincavelli, Marco and Lilienthal, Achim J.}, booktitle = {2015 IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, institution = {Scania AB, Södertälje, Sweden}, pages = {3428--3434}, title = {Efficient Measurement Planning for Remote Gas Sensing with Mobile Robots}, DOI = {10.1109/ICRA.2015.7139673}, keywords = {Sensor planning, mobile robot olfaction, remote gas sensing}, abstract = {The problem of gas detection is relevant to manyreal-world applications, such as leak detection in industrialsettings and surveillance. In this paper we address the problemof gas detection in large areas with a mobile robotic platformequipped with a remote gas sensor. We propose a novelmethod based on convex relaxation for quickly finding anexploration plan that guarantees a complete coverage of theenvironment. Our method proves to be highly efficient in termsof computational requirements and to provide nearly-optimalsolutions. We validate our approach both in simulation andin real environments, thus demonstrating its applicability toreal-world problems. }, ISBN = {978-1-4799-6923-4}, year = {2015} } @inproceedings{Andreasson894653, author = {Andreasson, Henrik and Saarinen, Jari and Cirillo, Marcello and Stoyanov, Todor and Lilienthal, Achim}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA), 2015 : }, institution = {Örebro University, School of Science and Technology}, institution = {SCANIA AB, Södertälje, Sweden}, pages = {662--669}, title = {Fast, continuous state path smoothing to improve navigation accuracy}, series = {Proceedings - IEEE International Conference on Robotics and Automation}, DOI = {10.1109/ICRA.2015.7139250}, abstract = {Autonomous navigation in real-world industrial environments is a challenging task in many respects. One of the key open challenges is fast planning and execution of trajectories to reach arbitrary target positions and orientations with high accuracy and precision, while taking into account non-holonomic vehicle constraints. In recent years, lattice-based motion planners have been successfully used to generate kinematically and kinodynamically feasible motions for non-holonomic vehicles. However, the discretized nature of these algorithms induces discontinuities in both state and control space of the obtained trajectories, resulting in a mismatch between the achieved and the target end pose of the vehicle. As endpose accuracy is critical for the successful loading and unloading of cargo in typical industrial applications, automatically planned paths have not be widely adopted in commercial AGV systems. The main contribution of this paper addresses this shortcoming by introducing a path smoothing approach, which builds on the output of a lattice-based motion planner to generate smooth drivable trajectories for non-holonomic industrial vehicles. In real world tests presented in this paper we demonstrate that the proposed approach is fast enough for online use (it computes trajectories faster than they can be driven) and highly accurate. In 100 repetitions we achieve mean end-point pose errors below 0.01 meters in translation and 0.002 radians in orientation. Even the maximum errors are very small: only 0.02 meters in translation and 0.008 radians in orientation. }, ISBN = {9781479969234}, year = {2015} } @article{Arain807075, author = {Arain, Muhammad Asif and Trincavelli, Marco and Cirillo, Marcello and Schaffernicht, Erik and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Sensors}, number = {3}, pages = {6845--6871}, title = {Global coverage measurement planning strategies for mobile robots equipped with a remote gas sensor}, volume = {15}, DOI = {10.3390/s150306845}, keywords = {Coverage planning, Mobile robot olfaction, Remote gas detection, Sensor planning, Surveillance robots}, abstract = {The problem of gas detection is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. In this paper, we address the problem of gas detection in large areas with a mobile robotic platform equipped with a remote gas sensor. We propose an algorithm that leverages a novel method based on convex relaxation for quickly solving sensor placement problems, and for generating an efficient exploration plan for the robot. To demonstrate the applicability of our method to real-world environments, we performed a large number of experimental trials, both on randomly generated maps and on the map of a real environment. Our approach proves to be highly efficient in terms of computational requirements and to provide nearly-optimal solutions. }, year = {2015} } @inproceedings{Krug842706, author = {Krug, Robert and Stoyanov, Todor and Lilienthal, Achim}, booktitle = {Robotics: Science and Systems Conference : Workshop on Bridging the Gap between Data-driven and Analytical Physics-based Grasping and Manipulation}, institution = {Örebro University, School of Science and Technology}, title = {Grasp Envelopes for Constraint-based Robot Motion Planning and Control}, keywords = {Grasping, Grasp Control, Motion Control}, abstract = {We suggest a grasp represen-tation in form of a set of enveloping spatial constraints. Our representation transforms the grasp synthesisproblem (i. e., the question of where to position the graspingdevice) from finding a suitable discrete manipulator wrist pose to finding a suitable pose manifold. Also the correspondingmotion planning and execution problem is relaxed – insteadof transitioning the wrist to a discrete pose, it is enough tomove it anywhere within the grasp envelope which allows toexploit kinematic redundancy. }, year = {2015} } @inproceedings{HernandezBennetts900844, author = {Hernandez Bennetts, Victor and Lilienthal, Achim J. and Schaffernicht, Erik and Ferrari, Silvia and Albertson, John}, booktitle = {Workshop on Realistic, Rapid and Repeatable Robot Simulation : }, institution = {Örebro University, School of Science and Technology}, institution = {Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca NY, USA}, institution = {School of Civil and Environmental Engineering, Cornell University, Ithaca NY, USA}, title = {Integrated Simulation of Gas Dispersion and Mobile Sensing Systems}, keywords = {Robot simulatior, gas dispersion simulation, metal oxide sensors}, abstract = {Accidental or intentional releases of contaminants into the atmosphere pose risks to human health, the environment, the economy, and national security. In some cases there may be a single release from an unknown source, while in other cases there are fugitive emissions from multiple sources. The need to locate and characterize the sources efficiently - whether it be the urgent need to evacuate or the systematic need to cover broad geographical regions with limited resources - is shared among all cases. Efforts have begun to identify leaks with gas analyzers mounted on Mobile Robot Olfaction (MRO) systems, road vehicles, and networks of fixed sensors, such as may be based in urban environments. To test and compare approaches for gas-sensitive robots a truthful gas dispersion simulator is needed. In this paper, we present a unified framework to simulate gas dispersion and to evaluate mobile robotics and gas sensing technologies using ROS. This framework is also key to developing and testing optimization and planning algorithms for determining sensor placement and sensor motion, as well as for fusing and connecting the sensor measurements to the leak locations. }, year = {2015} } @inproceedings{Mosberger891476, author = {Mosberger, Rafael and Leibe, Bastian and Andreasson, Henrik and Lilienthal, Achim}, booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, institution = {Aachen University, Aachen, Germany}, pages = {697--703}, title = {Multi-band Hough Forests for detecting humans with Reflective Safety Clothing from mobile machinery}, series = {Proceedings - IEEE International Conference on Robotics and Automation}, DOI = {10.1109/ICRA.2015.7139255}, keywords = {Human Detection, Robot Vision, Industrial Safety}, abstract = {We address the problem of human detection from heavy mobile machinery and robotic equipment operating at industrial working sites. Exploiting the fact that workers are typically obliged to wear high-visibility clothing with reflective markers, we propose a new recognition algorithm that specifically incorporates the highly discriminative features of the safety garments in the detection process. Termed Multi-band Hough Forest, our detector fuses the input from active near-infrared (NIR) and RGB color vision to learn a human appearance model that not only allows us to detect and localize industrial workers, but also to estimate their body orientation. We further propose an efficient pipeline for automated generation of training data with high-quality body part annotations that are used in training to increase detector performance. We report a thorough experimental evaluation on challenging image sequences from a real-world production environment, where persons appear in a variety of upright and non-upright body positions. }, ISBN = {978-1-4799-6923-4}, year = {2015} } @inproceedings{Krug808145, author = {Krug, Robert and Stoyanov, Todor and Tincani, Vinicio and Andreasson, Henrik and Mosberger, Rafael and Fantoni, Gualtiero and Bicchi, Antonio and Lilienthal, Achim}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA) - Workshop on Robotic Hands, Grasping, and Manipulation : }, institution = {Örebro University, School of Science and Technology}, institution = {Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy}, institution = {Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy}, institution = {Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy}, title = {On Using Optimization-based Control instead of Path-Planning for Robot Grasp Motion Generation}, keywords = {Grasping, Motion Planning, Control}, year = {2015} } @inproceedings{Magnusson849536, author = {Magnusson, Martin and Kucner, Tomasz and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE) : }, institution = {Örebro University, School of Science and Technology}, pages = {450--455}, publisher = {IEEE conference proceedings}, title = {Quantitative Evaluation of Coarse-To-Fine Loading Strategies for Material Rehandling}, series = {IEEE International Conference on Automation Science and Engineering (CASE)}, DOI = {10.1109/CoASE.2015.7294120}, keywords = {Emerging Topics in Automation, Automation for Machine Tools, Sustainable Production}, abstract = {Autonomous handling of piled materials is an emerging topic in automation science and engineering. A central question for material rehandling tasks (transporting materials that have been assembled in piles) is “where to dig, in order to optimise performance”? In particular, we are interested in the application of autonomous wheel loaders to handle piles of gravel. Still, the methodology proposed in this paper relates to granular materials in other applications too. Although initial work on suggesting strategies for where to dig has been done by a few other groups, there has been a lack of structured evaluation of the usefulness of the proposed strategies. In an attempt to further the field, we present a quantitative evaluation of loading strategies; both coarse ones, aiming to maintain a good pile shape over long-term operation; and refined ones, aiming to detect the locally best attack pose for acquiring a good fill grade in the bucket. Using real-world data from a semi-automated test platform, we present an assessment of how previously proposed pile shape measures can be mapped to the amount of material in the bucket after loading. We also present experimental data for long-term strategies, using simulations based on real-world 3D scan data from a production site. }, ISBN = {978-1-4673-8183-3}, year = {2015} } @inproceedings{Tincani900484, author = {Tincani, Vinicio and Catalano, Manuel and Grioli, Giorgio and Stoyanov, Todor and Krug, Robert and Lilienthal, Achim J. and Fantoni, Gualtiero and Bicchi, Antonio}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {University of Pisa, Pisa, Italy}, institution = {University of Pisa, Pisa, Italy}, institution = {University of Pisa, Pisa, Italy}, institution = {University of Pisa, Pisa, Italy}, institution = {University of Pisa, Pisa, Italy; Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genova, Italy}, pages = {2744--2750}, title = {Sensitive Active Surfaces on the Velvet II Dexterous Gripper}, URL = {https://www.ias.informatik.tu-darmstadt.de/uploads/Workshops/ICRA2015TactileForce/03_icra_ws_tactileforce.pdf}, year = {2015} } @article{Mojtahedzadeh778509, author = {Mojtahedzadeh, Rasoul and Bouguerra, Abdelbaki and Schaffernicht, Erik and Lilienthal, Achim J}, institution = {Örebro University, School of Science and Technology}, journal = {Robotics and Autonomous Systems}, number = {SI}, pages = {99--117}, title = {Support relation analysis and decision making for safe robotic manipulation tasks}, volume = {71}, DOI = {10.1016/j.robot.2014.12.014}, keywords = {Scene analysis, Machine learning, Decision making, World models, Robotic manipulation}, abstract = {In this article, we describe an approach to address the issue of automatically building and using high-level symbolic representations that capture physical interactions between objects in static configurations. Our work targets robotic manipulation systems where objects need to be safely removed from piles that come in random configurations. We assume that a 3D visual perception module exists so that objects in the piles can be completely or partially detected. Depending on the outcome of the perception, we divide the issue into two sub-issues: 1) all objects in the configuration are detected; 2) only a subset of objects are correctly detected. For the first case, we use notions from geometry and static equilibrium in classical mechanics to automatically analyze and extract act and support relations between pairs of objects. For the second case, we use machine learning techniques to estimate the probability of objects supporting each other. Having the support relations extracted, a decision making process is used to identify which object to remove from the configuration so that an expected minimum cost is optimized. The proposed methods have been extensively tested and validated on data sets generated in simulation and from real world configurations for the scenario of unloading goods from shipping containers. }, year = {2015} } @inproceedings{Chadalavada900532, author = {Chadalavada, Ravi Teja and Andreasson, Henrik and Krug, Robert and Lilienthal, Achim}, booktitle = {2015 European Conference on Mobile Robots (ECMR) : }, institution = {Örebro University, School of Science and Technology}, publisher = {IEEE conference proceedings}, title = {That’s on my Mind! : Robot to Human Intention Communication through on-board Projection on Shared Floor Space}, DOI = {10.1109/ECMR.2015.7403771}, keywords = {Human Robot Interaction, Intention Communication, Shared spaces}, abstract = {The upcoming new generation of autonomous vehicles for transporting materials in industrial environments will be more versatile, flexible and efficient than traditional AGVs, which simply follow pre-defined paths. However, freely navigating vehicles can appear unpredictable to human workers and thus cause stress and render joint use of the available space inefficient. Here we address this issue and propose on-board intention projection on the shared floor space for communication from robot to human. We present a research prototype of a robotic fork-lift equipped with a LED projector to visualize internal state information and intents. We describe the projector system and discuss calibration issues. The robot’s ability to communicate its intentions is evaluated in realistic situations where test subjects meet the robotic forklift. The results show that already adding simple information, such as the trajectory and the space to be occupied by the robot in the near future, is able to effectively improve human response to the robot. }, ISBN = {978-1-4673-9163-4}, year = {2015} } @inproceedings{Tincani900487, author = {Tincani, Vinicio and Stoyanov, Todor and Krug, Robert and Catalano, Manuel and Grioli, Giorgio and Lilienthal, Achim J. and Fantoni, Gualtiero and Bicchi, Antonio}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {University of Pisa, Pisa, Italy}, institution = {University of Pisa, Pisa, Italy}, institution = {University of Pisa, Pisa, Italy}, institution = {University of Pisa, Pisa, Italy}, institution = {Istituto Italiano di Tecnologia, Genova, Italy}, title = {The Grasp Acquisition Strategy of the Velvet II}, year = {2015} } @inproceedings{Kucner957495, author = {Kucner, Tomasz Piotr and Magnusson, Martin and Lilienthal, Achim J.}, booktitle = {2015 European Conference on Mobile Robots (ECMR) : }, institution = {Örebro University, School of Science and Technology}, publisher = {IEEE conference proceedings}, title = {Where am I? : An NDT-based prior for MCL}, DOI = {10.1109/ECMR.2015.7324175}, abstract = {One of the key requirements of autonomous mobile robots is a robust and accurate localisation system. Recent advances in the development of Monte Carlo Localisation (MCL) algorithms, especially the Normal Distribution Transform Monte Carlo Localisation (NDT-MCL), provides memory-efficient reliable localisation with industry-grade precision. We propose an approach for building an informed prior for NDT-MCL (in fact for any MCL algorithm) using an initial observation of the environment and its map. Leveraging on the NDT map representation, we build a set of poses using partial observations. After that we construct a Gaussian Mixture Model (GMM) over it. Next we obtain scores for each distribution in GMM. In this way we obtain in an efficient way a prior for NDT-MCL. Our approach provides a more focused then uniform initial distribution, concentrated in states where the robot is more likely to be, by building a Gaussian mixture model over potential poses. We present evaluations and quantitative results using real-world data from an indoor environment. Our experiments show that, compared to a uniform prior, the proposed method significantly increases the number of successful initialisations of NDT-MCL and reduces the time until convergence, at a negligible initial cost for computing the prior. }, ISBN = {978-1-4673-9163-4}, year = {2015} } @article{Mosberger772165, author = {Mosberger, Rafael and Andreasson, Henrik and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Sensors}, number = {10}, pages = {17952--17980}, title = {A customized vision system for tracking humans wearing reflective safety clothing from industrial vehicles and machinery}, volume = {14}, DOI = {10.3390/s141017952}, keywords = {infrared vision, human detection, industrial safety, high-visibility clothing}, abstract = {This article presents a novel approach for vision-based detection and tracking of humans wearing high-visibility clothing with retro-reflective markers. Addressing industrial applications where heavy vehicles operate in the vicinity of humans, we deploy a customized stereo camera setup with active illumination that allows for efficient detection of the reflective patterns created by the worker's safety garments. After segmenting reflective objects from the image background, the interest regions are described with local image feature descriptors and classified in order to discriminate safety garments from other reflective objects in the scene. In a final step, the trajectories of the detected humans are estimated in 3D space relative to the camera. We evaluate our tracking system in two industrial real-world work environments on several challenging video sequences. The experimental results indicate accurate tracking performance and good robustness towards partial occlusions, body pose variation, and a wide range of different illumination conditions. }, year = {2014} } @inproceedings{HernandezBennetts779039, author = {Hernandez Bennetts, Victor and Schaffernicht, Erik and Pomadera Sese, Victor and Lilienthal, Achim J. and Trincavelli, Marco}, booktitle = {Proceedings of the IEEE Sensors Conference 2014 : }, institution = {Örebro University, School of Science and Technology}, institution = {Institute of Bioengineering of Catalonia, Barcelona, Spain}, title = {A Novel Approach for Gas Discrimination in Natural Environments with Open Sampling Systems}, DOI = {10.1109/ICSENS.2014.6985437}, abstract = {This work presents a gas discrimination approachfor Open Sampling Systems (OSS), composed of non-specificmetal oxide sensors only. In an OSS, as used on robots or insensor networks, the sensors are exposed to the dynamics of theenvironment and thus, most of the data corresponds to highlydiluted samples while high concentrations are sparse. In addition,a positive correlation between class separability and concentra-tion level can be observed. The proposed approach computes theclass posteriors by coupling the pairwise probabilities betweenthe compounds to a confidence model based on an estimation ofthe concentration. In this way a rejection posterior, analogous tothe detection limit of the human nose, is learned. Evaluation wasconducted in indoor and outdoor sites, with an OSS equippedrobot, in the presence of two gases. The results show that theproposed approach achieves a high classification performancewith a low sensitivity to the selection of meta parameters. }, year = {2014} } @article{Neumann780080, author = {Neumann, Patrick P. and Schn{\"u}rmacher, Michael and Hernandez Bennetts, Victor and Lilienthal, Achim J. and Bartholmai, Matthias and Schiller, Jochen H.}, institution = {Örebro University, School of Science and Technology}, institution = {BAM Federal Institute for Materials Research and Testing, Berlin, Germany}, institution = {Institute of Computer Science, FU University, Berlin, Germany}, institution = {BAM Federal Institute for Materials Research and Testing, Berlin, Germany}, institution = {Institute of Computer Science, FU University, Berlin, Germany}, journal = {Sensor Letters}, number = {6-7}, pages = {1113--1118}, title = {A Probabilistic Gas Patch Path Prediction Approach for Airborne Gas Source Localization in Non-Uniform Wind Fields}, volume = {12}, DOI = {10.1166/sl.2014.3168}, keywords = {autonomous micro UAV, chemical and wind sensing, gas source localization, particle filter}, abstract = {In this paper, we show that a micro unmanned aerial vehicle (UAV) equipped with commercially available gas sensors can addressenvironmental monitoring and gas source localization (GSL) tasks. To account for the challenges of gas sensing under real-world conditions,we present a probabilistic approach to GSL that is based on a particle filter (PF). Simulation and real-world experiments demonstrate thesuitability of this algorithm for micro UAV platforms. }, year = {2014} } @article{Schaffernicht779893, author = {Schaffernicht, Erik and Trincavelli, Marco and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Sensor Letters}, number = {6-7}, pages = {1142--1146}, title = {Bayesian Spatial Event Distribution Grid Maps for Modeling the Spatial Distribution of Gas Detection Events}, volume = {12}, DOI = {10.1166/sl.2014.3189}, keywords = {BERNOULLI DISTRIBUTION; BETA DISTRIBUTION; GAS DISTRIBUTION MAPPING; STATISTICAL MODELING}, abstract = {In this paper we introduce a novel gas distribution mapping algorithm, Bayesian Spatial Event Distribution (BASED), that, instead of modeling the spatial distribution of a quasi-continuous gas concentration, models the spatial distribution of gas events, for example detection and non-detection of a target gas. The proposed algorithm is based on the Bayesian Inference framework and models the likelihood of events at a certain location with a Bernoulli distribution. In order to avoid overfitting, a Bayesian approach is used with a beta distribution prior for the parameter μ that governs the Bernoulli distribution. In this way, the posterior distribution maintains the same form of the prior, i.e., will be a beta distribution as well, enabling a simple approach for sequential learning. To learn a map composed of beta distributions, we discretize the inspection area into a grid and extrapolate from local measurements using Gaussian kernels. We demonstrate the proposed algorithm for MOX sensors and a photo ionization detector mounted on a mobile robot and show how qualitatively similar maps are obtained from very different gas sensors. }, year = {2014} } @article{HernandezBennetts748117, author = {Hernandez Bennetts, Victor and Schaffernicht, Erik and Pomadera Sese, Victor and Lilienthal, Achim J. and Marco, Santiago and Trincavelli, Marco}, institution = {Örebro University, School of Science and Technology}, institution = {Institute of Bioengineering of Catalonia, Barcelona, Spain}, institution = {Signal and Information Processing for Sensing Systema, Institute for Bioengineering of Catalonia, Barcelona, Spain; Departament d’Electrònica, Universitat de Barcelona, Barcelona, Spain}, journal = {Sensors}, note = {Funding Agencies:Gasbot project 8140Spanish project: "Signal Processing for Ion Mobility Spectrometry: Analysis of Biomedical fluids and detection of toxic chemicals" TEC2011-26143Departament d'Universitats, Recerca i Societat de la Informacio de la Generalitat de Catalunya SGR 1445Comissionat per a Universitats i Recerca del DIUE de la Generalitat de Catalunya and the European Social Fund (ESF)SURDepartment d'Economia i Coneixement}, number = {9}, pages = {17331--17352}, publisher = {MDPI AG}, title = {Combining Non Selective Gas Sensors on a Mobile Robot for Identification and Mapping of Multiple Chemical Compounds}, volume = {14}, DOI = {10.3390/s140917331}, keywords = {environmental monitoring; gas discrimination; gas distribution mapping; service robots; open sampling systems; PID, metal oxide sensors}, abstract = {In this paper, we address the task of gas distribution modeling in scenarios where multiple heterogeneous compounds are present. Gas distribution modeling is particularly useful in emission monitoring applications where spatial representations of the gaseous patches can be used to identify emission hot spots. In realistic environments, the presence of multiple chemicals is expected and therefore, gas discrimination has to be incorporated in the modeling process. The approach presented in this work addresses the task of gas distribution modeling by combining different non selective gas sensors. Gas discrimination is addressed with an open sampling system, composed by an array of metal oxide sensors and a probabilistic algorithm tailored to uncontrolled environments. For each of the identified compounds, the mapping algorithm generates a calibrated gas distribution model using the classification uncertainty and the concentration readings acquired with a photo ionization detector. The meta parameters of the proposed modeling algorithm are automatically learned from the data. The approach was validated with a gas sensitive robot patrolling outdoor and indoor scenarios, where two different chemicals were released simultaneously. The experimental results show that the generated multi compound maps can be used to accurately predict the location of emitting gas sources. }, year = {2014} } @article{Andreasson780236, author = {Andreasson, Henrik and Saarinen, Jari and Cirillo, Marcello and Stoyanov, Todor and Lilienthal, Achim}, institution = {Örebro University, School of Science and Technology}, journal = {Robotics}, number = {4}, pages = {400--416}, publisher = {M D P I AG}, title = {Drive the Drive : From Discrete Motion Plans to Smooth Drivable Trajectories}, volume = {3}, DOI = {10.3390/robotics3040400}, keywords = {Motion planning, motion and path planning, autonomous navigation}, abstract = {Autonomous navigation in real-world industrial environments is a challenging task in many respects. One of the key open challenges is fast planning and execution of trajectories to reach arbitrary target positions and orientations with high accuracy and precision, while taking into account non-holonomic vehicle constraints. In recent years, lattice-based motion planners have been successfully used to generate kinematically and kinodynamically feasible motions for non-holonomic vehicles. However, the discretized nature of these algorithms induces discontinuities in both state and control space of the obtained trajectories, resulting in a mismatch between the achieved and the target end pose of the vehicle. As endpose accuracy is critical for the successful loading and unloading of cargo in typical industrial applications, automatically planned paths have not been widely adopted in commercial AGV systems. The main contribution of this paper is a path smoothing approach, which builds on the output of a lattice-based motion planner to generate smooth drivable trajectories for non-holonomic industrial vehicles. The proposed approach is evaluated in several industrially relevant scenarios and found to be both fast (less than 2 s per vehicle trajectory) and accurate (end-point pose errors below 0.01 m in translation and 0.005 radians in orientation). }, year = {2014} } @inproceedings{Krug780127, author = {Krug, Robert and Stoyanov, Todor and Bonilla, Manuel and Tincani, Vinicio and Vaskevicius, Narunas and Fantoni, Gualtiero and Birk, Andreas and Lilienthal, Achim and Bicchi, Antonio}, booktitle = {Workshop on Autonomous Grasping and Manipulation : An Open Challenge}, institution = {Örebro University, School of Science and Technology}, institution = {Faculty of Engineering, Interdepart. Research Center "Enrico Piaggio", University of Pisa, Pisa, Italy}, institution = {Faculty of Engineering, Interdepart. Research Center "Enrico Piaggio", University of Pisa, Pisa, Italy}, institution = {Faculty of Engineering, Interdepart. Research Center "Enrico Piaggio", University of Pisa, Pisa, Italy}, institution = {Faculty of Engineering, Interdepart. Research Center "Enrico Piaggio", University of Pisa, Pisa, Italy}, institution = {Faculty of Engineering, Interdepart. Research Center "Enrico Piaggio", University of Pisa, Pisa, Italy}, institution = {Faculty of Engineering, Interdepart. Research Center "Enrico Piaggio", University of Pisa, Pisa, Italy}, title = {Improving Grasp Robustness via In-Hand Manipulation with Active Surfaces}, keywords = {Grasping, Grasp Control, Grasp Planning}, year = {2014} } @article{Almqvist704809, author = {Almqvist, H{\aa}kan and Magnusson, Martin and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Journal of Intelligent and Robotic Systems}, number = {1}, pages = {101--128}, title = {Improving Point Cloud Accuracy Obtained from a Moving Platform for Consistent Pile Attack Pose Estimation}, volume = {75}, DOI = {10.1007/s10846-013-9957-9}, keywords = {3D perception, Autoloading, Scanning while moving}, abstract = {We present a perception system for enabling automated loading with waist-articulated wheel loaders. To enable autonomous loading of piled materials, using either above-ground wheel loaders or underground load-haul-dump vehicles, 3D data of the pile shape is needed. However, using common 3D scanners, the scan data is distorted while the wheel loader is moving towards the pile. Existing methods that make use of 3D scan data (for autonomous loading as well as tasks such as mapping, localisation, and object detection) typically assume that each 3D scan is accurate. For autonomous robots moving over rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one 3D scan, in which case the scan data will be distorted. We present a study of auto-loading methods, and how to locate piles in real-world scenarios with nontrivial ground geometry. We have compared how consistently each method performs for live scans acquired in motion, and also how the methods perform with different view points and scan configurations. The system described in this paper uses a novel method for improving the quality of distorted 3D scans made from a vehicle moving over uneven terrain. The proposed method for improving scan quality is capable of increasing the accuracy of point clouds without assuming any specific features of the environment (such as planar walls), without resorting to a “stop-scan-go” approach, and without relying on specialised and expensive hardware. Each new 3D scan is registered to the preceding using the normal-distributions transform (NDT). After each registration, a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. To verify the impact of the quality improvement, we present data that shows how auto-loading methods benefit from the corrected scans. The presented methods are validated on data from an autonomous wheel loader, as well as with simulated data. The proposed scan-correction method increases the accuracy of both the vehicle trajectory and the point cloud. We also show that it increases the reliability of pile-shape measures used to plan an efficient attack pose when performing autonomous loading. }, year = {2014} } @inproceedings{Valencia780074, author = {Valencia, Rafael and Saarinen, Jari and Andreasson, Henrik and Vallv{\’e;}, Joan and Andrade-Cetto, Juan and Lilienthal, Achim J.}, booktitle = {2014 IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, institution = {CSIC-UPC, Barcelona,Spain}, institution = {CSIC-UPC, Barcelona, Spain}, note = {Institut de Robòtica i Informàtica industrial - UPC, Joint Research Center of the Technical University of Catalonia (UPC) and the Spanish Council for Scientific Research (CSIC) focused on robotics research}, pages = {3956--3962}, title = {Localization in highly dynamic environments using dual-timescale NDT-MCL}, series = {Proceedings - IEEE International Conference on Robotics and Automation}, DOI = {10.1109/ICRA.2014.6907433}, keywords = {Localization, Monte Carlo Localization, Intra Logistics, Mapping}, abstract = {Industrial environments are rarely static and oftentheir configuration is continuously changing due to the materialtransfer flow. This is a major challenge for infrastructure freelocalization systems. In this paper we address this challengeby introducing a localization approach that uses a dualtimescaleapproach. The proposed approach - Dual-TimescaleNormal Distributions Transform Monte Carlo Localization (DTNDT-MCL) - is a particle filter based localization method,which simultaneously keeps track of the pose using an aprioriknown static map and a short-term map. The short-termmap is continuously updated and uses Normal DistributionsTransform Occupancy maps to maintain the current state ofthe environment. A key novelty of this approach is that it doesnot have to select an entire timescale map but rather use thebest timescale locally. The approach has real-time performanceand is evaluated using three datasets with increasing levels ofdynamics. We compare our approach against previously proposedNDT-MCL and commonly used SLAM algorithms andshow that DT-NDT-MCL outperforms competing algorithmswith regards to accuracy in all three test cases. }, year = {2014} } @inproceedings{Vaskevicius772382, author = {Vaskevicius, N. and Mueller, C. A. and Bonilla, M. and Tincani, V. and Stoyanov, Todor and Fantoni, G. and Pathak, K. and Lilienthal, Achim J. and Bicchi, A. and Birk, A.}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {Jacobs University, Bremen, Germany}, institution = {Jacobs University, Bremen, Germany}, institution = {University of Pisa, Pisa, Italy}, institution = {University of Pisa, Pisa, Italy}, institution = {University of Pisa, Pisa, Italy}, institution = {Jacobs University, Bremen, Germany}, institution = {University of Pisa, Pisa, Italy}, institution = {Jacobs University, Bremen, Germany}, pages = {1270--1277}, title = {Object recognition and localization for robust grasping with a dexterous gripper in the context of container unloading}, DOI = {10.1109/CoASE.2014.6899490}, keywords = {containers;control engineering computing;dexterous manipulators;goods distribution;grippers;industrial robots;logistics;object recognition;autonomous shipping-container unloading;dexterous gripper;object recognition;perception system;pose estimation errors;table-top scenarios;Educational institutions;Grasping;Grippers;Robot sensing systems;Thumb}, abstract = {The work presented here is embedded in research on an industrial application scenario, namely autonomous shipping-container unloading, which has several challenging constraints: the scene is very cluttered, objects can be much larger than in common table-top scenarios; the perception must be highly robust, while being as fast as possible. These contradicting goals force a compromise between speed and accuracy. In this work, we investigate a state of the art perception system integrated with a dexterous gripper. In particular, we are interested in pose estimation errors from the recognition module and whether these errors can be handled by the abilities of the gripper. }, year = {2014} } @article{HernandezBennetts676766, author = {Hernandez Bennetts, Victor and Trincavelli, Marco and Lilienthal, Achim J. and Schaffernicht, Erik}, institution = {Örebro University, School of Science and Technology}, journal = {Sensor Letters}, number = {6-7}, pages = {1147--1151}, title = {Online parameter selection for gas distribution mapping}, volume = {12}, DOI = {10.1166/sl.2014.3191}, keywords = {BANDWIDTH SELECTION; GAS DISTRIBUTION MAPPING; VIRTUAL LEAVE-ONE-OUT CROSS VALIDATION}, abstract = {The ability to produce truthful maps of the distribution of one or more gases is beneficial for applications ranging from environmental monitoring to mines and industrial plants surveillance. Realistic environments are often too complicated for applying analytical gas plume models or performing reliable CFD simulations, making data-driven statistical gas distribution models the most attractive alternative. However, statistical models for gas distribution modelling, often rely on a set of meta-parameters that need to be learned from the data through Cross Validation (CV) techniques. CV techniques are computationally expensive and therefore need to be computed offline. As a faster alternative, we propose a parameter selection method based on Virtual Leave-One-Out Cross Validation (VLOOCV) that enables online learning of meta-parameters. In particular, we consider the Kernel DM+V, one of the most well studied algorithms for statistical gas distribution mapping, which relies on a meta-parameter, the kernel bandwidth. We validate the proposed VLOOCV method on a set of indoor and outdoor experiments where a mobile robot with a Photo Ionization Detector (PID) was collecting gas measurements. The approximation provided by the proposed VLOOCV method achieves very similar results to plain Cross Validation at a fraction of the computational cost. This is an important step in the development of on-line statistical gas distribution modelling algorithms. }, year = {2014} } @inproceedings{Mojtahedzadeh778503, author = {Mojtahedzadeh, Rasoul and Bouguerra, Abdelbaki and Schaffernicht, Erik and Lilienthal, Achim J.}, booktitle = {Robotics and Automation (ICRA), 2014 IEEE International Conference on : }, institution = {Örebro University, School of Science and Technology}, pages = {5685--5690}, title = {Probabilistic Relational Scene Representation and Decision Making Under Incomplete Information for Robotic Manipulation Tasks}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ICRA.2014.6907695}, keywords = {Containers, Manipulators, Industrial Robots, Object Detection, Support Vector Machines, Decision Making}, abstract = {In this paper, we propose an approach for robotic manipulation systems to autonomously reason about their environments under incomplete information. The target application is to automate the task of unloading the content of shipping containers. Our goal is to capture possible support relations between objects in partially known static configurations. We employ support vector machines (SVM) to estimate the probability of a support relation between pairs of detected objects using features extracted from their geometrical properties and 3D sampled points of the scene. The set of probabilistic support relations is then used for reasoning about optimally selecting an object to be unloaded first. The proposed approach has been extensively tested and verified on data sets generated in simulation and from real world configurations. }, ISBN = {978-1-4799-3685-4}, year = {2014} } @inproceedings{Bennetts1072051, author = {Bennetts, Victor Hernandez and Schaffernicht, Erik and Stoyanov, Todor and Lilienthal, Achim J. and Trincavelli, Marco}, booktitle = {2014 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) : }, institution = {Örebro University, School of Science and Technology}, pages = {6362--6367}, title = {Robot Assisted Gas Tomography - Localizing Methane Leaks in Outdoor Environments}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ICRA.2014.6907798}, abstract = {In this paper we present an inspection robot to produce gas distribution maps and localize gas sources in large outdoor environments. The robot is equipped with a 3D laser range finder and a remote gas sensor that returns integral concentration measurements. We apply principles of tomography to create a spatial gas distribution model from integral gas concentration measurements. The gas distribution algorithm is framed as a convex optimization problem and it models the mean distribution and the fluctuations of gases. This is important since gas dispersion is not an static phenomenon and furthermore, areas of high fluctuation can be correlated with the location of an emitting source. We use a compact surface representation created from the measurements of the 3D laser range finder with a state of the art mapping algorithm to get a very accurate localization and estimation of the path of the laser beams. In addition, a conic model for the beam of the remote gas sensor is introduced. We observe a substantial improvement in the gas source localization capabilities over previous state-of-the-art in our evaluation carried out in an open field environment. }, ISBN = {978-1-4799-3685-4}, year = {2014} } @inproceedings{HernandezBennetts748476, author = {Hernandez Bennetts, Victor and Schaffernicht, Erik and Stoyanov, Todor and Lilienthal, Achim J. and Trincavelli, Marco}, booktitle = {Workshop on Robot Monitoring : }, institution = {Örebro University, School of Science and Technology}, title = {Robot assisted gas tomography : an alternative approach for the detection of fugitive methane emissions}, abstract = {Methane (CH4) based combustibles, such as Natural Gas (NG) and BioGas (BG), are considered bridge fuels towards a decarbonized global energy system. NG emits less CO2 during combustion than other fossil fuels and BG can be produced from organic waste. However, at BG production sites, leaks are common and CH4 can escape through fissures in pipes and insulation layers. While by regulation BG producers shall issue monthly CH4 emission reports, measurements are sparsely collected, only at a few predefined locations. Due to the high global warming potential of CH4, efficient leakage detection systems are critical. We present a robotics approach to localize CH4 leaks. In Robot assisted Gas Tomography (RGT), a mobile robot is equipped with remote gas sensors to create gas distribution maps, which can be used to infer the location of emitting sources. Spectroscopy based remote gas sensors report integral concentrations, which means that the measurements are spatially unresolved, with neither information regarding the gas distribution over the optical path nor the length of the s beam. Thus, RGT fuses different sensing modalities, such as range sensors for robot localization and ray tracing, in order to infer plausible gas distribution models that explain the acquired integral concentration measurements. }, year = {2014} } @article{Pashami758138, author = {Pashami, Sepideh and Lilienthal, Achim J. and Schaffernicht, Erik and Trincavelli, Marco}, institution = {Örebro University, School of Science and Technology}, journal = {Sensor Letters}, number = {6/7}, pages = {1123--1127}, publisher = {American Scientific Publishers}, title = {rTREFEX: Reweighting norms for detecting changes in the response of MOX gas sensors}, volume = {12}, DOI = {10.1166/sl.2014.3170}, keywords = {MOX Sensor, Open Sampling System, Change Point Detection, Reweighted Norm Minimization}, abstract = { The detection of changes in the response of metal oxide (MOX) gas sensors deployed in an open sampling system is a hard problem. It is relevant for applications such as gas leak detection in mines or large-scale pollution monitoring where it is impractical to continuously store or transfer sensor readings and reliable calibration is hard to achieve. Under these circumstances, it is desirable to detect points in the signal where a change indicates a significant event, e.g. the presence of gas or a sudden change of concentration. The key idea behind the proposed change detection approach is that a change in the emission modality of a gas source appears locally as an exponential function in the response of MOX sensors due to their long response and recovery times. The algorithm proposed in this paper, rTREFEX, is an extension of the previously proposed TREFEX algorithm. rTREFEX interprets the sensor response by fitting piecewise exponential functions with different time constants for the response and recovery phase. The number of exponentials, which has to be kept as low as possible, is determined automatically using an iterative approach that solves a sequence of convex optimization problems based on l1-norm. The algorithm is evaluated with an experimental setup where a gas source changes in intensity, compound, and mixture ratio, and the gas source is delivered to the sensors exploiting natural advection and turbulence mechanisms. rTREFEX is compared against the previously proposed TREFEX, which already proved superior to other algorithms. }, year = {2014} } @inproceedings{Krug696464, author = {Krug, Robert and Stoyanov, Todor and Bonilla, Manuel and Tincani, Vinicio and Vaskevicius, Narunas and Fantoni, Gualtiero and Birk, Andreas and Lilienthal, Achim J. and Bicchi, Antonio}, booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, institution = {Interdepart. Research Center “E. Piaggio”, University of Pisa, Pisa, Italy}, institution = {Interdepart. Research Center “E. Piaggio”, University of Pisa, Pisa, Italy}, institution = {Robotics Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany}, institution = {Interdepart. Research Center “E. Piaggio”, University of Pisa, Pisa, Italy}, institution = {Robotics Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany}, institution = {Interdepart. Research Center “E. Piaggio”, University of Pisa, Pisa, Italy}, pages = {3669--3675}, title = {Velvet fingers : grasp planning and execution for an underactuated gripper with active surfaces}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ICRA.2014.6907390}, keywords = {Grasp Planning, Grasp Control, Underactuation}, abstract = {In this work we tackle the problem of planning grasps for an underactuated gripper which enable it to retrieve target objects from a cluttered environment. Furthermore,we investigate how additional manipulation capabilities of the gripping device, provided by active surfaces on the inside of the fingers, can lead to performance improvement in the grasp execution process. To this end, we employ a simple strategy, in which the target object is ‘pulled-in’ towards the palm during grasping which results in firm enveloping grasps. We show the effectiveness of the suggested methods by means of experiments conducted in a real-world scenario. }, ISBN = {978-1-4799-3685-4}, year = {2014} } @article{Saarinen644380, author = {Saarinen, Jari and Andreasson, Henrik and Stoyanov, Todor and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {The international journal of robotics research}, note = {Funding agency:Kunskaps och Kompetensutveckling Stiftelsen project SAUNA 20100315}, number = {14}, pages = {1627--1644}, title = {3D normal distributions transform occupancy maps : an efficient representation for mapping in dynamic environments}, volume = {32}, DOI = {10.1177/0278364913499415}, abstract = {In order to enable long-term operation of autonomous vehicles in industrial environments numerous challenges need to be addressed. A basic requirement for many applications is the creation and maintenance of consistent 3D world models. This article proposes a novel 3D spatial representation for online real-world mapping, building upon two known representations: normal distributions transform (NDT) maps and occupancy grid maps. The proposed normal distributions transform occupancy map (NDT-OM) combines the advantages of both representations; compactness of NDT maps and robustness of occupancy maps. One key contribution in this article is that we formulate an exact recursive updates for NDT-OMs. We show that the recursive update equations provide natural support for multi-resolution maps. Next, we describe a modification of the recursive update equations that allows adaptation in dynamic environments. As a second key contribution we introduce NDT-OMs and formulate the occupancy update equations that allow to build consistent maps in dynamic environments. The update of the occupancy values are based on an efficient probabilistic sensor model that is specially formulated for NDT-OMs. In several experiments with a total of 17 hours of data from a milk factory we demonstrate that NDT-OMs enable real-time performance in large-scale, long-term industrial setups. }, year = {2013} } @inproceedings{Blanco676862, author = {Blanco, Jose Luis and Monroy, Javier G. and Gonzalez-Jimenez, Javier and Lilienthal, Achim J.}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {University of Màlaga, Màlaga, Spain}, institution = {University of Màlaga, Màlaga, Spain}, institution = {University of Màlaga, Màlaga, Spain}, note = {© ACM, YYYY. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is published in 28th ACM Symposium on Applied Computing (SAC 2013), 2013} http://doi.acm.org/10.1145/2480362.2480409"}, pages = {217--222}, title = {A Kalman Filter Based Approach To Probabilistic Gas Distribution Mapping}, DOI = {10.1145/2480362.2480409}, keywords = {Kalman Filter, Gas Distribution Mapping, Mobile Olfaction}, abstract = {Building a model of gas concentrations has important indus-trial and environmental applications, and mobile robots ontheir own or in cooperation with stationary sensors play animportant role in this task. Since an exact analytical de-scription of turbulent flow remains an intractable problem,we propose an approximate approach which not only esti-mates the concentrations but also their variances for eachlocation. Our point of view is that of sequential Bayesianestimation given a lattice of 2D cells treated as hidden vari-ables. We first discuss how a simple Kalman filter pro-vides a solution to the estimation problem. To overcomethe quadratic computational complexity with the mappedarea exhibited by a straighforward application of Kalmanfiltering, we introduce a sparse implementation which runsin constant time. Experimental results for a real robot vali-date the proposed method. }, ISBN = {9781450316569}, year = {2013} } @inproceedings{Neumann644432, author = {Neumann, Patrick and Schn{\"u}rmacher, Michael and Hernandez Bennetts, Victor and Lilienthal, Achim J. and Bartholmai, Matthias and Schiller, Jochen}, booktitle = {Proceedings of the 15th ISOEN : }, institution = {Örebro University, School of Science and Technology}, institution = {BAM Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {BAM Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {BAM Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {BAM Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, title = {A Probabilistic Gas Patch Prediction Approach for Airborne Gas Source Localization in Non-Uniform Wind Fields}, year = {2013} } @inproceedings{Pashami645513, author = {Pashami, Sepideh and Lilienthal, Achim J. and Trincavelli, Marco}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, title = {A trend filtering approach for change point detection in MOX gas sensors}, keywords = {MOX sensor; open sampling system; change point detection; trend filtering}, abstract = {Detecting changes in the response of metal oxide (MOX) gas sensors deployed in an open sampling system is a hard problem. It is relevant for applicationssuch as gas leak detection in coal mines[1],[2] or large scale pollution monitoring [3],[4] where it is unpractical to continuously store or transfer sensor readings and reliable calibration is hard to achieve. Under these circumstances it is desirable to detect points in the signal where a change indicates a significant event, e.g. the presence of gas or a sudden change of concentration. The key idea behind the proposed change detection approach isthat a change in the emission modality of a gas source appears locally as an exponential function in the response of MOX sensors due to their long response and recovery times. The proposed method interprets the sensor responseby fitting piecewise exponential functions with different time constants for the response and recovery phase. The number of exponentials is determined automatically using an approximate method based on the L1-norm. This asymmetric exponential trend filtering problem is formulated as a convex optimization problem, which is particularly advantageous from the computational point of view. The algorithm is evaluated with an experimental setup where a gas source changes in intensity, compound, and mixture ratio, and it is compared against the previously proposed Generalized Likelihood Ratio (GLR) based algorithm [6]. }, year = {2013} } @inproceedings{Mojtahedzadeh698571, author = {Mojtahedzadeh, Rasoul and Stoyanov, Todor and Lilienthal, Achim J.}, booktitle = {Proceedings of the European Conference on Mobile Robots (ECMR) : }, institution = {Örebro University, School of Science and Technology}, pages = {313--318}, title = {Application Based 3D Sensor Evaluation : A Case Study in 3D Object Pose Estimation for Automated Unloading of Containers}, DOI = {10.1109/ECMR.2013.6698860}, abstract = {A fundamental task in the design process of a complex system that requires 3D visual perception is the choice of suitable 3D range sensors. Identifying the utility of 3D range sensors in an industrial application solely based on an evaluation of their distance accuracy and the noise level may lead to an inappropriate selection. To assess the actual effect on the performance of the system as a whole requires a more involved analysis. In this paper, we examine the problem of selecting a set of 3D range sensors when designing autonomous systems for specific industrial applications in a holistic manner. As an instance of this problem we present a case study with an experimental evaluation of the utility of four 3D range sensors for object pose estimation in the process of automation of unloading containers. }, year = {2013} } @inproceedings{Mojtahedzadeh664340, author = {Mojtahedzadeh, Rasoul and Bouguerra, Abdelbaki and Lilienthal, Achim J.}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, pages = {1335--1340}, title = {Automatic relational scene representation for safe robotic manipulation tasks}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2013.6696522}, abstract = {In this paper, we propose a new approach forautomatically building symbolic relational descriptions of staticconfigurations of objects to be manipulated by a robotic system.The main goal of our work is to provide advanced cognitiveabilities for such robotic systems to make them more aware ofthe outcome of their actions. We describe how such symbolicrelations are automatically extracted for configurations ofbox-shaped objects using notions from geometry and staticequilibrium in classical mechanics. We also present extensivesimulation results as well as some real-world experiments aimedat verifying the output of the proposed approach. }, ISBN = {978-1-4673-6358-7}, year = {2013} } @inproceedings{Pomareda646007, author = {Pomareda, Victor and Hernandez Bennetts, Victor and Abdul Khaliq, Ali and Trincavelli, Marco and Lilienthal, Achim J. and Marco, Santiago}, booktitle = {Proceedings of the 15th International Symposium on Olfaction and Electronic Nose (ISOEN 2013) : }, institution = {Örebro University, School of Science and Technology}, institution = {Intelligent Signal Processing, Department of Electronics, University of Barcelona, Barcelona, Spain}, institution = {Intelligent Signal Processing, Department of Electronics, University of Barcelona, Barcelona, Spain}, title = {Chemical source localization in real environments integrating chemical concentrations in a probabilistic plume mapping approach}, keywords = {chemical plume source localization, Bayesian inference, chemical concentration, mobile robots, real environmen ts}, abstract = {Chemical plume source localization algorithms can be classified either as reactive plume tracking or gas distribution mapping approaches. Here, we focus on gas distribution mapping methods where the robot does not need to track the plume to find the source and can be used for other tasks. Probabilistic mapping approaches have been previously applied to real-world data successfully; e.g., in the approach proposed by Pang and Farrell. Instead of the quasi-continuous gas measurement values, this algorithm considers events (detections and non-detections) based on whether the sensor response is above or below a threshold to update recursively a source probability grid map; thus, discarding important information. We developed an extension of this event-based approach, integrating chemical concentrations directly instead of binary information. In this work, both algorithms are compared using real-world data obtained from a photo-ionization detector (PID), a non-selective gas sensor, and an anemometer in real environments. We validate simulation results and demonstrate that the concentration-based approach is more accurate in terms of a higher probability at the ground truth source location, a smaller distance between the probability maximum and the source location, and a more peaked probability distribution, measured in terms of the overall entropy. }, year = {2013} } @article{Stoyanov618586, author = {Stoyanov, Todor and Mojtahedzadeh, Rasoul and Andreasson, Henrik and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Robotics and Autonomous Systems}, number = {10}, pages = {1094--1105}, title = {Comparative evaluation of range sensor accuracy for indoor mobile robotics and automated logistics applications}, volume = {61}, DOI = {10.1016/j.robot.2012.08.011}, abstract = {3D range sensing is an important topic in robotics, as it is a component in vital autonomous subsystems such as for collision avoidance, mapping and perception. The development of affordable, high frame rate and precise 3D range sensors is thus of considerable interest. Recent advances in sensing technology have produced several novel sensors that attempt to meet these requirements. This work is concerned with the development of a holistic method for accuracy evaluation of the measurements produced by such devices. A method for comparison of range sensor output to a set of reference distance measurements, without using a precise ground truth environment model, is proposed. This article presents an extensive evaluation of three novel depth sensors — the Swiss Ranger SR-4000, Fotonic B70 and Microsoft Kinect. Tests are concentrated on the automated logistics scenario of container unloading. Six different setups of box-, cylinder-, and sack-shaped goods inside a mock-up container are used to collect range measurements. Comparisons are performed against hand-crafted ground truth data, as well as against a reference actuated Laser Range Finder (aLRF) system. Additional test cases in an uncontrolled indoor environment are performed in order to evaluate the sensors’ performance in a challenging, realistic application scenario. }, year = {2013} } @article{Stoyanov618700, author = {Stoyanov, Todor and Magnusson, Martin and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Journal of Field Robotics}, number = {2}, pages = {216--236}, title = {Comparative evaluation of the consistency of three-dimensional spatial representations used in autonomous robot navigation}, volume = {30}, DOI = {10.1002/rob.21446}, abstract = {An increasing number of robots for outdoor applications rely on complex three-dimensional (3D) environmental models. In many cases, 3D maps are used for vital tasks, such as path planning and collision detection in challenging semistructured environments. Thus, acquiring accurate three-dimensional maps is an important research topic of high priority for autonomously navigating robots. This article proposes an evaluation method that is designed to compare the consistency with which different representations model the environment. In particular, the article examines several popular (probabilistic) spatial representations that are capable of predicting the occupancy of any point in space, given prior 3D range measurements. This work proposes to reformulate the obtained environmental models as probabilistic binary classifiers, thus allowing for the use of standard evaluation and comparison procedures. To avoid introducing localization errors, this article concentrates on evaluating models constructed from measurements acquired at fixed sensor poses. Using a cross-validation approach, the consistency of different representations, i.e., the likelihood of correctly predicting unseen measurements in the sensor field of view, can be evaluated. Simulated and real-world data sets are used to benchmark the precision of four spatial models—occupancy grid, triangle mesh, and two variations of the three-dimensional normal distributions transform (3D-NDT)—over various environments and sensor noise levels. Overall, the consistency of representation of the 3D-NDT is found to be the highest among the tested models, with a similar performance over varying input data. }, year = {2013} } @inproceedings{Kucner664130, author = {Kucner, Tomasz and Sarinen, Jari and Magnusson, Martin and Lilienthal, Achim J.}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems : }, institution = {Örebro University, School of Science and Technology}, institution = {Aalto university, Helsinki, Finland}, pages = {1196--1201}, title = {Conditional transition maps: learning motion patterns in dynamic environments}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2013.6696502}, keywords = {Mapping, Navigation}, abstract = {In this paper we introduce a method for learning motion patterns in dynamic environments. Representations of dynamic environments have recently received an increasing amount of attention in the research community. Understanding dynamic environments is seen as one of the key challenges in order to enable autonomous navigation in real-world scenarios. However, representing the temporal dimension is a challenge yet to be solved. In this paper we introduce a spatial representation, which encapsulates the statistical dynamic behavior observed in the environment. The proposed Conditional Transition Map (CTMap) is a grid-based representation that associates a probability distribution for an object exiting the cell, given its entry direction. The transition parameters are learned from a temporal signal of occupancy on cells by using a local-neighborhood cross-correlation method. In this paper, we introduce the CTMap, the learning approach and present a proof-of-concept method for estimating future paths of dynamic objects, called Conditional Probability Propagation Tree (CPPTree). The evaluation is done using a real-world data-set collected at a busy roundabout. }, ISBN = {978-1-4673-6357-0}, year = {2013} } @inproceedings{Saarinen644375, author = {Saarinen, Jari and Stoyanov, Todor and Andreasson, Henrik and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, note = {to appear}, pages = {4694--4701}, title = {Fast 3D mapping in highly dynamic environments using normal distributions transform occupancy maps}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2013.6697032}, ISBN = {978-1-4673-6358-7}, year = {2013} } @article{Neumann644489, author = {Neumann, Patrick and Hernandez Bennetts, Victor and Lilienthal, Achim J. and Bartholmai, Matthias and Schiller, Jochen H.}, institution = {Örebro University, School of Science and Technology}, institution = {Federal Institute for Materials Research and Testing (BAM), Berlin, Germany}, institution = {Federal Institute for Materials Research and Testing (BAM), Berlin, Germany}, institution = {Institute of Computer Science, Freie Universität, Berlin, Germany}, journal = {Advanced Robotics}, number = {9}, pages = {725--738}, title = {Gas source localization with a micro-drone using bio-inspired and particle filter-based algorithms}, volume = {27}, DOI = {10.1080/01691864.2013.779052}, keywords = {autonomous micro UAV; chemical and wind sensing; gas source localization; particle filter}, abstract = {Gas source localization (GSL) with mobile robots is a challenging task due to the unpredictable nature of gas dispersion,the limitations of the currents sensing technologies, and the mobility constraints of ground-based robots. This work proposesan integral solution for the GSL task, including source declaration. We present a novel pseudo-gradient-basedplume tracking algorithm and a particle filter-based source declaration approach, and apply it on a gas-sensitivemicro-drone. We compare the performance of the proposed system in simulations and real-world experiments againsttwo commonly used tracking algorithms adapted for aerial exploration missions. }, year = {2013} } @inproceedings{Canelhas644372, author = {Canelhas, Daniel R. and Stoyanov, Todor and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, pages = {3203--3209}, title = {Improved local shape feature stability through dense model tracking}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2013.6696811}, abstract = {In this work we propose a method to effectively remove noise from depth images obtained with a commodity structured light sensor. The proposed approach fuses data into a consistent frame of reference over time, thus utilizing prior depth measurements and viewpoint information in the noise removal process. The effectiveness of the approach is compared to two state of the art, single-frame denoising methods in the context of feature descriptor matching and keypoint detection stability. To make more general statements about the effect of noise removal in these applications, we extend a method for evaluating local image gradient feature descriptors to the domain of 3D shape descriptors. We perform a comparative study of three classes of such descriptors: Normal Aligned Radial Features, Fast Point Feature Histograms and Depth Kernel Descriptors; and evaluate their performance on a real-world industrial application data set. We demonstrate that noise removal enabled by the dense map representation results in major improvements in matching across all classes of descriptors as well as having a substantial positive impact on keypoint detection reliability }, ISBN = {978-1-4673-6358-7}, year = {2013} } @inproceedings{Almqvist644368, author = {Almqvist, H{\aa}kan and Magnusson, Martin and Stoyanov, Todor and Lilienthal, Achim J.}, booktitle = {2013 IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, pages = {733--738}, title = {Improving Point-Cloud Accuracy from a Moving Platform in Field Operations}, DOI = {10.1109/ICRA.2013.6630654}, abstract = {This paper presents a method for improving the quality of distorted 3D point clouds made from a vehicle equipped with a laser scanner moving over uneven terrain. Existing methods that use 3D point-cloud data (for tasks such as mapping, localisation, and object detection) typically assume that each point cloud is accurate. For autonomous robots moving in rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one point cloud, in which case the data will be distorted. The method proposed in this paper is capable of increasing the accuracy of 3D point clouds, without assuming any specific features of the environment (such as planar walls), without resorting to a "stop-scan-go" approach, and without relying on specialised and expensive hardware. Each new point cloud is matched to the previous using normal-distribution-transform (NDT) registration, after which a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. The proposed method increases the accuracy of both the measured platform trajectory and the point cloud. The method is validated on both real-world and simulated data. }, ISBN = {978-1-4673-5641-1}, ISBN = {978-1-4673-5643-5}, year = {2013} } @inproceedings{Lilienthal646005, author = {Lilienthal, Achim J. and Trincavelli, Marco and Schaffernicht, Erik}, booktitle = {Proceedings of the 15th International Symposium on Olfaction and Electronic Nose (ISOEN 2013) : }, institution = {Örebro University, School of Science and Technology}, title = {It's always smelly around here! Modeling the Spatial Distribution of Gas Detection Events with BASED Grid Maps}, keywords = {Gas Distribution Mapping, Bayesian Statistical Modeling, Beta Distribution}, abstract = {In this paper we introduce a novel gas distribution mapping algorithm, Bayesian Spatial Event Distribution (BASED), that, instead of modeling the spatial distribution of the gas concentration, models the spatial distribution of events of detection and non-detection of a target gas. The proposed algorithm is based on the Bayesian inference framework and models the likelihood of events at a certain location with a Bernoulli distribution. In order to avoid overfitting a Bayesian approach is used with a beta distribution prior for the parameter u that governs the Bernoulli distribution. In this way, the posterior distribution maintains the same form of the prior, i.e. will be a beta distribution, enabling a simple approach for sequential learning. To learn a field of beta distributions, we discretize the inspection area into a grid map and extrapolate from local measurements using Gaussian kernels. We demonstrate the proposed algorithm for different sensors mounted on a mobile robot and show how qualitatively similar maps are obtained from very different gas sensors. }, year = {2013} } @article{Neumann644493, author = {Neumann, Patrick and Asadi, Sahar and Hernandez Bennetts, Victor and Lilienthal, Achim J. and Bartholmai, Matthias}, institution = {Örebro University, School of Science and Technology}, institution = { Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = { Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, journal = {Energy Procedia}, pages = {4182--4190}, title = {Monitoring of CCS areas using micro unmanned aerial vehicles (MUAVs)}, volume = {37}, DOI = {10.1016/j.egypro.2013.06.320}, keywords = {gas-sensitive micro-drone; gas distribution mapping; sensor planning; artificial potential field; CCS}, abstract = {Carbon capture & storage (CCS) is one of the most promis ing technologies for greenhouse gas (GHG) management.However, an unsolved issue of CCS is the development of appropriate long-term monitoring systems for leakdetection of the stored CO2. To complement already existing monitoring infrastructure for CO2 storage areas, and toincrease the granularity of gas concentration measurements, a quickly deployab le, mobile measurement device isneeded. In this paper, we present an autonomous gas-sensitive micro-drone, which can be used to monitor GHGemissions, more specifically, CO2. Two different measurement strategies are proposed to address this task. First, theuse of predefined sensing trajectories is evaluated for the task of gas distribution mapping using the micro-drone.Alternatively, we present an adaptive strategy, which suggests sampling points based on an artific ial potential field(APF). The results of real-world experiments demonstrate the feas ibility of using gas-sensitive micro-drones for GHG monitoring missions. Thus, we suggest a multi-layered surveillance system for CO2 storage areas. }, year = {2013} } @inproceedings{Mosberger684470, author = {Mosberger, Rafael and Andreasson, Henrik and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, pages = {638--644}, title = {Multi-human Tracking using High-visibility Clothing for Industrial Safety}, series = {Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on}, DOI = {10.1109/IROS.2013.6696418}, keywords = {Human Detection, Robot Vision, Industrial Safety}, abstract = {We propose and evaluate a system for detecting and tracking multiple humans wearing high-visibility clothing from vehicles operating in industrial work environments. We use a customized stereo camera setup equipped with IR flash and IR filter to detect the reflective material on the worker's garments and estimate their trajectories in 3D space. An evaluation in two distinct industrial environments with different degrees of complexity demonstrates the approach to be robust and accurate for tracking workers in arbitrary body poses, under occlusion, and under a wide range of different illumination settings. }, year = {2013} } @inproceedings{Saarinen644376, author = {Saarinen, Jari and Andreasson, Henrik and Stoyanov, Todor and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, note = {to appear}, pages = {382--389}, title = {Normal distributions transform monte-carlo localization (NDT-MCL)}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2013.6696380}, ISBN = {978-1-4673-6358-7}, year = {2013} } @inproceedings{Stoyanov644379, author = {Stoyanov, Todor and Saarinen, Jari and Andreasson, Henrik and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, note = {to appear}, pages = {4702--4708}, title = {Normal distributions transform occupancy map fusion : simultaneous mapping and tracking in large scale dynamic environments}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2013.6697033}, ISBN = {978-1-4673-6358-7}, year = {2013} } @inproceedings{Saarinen622633, author = {Saarinen, Jari and Andreasson, Henrik and Stoyanov, Todor and Ala-Luhtala, Juha and Lilienthal, Achim J.}, booktitle = {IEEE International Conference on Robotics and Automation : }, institution = {Örebro University, School of Science and Technology}, institution = {Aalto University of Technology, Aalto, Finland}, pages = {2233--2238}, title = {Normal distributions transform occupancy maps : application to large-scale online 3D mapping}, DOI = {10.1109/ICRA.2013.6630878}, abstract = {Autonomous vehicles operating in real-world industrial environments have to overcome numerous challenges, chief among which is the creation and maintenance of consistent 3D world models. This paper proposes to address the challenges of online real-world mapping by building upon previous work on compact spatial representation and formulating a novel 3D mapping approach — the Normal Distributions Transform Occupancy Map (NDT-OM). The presented algorithm enables accurate real-time 3D mapping in large-scale dynamic nvironments employing a recursive update strategy. In addition, the proposed approach can seamlessly provide maps at multiple resolutions allowing for fast utilization in high-level functions such as localization or path planning. Compared to previous approaches that use the NDT representation, the proposed NDT-OM formulates an exact and efficient recursive update formulation and models the full occupancy of the map. }, year = {2013} } @inproceedings{HernandezBennetts644410, author = {Hernandez Bennetts, Victor and Trincavelli, Marco and Lilienthal, Achim J. and Pomadera Sese, Victor and Schaffernicht, Erik}, booktitle = {Proceedings of the ISOEN conference 2013 : }, institution = {Örebro University, School of Science and Technology}, institution = {Institute of Bioengineering of Catalonia, Barcelona, Spain}, title = {Online parameter selection for gas distribution mapping}, year = {2013} } @article{GonzàlezMonroy641025, author = {Gonzàlez Monroy, Javier and Lilienthal, Achim J. and Blanco, Jose Luis and Gonzàlez Jimenez, Javier and Trincavelli, Marco}, institution = {Örebro University, School of Science and Technology}, institution = {University of Málaga, Málaga, Spain}, institution = {University of Almería, Almería, Spain}, institution = {University of Málaga, Málaga, Spain}, journal = {Sensors and actuators. B, Chemical}, note = {Funding agency:Regional Government of Andalucia European Union (FEDER) P08-TEP-4016 }, pages = {298--312}, title = {Probabilistic gas quantification with MOX sensors in open sampling systems : a gaussian process approach}, volume = {188}, DOI = {10.1016/j.snb.2013.06.053}, keywords = {Gas quantification, Open Sampling System, MOX sensors, Gaussian Processes}, abstract = {Gas quantification based on the response of an array of metal oxide (MOX) gas sensors in an Open Sampling System is a complex problem due to the highly dynamic characteristic of turbulent airflow and the slow dynamics of the MOX sensors. However, many gas related applications require to determine the gas concentration the sensors are being exposed to. Due to the chaotic nature that dominates gas dispersal, in most cases it is desirable to provide, together with an estimate of the mean concentration, an estimate of the uncertainty of the prediction. This work presents a probabilistic approach for gas quantification with an array of MOX gas sensors based on Gaussian Processes, estimating for every measurement of the sensors a posterior distribution of the concentration, from which confidence intervals can be obtained. The proposed approach has been tested with an experimental setup where an array of MOX sensors and a Photo Ionization Detector (PID), used to obtain ground truth concentration, are placed downwind with respect to the gas source. Our approach has been implemented and compared with standard gas quantification methods, demonstrating the advantages when estimating gas concentrations. }, year = {2013} } @inproceedings{Canelhas644377, author = {Canelhas, Daniel R. and Stoyanov, Todor and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, pages = {3671--3676}, title = {SDF tracker : a parallel algorithm for on-line pose estimation and scene reconstruction from depth images}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2013.6696880}, abstract = {Ego-motion estimation and environment mapping are two recurring problems in the field of robotics. In this work we propose a simple on-line method for tracking the pose of a depth camera in six degrees of freedom and simultaneously maintaining an updated 3D map, represented as a truncated signed distance function. The distance function representation implicitly encodes surfaces in 3D-space and is used directly to define a cost function for accurate registration of new data. The proposed algorithm is highly parallel and achieves good accuracy compared to state of the art methods. It is suitable for reconstructing single household items, workspace environments and small rooms at near real-time rates, making it practical for use on modern CPU hardware }, ISBN = {978-1-4673-6358-7}, year = {2013} } @article{Duckett664999, author = {Duckett, Tom and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Lincoln School of Computer Science, University of Lincoln, Lincoln, United Kingdom}, journal = {Robotics and Autonomous Systems}, number = {10}, pages = {1049--1050}, title = {Special Issue : Selected Papers from the 5th European Conference on Mobile Robots (ECMR 2011)}, volume = {61}, DOI = {10.1016/j.robot.2013.01.005}, year = {2013} } @inproceedings{HernandezBennetts741445, author = {Hernandez Bennetts, Victor Manuel and Lilienthal, Achim J. and Khaliq, Ali Abdul and Pomareda Sese, Victor and Trincavelli, Marco}, booktitle = {2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) : }, institution = {Örebro University, School of Science and Technology}, institution = {Institute of Bioengineering of Catalonia, Barcelona, Spain}, pages = {2335--2340}, title = {Towards Real-World Gas Distribution Mapping and Leak Localization Using a Mobile Robot with 3D and Remote Gas Sensing Capabilities}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ICRA.2013.6630893}, abstract = {Due to its environmental, economical and safety implications, methane leak detection is a crucial task to address in the biogas production industry. In this paper, we introduce Gasbot, a robotic platform that aims to automatize methane emission monitoring in landfills and biogas production sites. The distinctive characteristic of the Gasbot platform is the use of a Tunable Laser Absorption Spectroscopy (TDLAS) sensor. This sensor provides integral concentration measurements over the path of the laser beam. Existing gas distribution mapping algorithms can only handle local measurements obtained from traditional in-situ chemical sensors. In this paper we also describe an algorithm to generate 3D methane concentration maps from integral concentration and depth measurements. The Gasbot platform has been tested in two different scenarios: an underground corridor, where a pipeline leak was simulated and in a decommissioned landfill site, where an artificial methane emission source was introduced. }, year = {2013} } @article{Pashami625614, author = {Pashami, Sepideh and Lilienthal, Achim J. and Schaffernicht, Erik and Trincavelli, Marco}, institution = {Örebro University, School of Science and Technology}, journal = {Sensors}, number = {6}, pages = {7323--7344}, publisher = {MDPI AG}, title = {TREFEX : trend estimation and change detection in the response of mox gas sensors}, volume = {13}, DOI = {10.3390/s130607323}, keywords = {metal oxide sensors, open sampling system, change point detection, trend filtering}, abstract = {Many applications of metal oxide gas sensors can benefit from reliable algorithmsto detect significant changes in the sensor response. Significant changes indicate a changein the emission modality of a distant gas source and occur due to a sudden change ofconcentration or exposure to a different compound. As a consequence of turbulent gastransport and the relatively slow response and recovery times of metal oxide sensors,their response in open sampling configuration exhibits strong fluctuations that interferewith the changes of interest. In this paper we introduce TREFEX, a novel change pointdetection algorithm, especially designed for metal oxide gas sensors in an open samplingsystem. TREFEX models the response of MOX sensors as a piecewise exponentialsignal and considers the junctions between consecutive exponentials as change points. Weformulate non-linear trend filtering and change point detection as a parameter-free convexoptimization problem for single sensors and sensor arrays. We evaluate the performanceof the TREFEX algorithm experimentally for different metal oxide sensors and severalgas emission profiles. A comparison with the previously proposed GLR method shows aclearly superior performance of the TREFEX algorithm both in detection performance andin estimating the change time. }, year = {2013} } @inproceedings{Trincavelli617890, author = {Trincavelli, Marco and Hernandez Bennetts, Victor and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE Sensors Conference, 2012 : }, institution = {Örebro University, School of Science and Technology}, pages = {550--553}, title = {A Least Squares approach for learning gas distribution maps from a set of integral gas concentration measurements obtained with a TDLAS sensor}, series = {IEEE Sensors}, DOI = {10.1109/ICSENS.2012.6411118}, keywords = {Absorption, Gas lasers, Laser beams, Measurement by laser beam, Noise, Noise measurement, Robot sensing systems}, abstract = {Applications related to industrial plant surveillance and environmental monitoring often require the creation of gas distribution maps (GDM). In this paper an approach for creating a gas distribution map using a Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor and a laser range scanner mounted on a pan tilt unit is presented. The TDLAS sensor can remotely sense the target gas, in this case methane, requiring novel GDM algorithms compared to the ones developed for traditional in-situ chemical sensors. The presented setup makes it possible to create a 3D model of the environment and to calculate the path travelled by the TDLAS beam. The knowledge of the beam path is of crucial importance since a TDLAS sensor provides an integral measurement of the gas concentration over that path. An efficient GDM algorithm based on a quadratic programming formulation is proposed. The approach is tested in an indoor scenario where transparent bottles filled with methane are successfully localized. }, ISBN = {978-1-4577-1766-6}, year = {2012} } @article{Neumann524749, author = {Neumann, Patrick P. and Asadi, Sahar and Lilienthal, Achim J. and Bartholmai, Matthias and Schiller, Jochen H.}, institution = {Örebro University, School of Science and Technology}, institution = {BAM Federal Institute for Materials Research and Testing, Berlin, Germany}, institution = {Sensors and Measurement Systems Working Group, BAM Federal Institute for Materials Research and Testing, Berlin, Germany}, institution = {Computer Systems and Telematics Working Group, Institute of Computer Science, Freie Universität, Berlin, Germany}, journal = {IEEE robotics & automation magazine}, note = {Funding Agencies:European Commission FP7 224318BMWi 28/07}, number = {1}, pages = {50--61}, title = {Autonomous gas-sensitive microdrone wind vector estimation and gas distribution mapping}, volume = {19}, DOI = {10.1109/MRA.2012.2184671}, keywords = {Robot sensing systems, Real time systems, Gas detectors, Delta modulation, Mobile communication}, abstract = {This article presents the development and validation of an autonomous, gas sensitive microdrone that is capable of estimating the wind vector in real time using only the onboard control unit of the microdrone and performing gas distribution mapping (DM). Two different sampling approaches are suggested to address this problem. On the one hand, a predefined trajectory is used to explore the target area with the microdrone in a real-world gas DM experiment. As an alternative sampling approach, we introduce an adaptive strategy that suggests next sampling points based on an artificial potential field (APF). Initial results in real-world experiments demonstrate the capability of the proposed adaptive sampling strategy for gas DM and its use for gas source localization. }, year = {2012} } @inproceedings{Monroy618182, author = {Monroy, Javier G. and Lilienthal, Achim J. and Blanco, Jose Luis and Gonz{\’a;}lez-Jimenez, Javier and Trincavelli, Marco}, booktitle = {Proceedings of the IEEE Sensors Conference, 2012 : }, institution = {Örebro University, School of Science and Technology}, institution = {Dept. of System Engineering and Automation, University of Málaga, Spain}, institution = {Dept. of Civil Engineering, University of Málaga, Spain}, institution = {Dept. of System Engineering and Automation, University of Málaga, Spain}, pages = {1--4}, title = {Calibration of mox gas sensors in open sampling systems based on gaussian processes}, DOI = {10.1109/ICSENS.2012.6411464}, keywords = {Calibration, Estimation, Gas detectors, Microwave integrated circuits, Robot sensing systems, Training, Uncertainty}, abstract = {Calibration of metal oxide (MOX) gas sensor for continuous monitoring is a complex problem due to the highly dynamic characteristics of the gas sensor signal when exposed to natural environment (Open Sampling System - OSS). This work presents a probabilistic approach to the calibration of a MOX gas sensor based on Gaussian Processes (GP). The proposed approach estimates for every sensor measurement a probability distribution of the gas concentration. This enables the calculation of confidence intervals for the predicted concentrations. This is particularly important since exact calibration is hard to obtain due to the chaotic nature that dominates gas dispersal. The proposed approach has been tested with an experimental setup where an array of MOX sensors and a Photo Ionization Detector (PID) are placed downwind w.r.t. the gas source. The PID is used to obtain ground truth concentration. Comparison with standard calibration methods demonstrates the advantage of the proposed approach. }, ISBN = {9781457717659}, year = {2012} } @inproceedings{Pashami572463, author = {Pashami, Sepideh and Lilienthal, Achim J. and Trincavelli, Marco}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, title = {Change detection in an array of MOX sensors}, abstract = {In this article we present an algorithm for online detection of change points in the response of an array of metal oxide (MOX) gas sensors deployed in an open sampling system.True change points occur due to changes in the emission modality of the gas source. The main challenge for change point detection in an open sampling system is the chaotic nature of gas dispersion, which causes fluctuations in the sensor response that are not related to changes in the gas source. These fluctuations should not be considered change points in the sensor response. The presented algorithm is derived from the well known Generalized Likelihood Ratio algorithm and it is used both on the output of a single sensor as well on the output of two or more sensors on the array. The algorithm is evaluated with an experimental setup where a gas source changes in intensity, compound, or mixture ratio. The performance measures considered are the detection rate, the number of false alarms and the delay of detection. }, year = {2012} } @inproceedings{HernandezBennetts617886, author = {Hernandez Bennetts, Victor and Lilienthal, Achim J. and Trincavelli, Marco}, booktitle = {Sensors, 2012 IEEE : }, institution = {Örebro University, School of Science and Technology}, pages = {554--557}, title = {Creating true gas concentration maps in presence of multiple heterogeneous gas sources}, series = {IEEE Sensors}, DOI = {10.1109/ICSENS.2012.6411119}, keywords = {gas distribution mapping, mobile robots, environmental monitoring}, abstract = {Gas distribution mapping is a crucial task in emission monitoring and search and rescue applications. A common assumption made by state-of-the art mapping algorithms is that only one type of gaseous substance is present in the environment. For real world applications, this assumption can become very restrictive. In this paper we present an algorithm that creates gas concentration maps in a scenario where multiple heterogeneous gas sources are present. First, using an array of metal oxide (MOX) sensors and a pattern recognition algorithm, the chemical compound is identified. Then, for each chemical compound a gas concentration map using the readings of a Photo Ionization Detector (PID) is created. The proposed approach has been validated in experiments with the sensors mounted on a mobile robot which performed a predefined trajectory in a room where two gas sources emitting respectively ethanol and 2-propanol have been placed. }, ISBN = {978-1-4577-1766-6}, year = {2012} } @article{Pashami572461, author = {Pashami, Sepideh and Lilienthal, Achim J. and Trincavelli, Marco}, institution = {Örebro University, School of Science and Technology}, journal = {Sensors}, number = {12}, pages = {16404--16419}, publisher = {MDPI AG}, title = {Detecting changes of a distant gas source with an array of MOX gas sensors}, volume = {12}, DOI = {10.3390/s121216404}, keywords = {MOX sensor; open sampling system; sensor selection; change point detection}, abstract = {We address the problem of detecting changes in the activity of a distant gas source from the response of an array of metal oxide (MOX) gas sensors deployed in an open sampling system. The main challenge is the turbulent nature of gas dispersion and the response dynamics of the sensors. We propose a change point detection approach and evaluate it on individual gas sensors in an experimental setup where a gas source changes in intensity, compound, or mixture ratio. We also introduce an efficient sensor selection algorithm and evaluate the change point detection approach with the selected sensor array subsets. }, year = {2012} } @article{Stoyanov618701, author = {Stoyanov, Todor and Magnusson, Martin and Lilienthal, Achim J. and Andreasson, Henrik}, institution = {Örebro University, School of Science and Technology}, journal = {The international journal of robotics research}, note = {Funding Agencies:European Union FP7 - 270350Kunskaps och Kompetensutveckling Stiftelsen project SAUNA 20100315}, number = {12}, pages = {1377--1393}, title = {Fast and accurate scan registration through minimization of the distance between compact 3D NDT Representations}, volume = {31}, DOI = {10.1177/0278364912460895}, keywords = {point set registration; mapping; normal distributions transform}, abstract = {Registration of range sensor measurements is an important task in mobile robotics and has received a lot of attention. Several iterative optimization schemes have been proposed in order to align three-dimensional (3D) point scans. With the more widespread use of high-frame-rate 3D sensors and increasingly more challenging application scenarios for mobile robots, there is a need for fast and accurate registration methods that current state-of-the-art algorithms cannot always meet. This work proposes a novel algorithm that achieves accurate point cloud registration an order of a magnitude faster than the current state of the art. The speedup is achieved through the use of a compact spatial representation: the Three-Dimensional Normal Distributions Transform (3D-NDT). In addition, a fast, global-descriptor based on the 3D-NDT is defined and used to achieve reliable initial poses for the iterative algorithm. Finally, a closed-form expression for the covariance of the proposed method is also derived. The proposed algorithms are evaluated on two standard point cloud data sets, resulting in stable performance on a par with or better than the state of the art. The implementation is available as an open-source package for the Robot Operating system (ROS). }, year = {2012} } @inproceedings{HernandezBennetts1190204, author = {Hernandez Bennetts, Victor and Lilienthal, Achim and Khaliq, Ali Abdul and Pomareda Sese, Victor and Trincavelli, Marco}, booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Workshop on Robotics for Environmental Monitoring (WREM), Vilamoura, Portugal, October 7-12, 2012 : }, institution = {Örebro University, School of Science and Technology}, institution = {Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain}, institution = {Örebro University, Örebro, Sweden.}, title = {Gasbot : A Mobile Robotic Platform for Methane Leak Detection and Emission Monitoring}, keywords = {Mobile robot olfaction, remote sensor, landfill sites, gas tomography}, abstract = {Due to its environmental, economical and safety implications, methane leak detection is a crucial task to address in the biogas production industry. In this paper, we introduce Gasbot, a robotic platform that aims to automatize methane emission monitoring in landfills and biogas production sites. The distinctive characteristic of the Gasbot platform is the use of a Tunable Laser Absorption Spectroscopy (TDLAS) sensor, along with a novel gas distribution algorithm to generate methane concentration maps of indoor and outdoor exploration areas. The Gasbot platform has been tested in two different scenarios: an underground corridor, where a pipeline leak was simulated and in a decommissioned landfill site, where an artificial methane emission source was introduced. }, year = {2012} } @inproceedings{Saarinen1190203, author = {Saarinen, Jari and Andreasson, Henrik and Lilienthal, Achim}, booktitle = {2012 IEEE/RSJ International Conference on Intelligent Robots and Systems : }, institution = {Örebro University, School of Science and Technology}, institution = {Department of Automation and Systems Technology, Aalto University, Alto, Finland}, pages = {3489--3495}, title = {Independent Markov Chain Occupancy Grid Maps for Representation of Dynamic Environments}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2012.6385629}, keywords = {Markov chain, Poisson process, model of dynamics}, abstract = {In this paper we propose a new grid based approach to model a dynamic environment. Each grid cell is assumed to be an independent Markov chain (iMac) with two states. The state transition parameters are learned online and modeled as two Poisson processes. As a result, our representation not only encodes the expected occupancy of the cell, but also models the expected dynamics within the cell. The paper also presents a strategy based on recency weighting to learn the model parameters from observations that is able to deal with non-stationary cell dynamics. Moreover, an interpretation of the model parameters with discussion about the convergence rates of the cells is presented. The proposed model is experimentally validated using offline data recorded with a Laser Guided Vehicle (LGV) system running in production use. }, ISBN = {978-1-4673-1736-8}, ISBN = {978-1-4673-1737-5}, ISBN = {978-1-4673-1735-1}, year = {2012} } @article{HernandezBennetts524684, author = {Hernandez Bennetts, Victor and Lilienthal, Achim J. and Neumann, Patrick P. and Trincavelli, Marco}, institution = {Örebro University, School of Science and Technology}, institution = {BAM Federal Institute for Materials Research and Testing, Berlin, Germany}, journal = {Frontiers in Neuroengineering}, number = {20}, pages = {1--12}, title = {Mobile robots for localizing gas emission sources on landfill sites : is bio-inspiration the way to go?}, volume = {4}, DOI = {10.3389/fneng.2011.00020}, keywords = {Mobile Robotics, Mobile Robot Olfaction, Landfill Surveillance, Biologically Inspired Robots}, abstract = {Roboticists often take inspiration from animals for designing sensors, actuators, or algorithms that control the behavior of robots. Bio-inspiration is motivated with the uncanny ability of animals to solve complex tasks like recognizing and manipulating objects, walking on uneven terrains, or navigating to the source of an odor plume. In particular the task of tracking an odor plume up to its source has nearly exclusively been addressed using biologically inspired algorithms and robots have been developed, for example, to mimic the behavior of moths, dung beetles, or lobsters. In this paper we argue that biomimetic approaches to gas source localization are of limited use, primarily because animals differ fundamentally in their sensing and actuation capabilities from state-of-the-art gas-sensitive mobile robots. To support our claim, we compare actuation and chemical sensing available to mobile robots to the corresponding capabilities of moths. We further characterize airflow and chemosensor measurements obtained with three different robot platforms (two wheeled robots and one flying micro-drone) in four prototypical environments and show that the assumption of a constant and unidirectional airflow, which is the basis of many gas source localization approaches, is usually far from being valid. This analysis should help to identify how underlying principles, which govern the gas source tracking behavior of animals, can be usefully translated into gas source localization approaches that fully take into account the capabilities of mobile robots. We also describe the requirements for a reference application, monitoring of gas emissions at landfill sites with mobile robots, and discuss an engineered gas source localization approach based on statistics as an alternative to biologically inspired algorithms. }, year = {2012} } @inproceedings{Stoyanov524119, author = {Stoyanov, Todor and Magnusson, Martin and Lilienthal, Achim J.}, booktitle = {2012 IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, note = {Accepted for publication. Advance copy available at http://aass.oru.se/Research/Learning/publications/2012/Stoyanov_etal_2012-ICRA.pdf}, pages = {5196--5201}, title = {Point Set Registration through Minimization of the L-2 Distance between 3D-NDT Models}, series = {Proceedings - IEEE International Conference on Robotics and Automation}, DOI = {10.1109/ICRA.2012.6224717}, abstract = {Point set registration — the task of finding the best fitting alignment between two sets of point samples, is an important problem in mobile robotics. This article proposes a novel registration algorithm, based on the distance between Three- Dimensional Normal Distributions Transforms. 3D-NDT models — a sub-class of Gaussian Mixture Models with uniformly weighted, largely disjoint components, can be quickly computed from range point data. The proposed algorithm constructs 3DNDT representations of the input point sets and then formulates an objective function based on the L2 distance between the considered models. Analytic first and second order derivatives of the objective function are computed and used in a standard Newton method optimization scheme, to obtain the best-fitting transformation. The proposed algorithm is evaluated and shown to be more accurate and faster, compared to a state of the art implementation of the Iterative Closest Point and 3D-NDT Point-to-Distribution algorithms. }, ISBN = {9781467314053}, ISBN = {9781467314039}, year = {2012} } @inproceedings{Neumann541169, author = {Neumann, Patrick and Asadi, Sahar and Schiller, Jochen H. and Lilienthal, Achim J. and Bartholmai, Matthias}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Institute of Computer Science, Freie Universität Berlin, Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, pages = {34--38}, title = {An artificial potential field based sampling strategy for a gas-sensitive micro-drone}, keywords = {autonomous UAV, chemical sensing, gas distribution modelling, gas source localization, gas sensors, mobile sensing system, quadrocopter, sensor planning, artificial potential field.}, abstract = {This paper presents a sampling strategy for mobile gas sensors. Sampling points are selected using a modified artificial potential field (APF) approach, which balances multiple criteria to direct sensor measurements towards locations of high mean concentration, high concentration variance and areas for which the uncertainty about the gas distribution model is still large. By selecting in each step the most often suggested close-by measurement location, the proposed approach introduces a locality constraint that allows planning suitable paths for mobile gas sensors. Initial results in simulation and in real-world experiments witha gas-sensitive micro-drone demonstrate the suitability of the proposed sampling strategy for gas distribution mapping and its use for gas source localization. }, year = {2011} } @inproceedings{Stoyanov540987, author = {Stoyanov, Todor and Louloudi, Athanasia and Andreasson, Henrik and Lilienthal, Achim J.}, booktitle = {Proceedings of the 5th European Conference on Mobile Robots, ECMR 2011 : }, institution = {Örebro University, School of Science and Technology}, pages = {19--24}, title = {Comparative evaluation of range sensor accuracy in indoor environments}, abstract = {3D range sensing is one of the important topics in robotics, as it is often a component in vital autonomous subsystems like collision avoidance, mapping and semantic perception. The development of affordable, high frame rate and precise 3D range sensors is thus of considerable interest. Recent advances in sensing technology have produced several novel sensors that attempt to meet these requirements. This work is concerned with the development of a holistic method for accuracy evaluation of the measurements produced by such devices. A method for comparison of range sensor output to a set of reference distance measurements is proposed. The approach is then used to compare the behavior of three integrated range sensing devices, to that of a standard actuated laser range sensor. Test cases in an uncontrolled indoor environment are performed in order to evaluate the sensors’ performance in a challenging, realistic application scenario. }, year = {2011} } @inproceedings{Asadi540979, author = {Asadi, Sahar and Badica, Costin and Comes, Tina and Conrado, Claudine and Evers, Vanessa and Groen, Frans and Illie, Sorin and Steen Jensen, Jan and Lilienthal, Achim J. and Milan, Bianca and Neidhart, Thomas and Nieuwenhuis, Kees and Pashami, Sepideh and Pavlin, Gregor and Pehrsson, Jan and Pinchuk, Rani and Scafes, Mihnea and Schou-Jensen, Leo and Schultmann, Frank and Wijngaards, Niek}, booktitle = {Proceedings of the 25th EnviroInfo Conference "Environmental Informatics" : }, institution = {Örebro University, School of Science and Technology}, institution = {Örebro University, School of Science and Technology}, institution = {University of Craiova, Craiova, Romania}, institution = {Karslruhe Institute of Technology, Karslruhe, Germany}, institution = {Thales Research and Technology, Delft, The Netherlands}, institution = {University of Amsterdam, Amsterdam, The Netherlands}, institution = {University of Amsterdam, Amsterdam, The Netherlands}, institution = {University of Craiova, Craiova, Romania}, institution = {Danish Emergency Management Agency (DEMA), Birkerød, Denmark}, institution = {DCMR, Delft, The Netherlands}, institution = {Space Applications Services, Zaventem, Belgium}, institution = {Thales Research and Technology, Delft, The Netherlands}, institution = {Thales Research and Technology, Delft, The Netherlands}, institution = {Prolog Development Center, Brøndby Copenhagen, Denmark}, institution = {Space Applications and Services, Zaventem, Belgium}, institution = {University of Craiova, Craiova, Romania}, institution = {DCMR, Brøndby Copenhagen, Denmark}, institution = {Karslruhe Institute of Technology, Karlsruhe, Germany}, institution = {Thales Research and Technology, Delft, the Netherlands}, note = {DMCR: the joint environmental protection agency of the province of South Holland and 16 municipalities}, pages = {920--931}, title = {ICT solutions supporting collaborative information acquisition, situation assessment and decision making in contemporary environmental management problems : the DIADEM approach}, abstract = {This paper presents a framework of ICT solutions developed in the EU research project DIADEM that supports environmental management with an enhanced capacity to assess population exposure and health risks, to alert relevant groups and to organize efficient response. The emphasis is on advanced solutions which are economically feasible and maximally exploit the existing communication, computing and sensing resources. This approach enables efficient situation assessment in complex environmental management problems by exploiting relevant information obtained from citizens via the standard communication infrastructure as well as heterogeneous data acquired through dedicated sensing systems. This is achieved through a combination of (i) advanced approaches to gas detection and gas distribution modelling, (ii) a novel service-oriented approach supporting seamless integration of human-based and automated reasoning processes in large-scale collaborative sense making processes and (iii) solutions combining Multi-Criteria Decision Analysis, Scenario-Based Reasoning and advanced human-machine interfaces. This paper presents the basic principles of the DIADEM solutions, explains how different techniques are combined to a coherent decision support system and briefly discusses evaluation principles and activities in the DIADEM project. }, ISBN = {978-3-8440-0451-9}, year = {2011} } @incollection{Lilienthal540974, author = {Lilienthal, Achim J.}, booktitle = {Intelligent Systems for Machine Olfaction : Tools and Methodologies}, institution = {Örebro University, School of Science and Technology}, pages = {249--276}, title = {Improved gas source localization with a mobile robot by learning analytical gas dispersal models from statistical gas distribution maps using evolutionary algorithms}, DOI = {10.4018/978-1-61520-915-6.ch010}, abstract = {The method presented in this chapter computes an estimate of the location of a single gas sourcefrom a set of localised gas sensor measurements. The estimation process consists of three steps.First, a statistical model of the time-averaged gas distribution is estimated in the form of a two-dimensional grid map. In order to compute the gas distribution grid map the Kernel DM algorithm isapplied, which carries out spatial integration by convolving localised sensor readings and modelling theinformation content of the point measurements with a Gaussian kernel. The statistical gas distributiongrid map averages out the transitory effects of turbulence and converges to a representation of thetime-averaged spatial distribution of a target gas. The second step is to learn the parameters ofan analytical model of average gas distribution. Learning is achieved by nonlinear least squaresfitting of the analytical model to the statistical gas distribution map using Evolution Strategies (ES),which are a special type of Evolutionary Algorithms (EA). This step provides an analysis of thestatistical gas distribution map regarding the airflow conditions and an alternative estimate of thegas source location, i.e. the location predicted by the analytical model in addition to the location ofthe maximum in the statistical gas distribution map. In the third step, an improved estimate of thegas source position can then be derived by considering the maximum in the statistical gas distributionmap, the best fit as well as the corresponding fitness value. Different methods to select the mosttruthful estimate are introduced and a comparison regarding their accuracy is presented, based on atotal of 34 hours of gas distribution mapping experiments with a mobile robot. This chapter is anextended version of a paper by the authors (Lilienthal et al. [2005]). }, ISBN = {9781615209156}, year = {2011} } @inproceedings{Stoyanov524116, author = {Stoyanov, Todor and Magnusson, Martin and Almqvist, H{\aa}kan and Lilienthal, Achim J.}, booktitle = {2011 IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, note = {Proceedings athttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5501116}, title = {On the Accuracy of the 3D Normal Distributions Transform as a Tool for Spatial Representation}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ICRA.2011.5979584}, abstract = {The Three-Dimensional Normal Distributions Transform (3D-NDT) is a spatial modeling technique with applications in point set registration, scan similarity comparison, change detection and path planning. This work concentrates on evaluating three common variations of the 3D-NDT in terms of accuracy of representing sampled semi-structured environments. In a novel approach to spatial representation quality measurement, the 3D geometrical modeling task is formulated as a classification problem and its accuracy is evaluated with standard machine learning performance metrics. In this manner the accuracy of the 3D-NDT variations is shown to be comparable to, and in some cases to outperform that of the standard occupancy grid mapping model. }, ISBN = {978-1-61284-385-8}, year = {2011} } @inproceedings{Trincavelli475978, author = {Trincavelli, Marco and Vergara, A. and Rulkov, N. and Murguia, J. S. and Lilienthal, Achim J. and Huerta, R.}, booktitle = {Olfaction and electronic nose : Proceedings of the 14th international symposium on olfaction and electonic nose}, institution = {Örebro University, School of Science and Technology}, pages = {225--227}, title = {Optimizing the operating temperature for an array of MOX sensors on an open sampling system}, series = {AIP Conference Proceedings}, number = {1362}, DOI = {10.1063/1.3626368}, keywords = {Metal Oxide Gas Sensors, Operating Temperature Optimization, Air Pollution Monitoring, Gas Leak Detection}, abstract = {Chemo-resistive transduction is essential for capturing the spatio-temporal structure of chemical compounds dispersed in different environments. Due to gas dispersion mechanisms, namely diffusion, turbulence and advection, the sensors in an open sampling system, i.e. directly exposed to the environment to be monitored, are exposed to low concentrations of gases with many fluctuations making, as a consequence, the identification and monitoring of the gases even more complicated and challenging than in a controlled laboratory setting. Therefore, tuning the value of the operating temperature becomes crucial for successfully identifying and monitoring the pollutant gases, particularly in applications such as exploration of hazardous areas, air pollution monitoring, and search and rescue I. In this study we demonstrate the benefit of optimizing the sensor's operating temperature when the sensors are deployed in an open sampling system, i.e. directly exposed to the environment to be monitored. }, ISBN = {978-0-7354-0920-0}, year = {2011} } @inproceedings{Echelmeyer1190428, author = {Echelmeyer, Wolfgang and Kirchheim, Alice and Lilienthal, Achim and Akbiyik, H{\"u}lya and Bonini, Marco}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {University of Reutlingen, Reutlingen, Germany}, institution = {School of Science and Technology, Örebro University, Örebro, Sweden}, institution = {University of Reutlingen, Reutlingen, Germany}, institution = {University of Reutlingen, Reutlingen, Germany}, title = {Performance Indicators for Robotics Systems in Logistics Applications}, abstract = {The transfer of research results to market-ready products is often a costly and time-consuming process. In order to generate successful products, researchers must cooperate with industrial companies; both the industrial and academic partners need to have a detailed understanding of the requirements of all parties concerned. Academic researchers need to identify the performance indicators for technical systems within a business environment and be able to apply them. Inservice logistics today, nearly all standardized mass goods are unloaded manually with one reason for this being the undefined position and orientation of the goods in the carrier. A study regarding the qualitative and quantitative properties of goods that are transported in containers shows that there is a huge economic relevance for autonomous systems. In 2008, more than 8,4 billion Twenty-foot equivalent units (TEU) were imported and unloaded manually at European ports, corresponding to more than 331,000 billion single goods items. Besides the economic relevance, the opinion of market participants is an important factor for the success of new systems on the market. The main outcomes of a study regarding the challenges, opportunities and barriers in robotic-logistics, allow for the estimation of the economic efficiency of performance indicators, performance flexibility and soft factors. The economic efficiency of the performance parameters is applied to the parcel robot – a cognitive system to unload parcels autonomously from containers. In the following article, the results of the study are presented and the resultant conclusions discussed. }, year = {2011} } @article{Petrovitc444371, author = {Petrovitc, Ivan and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Robotics and Autonomous Systems}, note = {Editorial material.}, number = {5}, pages = {263--264}, title = {Special issue ECMR 2009}, volume = {59}, DOI = {10.1016/j.robot.2011.02.014}, year = {2011} } @incollection{Asadi541144, author = {Asadi, Sahar and Reggente, Matteo and Stachniss, Cyrill and Plagemann, Christian and Lilienthal, Achim J.}, booktitle = {Intelligent systems for machine olfaction : tools and methodologies}, edition = {1}, institution = {Örebro University, School of Science and Technology}, institution = {University of Freiburg, Freiburg, Germany}, institution = {Stanford University, Stanford CA, USA}, pages = {153--179}, title = {Statistical gas distribution modeling using kernel methods}, DOI = {10.4018/978-1-61520-915-6.ch006}, keywords = {Gas sensors, Gas distribution modelling, Statistical Gas Distribution Modelling, Kernel density estimation, Kernel regression, Gaussian Processes, Gaussian Process Mixture Models, Environmental monitoring, Gas source localization}, abstract = {Gas distribution models can provide comprehensive information about a large number of gas concentration measurements, highlighting, for example, areas of unusual gas accumulation. They can also help to locate gas sources and to plan where future measurements should be carried out. Current physical modeling methods, however, are computationally expensive and not applicable for real world scenarios with real-time and high resolution demands. This chapter reviews kernel methodsthat statistically model gas distribution. Gas measurements are treated as randomvariables, and the gas distribution is predicted at unseen locations either using akernel density estimation or a kernel regression approach. The resulting statistical  apmodelsdo not make strong assumptions about the functional form of the gas distribution,such as the number or locations of gas sources, for example. The majorfocus of this chapter is on two-dimensional models that provide estimates for themeans and predictive variances of the distribution. Furthermore, three extensionsto the presented kernel density estimation algorithm are described, which allow toinclude wind information, to extend the model to three dimensions, and to reflecttime-dependent changes of the random process that generates the gas distributionmeasurements. All methods are discussed based on experimental validation usingreal sensor data. }, ISBN = {9781615209156}, year = {2011} } @inproceedings{Asadi540989, author = {Asadi, Sahar and Pashami, Sepideh and Loutfi, Amy and Lilienthal, Achim J.}, booktitle = {Olfaction and Electronic Nose : proceedings of the 14th International Symposium on Olfaction and Electronic Nose (ISOEN)}, institution = {Örebro University, School of Science and Technology}, pages = {281--282}, title = {TD Kernel DM+V : time-dependent statistical gas distribution modelling on simulated measurements}, series = {AIP Conference Proceedings}, number = {1362}, DOI = {10.1063/1.3651651}, abstract = {To study gas dispersion, several statistical gas distribution modelling approaches have been proposed recently. A crucial assumption in these approaches is that gas distribution models are learned from measurements that are generated by a time-invariant random process. While a time-independent random process can capture certain fluctuations in the gas distribution, more accurate models can be obtained by modelling changes in the random process over time. In this work we propose a time-scale parameter that relates the age of measurements to their validity for building the gas distribution model in a recency function. The parameters of the recency function define a time-scale and can be learned. The time-scale represents a compromise between two conflicting requirements for obtaining accurate gas distribution models: using as many measurements as possible and using only very recent measurements. We have studied several recency functions in a time-dependent extension of the Kernel DM+V algorithm (TD Kernel DM+V). Based on real-world experiments and simulations of gas dispersal (presented in this paper) we demonstrate that TD Kernel DM+V improves the obtained gas distribution models in dynamic situations. This represents an important step towards statistical modelling of evolving gas distributions. }, ISBN = {978-0-7354-0920-0}, year = {2011} } @article{Andreasson274835, author = {Andreasson, Henrik and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Robotics and Autonomous Systems}, note = {Selected papers from the 2007 European Conference on Mobile Robots (ECMR ’07)}, number = {2}, pages = {157--165}, title = {6D scan registration using depth-interpolated local image features}, volume = {58}, DOI = {10.1016/j.robot.2009.09.011}, keywords = {Registration, Vision, Laser Range Finder, SLAM}, abstract = {This paper describes a novel registration approach that is based on a combination of visual and 3D range information.To identify correspondences, local visual features obtained from images of a standard color camera are compared and the depth of matching features (and their position covariance) is determined from the range measurements of a 3D laserscanner. The matched depth-interpolated image features allows to apply registration with known correspondences.We compare several ICP variants in this paper and suggest an extension that considers the spatial distance betweenmatching features to eliminate false correspondences. Experimental results are presented in both outdoor and indoor environments. In addition to pair-wise registration, we also propose a global registration method that registers allscan poses simultaneously. }, year = {2010} } @article{Cielniak383180, author = {Cielniak, Grzegorz and Duckett, Tom and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Sch Comp Sci, Lincoln Univ, Lincoln, England}, institution = {Sch Comp Sci, Lincoln Univ, Lincoln, England}, journal = {Robotics and Autonomous Systems}, number = {5}, pages = {435--443}, title = {Data association and occlusion handling for vision-based people tracking by mobile robots}, volume = {58}, DOI = {10.1016/j.robot.2010.02.004}, keywords = {AdaBoost, Occlusion detection, Thermal vision, Colour vision, Bayesian estimation}, abstract = {This paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is incorporated into the tracker. The paper presents a comprehensive, quantitative evaluation of the whole system and its different components using several real world data sets. (C) 2010 Elsevier B.V. All rights reserved. }, year = {2010} } @inproceedings{Ferri524121, author = {Ferri, Gabriele and Mondini, Alessio and Manzi, Alessandro and Mazzolai, Barbara and Laschi, Cecilia and Mattoli, Virgilio and Reggente, Matteo and Stoyanov, Todor and Lilienthal, Achim J. and Lettere, Marco and Dario, Paolo.}, booktitle = {Proceedings of ICRA Workshop on Networked and Mobile Robot Olfaction in Natural, Dynamic Environments : }, institution = {Örebro University, School of Science and Technology}, institution = {Scuola Superiore Sant'Anna, Pisa, Italy}, institution = {Scuola Superiore Sant'Anna, Pisa, Italy}, institution = {Scuola Superiore Sant'Anna, Pisa, Italy}, institution = {Scuola Superiore Sant'Anna, Pisa, Italy}, institution = {Scuola Superiore Sant'Anna, Pisa, Italy}, institution = {Scuola Superiore Sant'Anna, Pisa, Italy}, institution = {Scuola Superiore Sant'Anna, Pisa, Italy}, institution = {Scuola Superiore Sant'Anna, Pisa, Italy}, note = {Conference url: http://icra2010.grasp.upenn.edu/?q=overview}, title = {DustCart, a Mobile Robot for Urban Environments : Experiments of Pollution Monitoring and Mapping during Autonomous Navigation in Urban Scenarios}, keywords = {mobile robots, urban robots, gas mapping, navigation}, abstract = {In the framework of DustBot European project, aimed at developing a new multi-robot system for urban hygiene management, we have developed a twowheeled robot: DustCart. DustCart aims at providing a solution to door-to-door garbage collection: the robot, called by a user, navigates autonomously to his/her house; collects the garbage from the user and discharges it in an apposite area. An additional feature of DustCart is the capability to monitor the air pollution by means of an on board Air Monitoring Module (AMM). The AMM integrates sensors to monitor several atmospheric pollutants, such as carbon monoxide (CO), particular matter (PM10), nitrogen dioxide (NO2), ozone (O3) plus temperature (T) and relative humidity (rHu). An Ambient Intelligence platform (AmI) manages the robots’ operations through a wireless connection. AmI is able to collect measurements taken by different robots and to process them to create a pollution distribution map. In this paper we describe the DustCart robot system, focusing on the AMM and on the process of creating the pollutant distribution maps. We report results of experiments of one DustCart robot moving in urban scenarios and producing gas distribution maps using the Kernel DM+V algorithm. These experiments can be considered as one of the first attempts to use robots as mobile monitoring devices that can complement the traditional fixed stations. }, year = {2010} } @inproceedings{Pashami534451, author = {Pashami, Sepideh and Asadi, Sahar and Lilienthal, Achim J.}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, note = {Proceedings available (after registration) athttp://www.opensourcecfd.com/conference2010/proceedings/content/home.php}, title = {Integration of OpenFOAM Flow Simulation and Filament-Based Gas Propagation Models for Gas Dispersion Simulation}, keywords = {Gas dispersion, CFD, OpenFOAM}, abstract = {In this paper, we present a gas dispersal simulation package which integrates OpenFOAM flow simulation and a filament-based gas propagation model to simulate gas dispersion for compressible flows with a realistic turbulence model. Gas dispersal simulation can be useful for many applications. In this paper, we focus on the evaluation of statistical gas distribution models. Simulated data offer several advantages for this purpose, including the availability of ground truth information, repetition of experiments with the exact same constraints and that intricate issue which come with using real gas sensors can be avoided.Apart from simulation results obtained in a simulated wind tunnel (designed to be equivalent to its real-world counterpart), we present initial results with time-independent and time-dependent statistical modelling approaches applied to simulated and real-world data. }, year = {2010} } @inproceedings{Stoyanov445259, author = {Stoyanov, Todor and Magnusson, Martin and Andreasson, Henrik and Lilienthal, Achim J.}, booktitle = {IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems (IROS 2010) : }, institution = {Örebro University, School of Science and Technology}, pages = {3263--3268}, title = {Path planning in 3D environments using the normal distributions transform}, DOI = {10.1109/IROS.2010.5650789}, abstract = {Planning feasible paths in fully three-dimensional environments is a challenging problem. Application of existing algorithms typically requires the use of limited 3D representations that discard potentially useful information. This article proposes a novel approach to path planning that utilizes a full 3D representation directly: the Three-Dimensional Normal Distributions Transform (3D-NDT). The well known wavefront planner is modified to use 3D-NDT as a basis for map representation and evaluated using both indoor and outdoor data sets. The use of 3D-NDT for path planning is thus demonstrated to be a viable choice with good expressive capabilities. }, ISBN = {978-1-4244-6675-7}, year = {2010} } @article{Valgren306578, author = {Valgren, Christoffer and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Department of Computer Science, Örebro University, Örebro, Sweden}, journal = {Robotics and Autonomous Systems}, number = {2}, pages = {149--156}, title = {SIFT, SURF {\&}amp; seasons : Appearance-based long-term localization in outdoor environments}, volume = {58}, DOI = {10.1016/j.robot.2009.09.010}, keywords = {Localization, Scene Recognition, Outdoor Environments}, abstract = {In this paper, we address the problem of outdoor, appearance-based topological localization, particularly over long periods of time where seasonal changes alter the appearance of the environment. We investigate a straight-forward method that relies on local image features to compare single image pairs. We rst look into which of the dominating image feature algorithms, SIFT or the more recent SURF, that is most suitable for this task. We then ne-tune our localization algorithm in terms of accuracy, and also introduce the epipolar constraint to further improve the result. The nal localization algorithm is applied on multiple data sets, each consisting of a large number of panoramic images, which have been acquired over a period of nine months with large seasonal changes. The nal localization rate in the single-image matching, cross-seasonal case is between 80 to 95%. }, year = {2010} } @inproceedings{Reggente444953, author = {Reggente, Matteo and Lilienthal, Achim J.}, booktitle = {2010 IEEE SENSORS}, institution = {Örebro University, School of Science and Technology}, pages = {999--1004}, title = {The 3D-kernel DM+V/W algorithm : using wind information in three dimensional gas distribution modelling with a mobile robot}, series = {IEEE Sensors}, DOI = {10.1109/ICSENS.2010.5690924}, abstract = {In this paper we present a statistical method to build three-dimensional gas distribution maps from gas sensor and wind measurements obtained with a mobile robot in uncontrolled environments. The particular contribution of this paper is to introduce and evaluate an algorithm for 3D statistical gas distribution mapping, that takes into account airflow information. 3D-Kernel DM+V/W algorithm uses a multivariate Gaussian weighting function to model the information provided by the gas sensors and an ultrasonic anemometer. The proposed algorithm is evaluated with respect to the ability of the obtained models to predict unseen measurements. The results based on 15 trials with a mobile robot in an indoor environment show improvements in the model performance when using the 3D kernel DM+V/W algorithm. Moreover the model is able to adapt to the dynamical changes of the environment learning the hyper-parameter from the sensors readings. }, ISBN = {978-1-4244-8168-2}, year = {2010} } @article{Reggente445325, author = {Reggente, Matteo and Mondini, Alessio and Ferri, Gabriele and Mazzolai, Barbara and Manzi, Alessandro and Gabelletti, Matteo and Dario, Paolo and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {CRIM Laboratory, Scuola Superiore Sant'Anna, Pisa, Italy }, institution = {CRIM Laboratory, Scuola Superiore Sant'Anna, Pisa, Italy }, institution = {Centre in MicroBioRobotics IIT at SSSA, Italian Institute of Technology, Pisa, Italy }, institution = {Arts Laboratory, Scuola Superiore Sant'Anna, Pisa, Italy }, institution = {Arts Laboratory, Scuola Superiore Sant'Anna, Pisa, Italy }, institution = {CRIM Laboratory, Scuola Superiore Sant'Anna, Pisa, Italy }, journal = {Chemical Engineering Transactions}, pages = {273--278}, publisher = {AIDIC Servizi}, title = {The DustBot System : Using Mobile Robots to Monitor Pollution in Pedestrian Area}, volume = {23}, DOI = {10.3303/CET1023046}, abstract = {The EU project DustBot addresses urban hydeience. Two types of robots were designed, the DustClean robot to autonomously clean pedestrian areas, and the DustCart robot for door-to-door garbage collection. Three prototype robots were built and equipped with electronic noses so as to enable them to collect environmental data while performing their urban hygiene tasks. Essentially, the robots act as a mobile, wirless node in a sensor network. In this paper we give an overview of the DusBot platform focusig on the Air Monitoring Module (AMM). We descibe the data flow between the robots throught the ubiquitous network to a gas distribution modelling server, where a gas deisribution model is computed. We descibe the Kernel DM+V algorithn, an approach to create statistical gas disdtribution models in the form of predictive mean and variance discrtized onto a grid map. Finally we present and discuss results obtained with the DustBot AMM during experimental trails performex in outdoor public places; a courtyard in Pontedera, Italy and a pedestrian square in Örebro, Sweden. }, ISBN = {978-88-95608-14-3}, year = {2010} } @inproceedings{Lilienthal274853, author = {Lilienthal, Achim J. and Reggente, Matteo and Trincavelli, Marco and Blanco, Jose Luis and Gonzalez, Javier}, booktitle = {IEEE/RSJ international conference on intelligent robots and systems : IROS 2009}, institution = {Örebro University, School of Science and Technology}, institution = {Dept. of System Engineering and Automation, University of Malaga}, pages = {570--576}, title = {A statistical approach to gas distribution modelling with mobile robots : the Kernel DM+V algorithm}, series = {IEEE Conference Publications}, DOI = {10.1109/IROS.2009.5354304}, abstract = {Gas distribution modelling constitutes an ideal application area for mobile robots, which – as intelligent mobile gas sensors – offer several advantages compared to stationary sensor networks. In this paper we propose the Kernel DM+V algorithm to learn a statistical 2-d gas distribution model from a sequence of localized gas sensor measurements. The algorithm does not make strong assumptions about the sensing locations and can thus be applied on a mobile robot that is not primarily used for gas distribution monitoring, and also in the case of stationary measurements. Kernel DM+V treats distribution modelling as a density estimation problem. In contrast to most previous approaches, it models the variance in addition to the distribution mean. Estimating the predictive variance entails a significant improvement for gas distribution modelling since it allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. Estimating the predictive variance also provides the means to learn meta parameters and to suggest new measurement locations based on the current model. We derive the Kernel DM+V algorithm and present a method for learning the hyper-parameters. Based on real world data collected with a mobile robot we demonstrate the consistency of the obtained maps and present a quantitative comparison, in terms of the data likelihood of unseen samples, with an alternative approach that estimates the predictive variance. }, ISBN = {978-1-4244-3803-7}, year = {2009} } @inproceedings{Astrand274865, author = {{\AA}strand, Bj{\"o}rn and R{\"o}gnvaldsson, Thorsteinn and Bouguerra, Abdelbaki and Andreasson, Henrik and Lilienthal, Achim J.}, booktitle = {Proceedings of the 4th Swedish Workshop on Autonomous Robotics (SWAR)}, institution = {Örebro University, School of Science and Technology}, institution = {Halmstad University}, institution = {Halmstad University}, pages = {56--57}, title = {An Autonomous Robotic System for Load Transportation}, year = {2009} } @inproceedings{Bouguerra274885, author = {Bouguerra, Abdelbaki and Andreasson, Henrik and Lilienthal, Achim J. and {\AA}strand, Bj{\"o}rn and R{\"o}gnvaldsson, Thorsteinn}, booktitle = {2009 IEEE Conference on Emerging Technologies & Factory Automation (EFTA 2009) : }, institution = {Örebro University, School of Science and Technology}, institution = {Halmstad University, Halmstad, Sweden}, institution = {Halmstad University, Halmstad, Sweden}, pages = {1563--1566}, title = {An autonomous robotic system for load transportation}, series = {IEEE International Conference on Emerging Technologies and Factory Automation-ETFA}, DOI = {10.1109/ETFA.2009.5347247}, keywords = {AGV system; Autonomous robotic systems; Dynamic environments; Material handling; Object Detection; Runtimes}, abstract = {This paper presents an overview of an autonomous robotic material handling system. The goal of the system is to extend the functionalities of traditional AGVs to operate in highly dynamic environments. Traditionally, the reliable functioning of AGVs relies on the availability of adequate infrastructure to support navigation. In the target environments of our system, such infrastructure is difficult to setup in an efficient way. Additionally, the location of objects to handle are unknown, which requires that the system be able to detect and track object positions at runtime. Another requirement of the system is to be able to generate trajectories dynamically, which is uncommon in industrial AGV systems. }, ISBN = {978-1-4244-2727-7}, ISBN = {978-1-4244-2728-4}, year = {2009} } @inproceedings{Magnusson391763, author = {Magnusson, Martin and Andreasson, Henrik and N{\"u}chter, A. and Lilienthal, Achim J.}, booktitle = {IEEE International Conference on Robotics and Automation 2009 (ICRA '09) : }, institution = {Örebro University, School of Science and Technology}, institution = {Jacobs University Bremen, Bremen, Germany}, note = {Funding Agency:Atlas Copco Rock Drills }, pages = {23--28}, title = {Appearance-based loop detection from 3D laser data using the normal distributions transform}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ROBOT.2009.5152712}, abstract = {We propose a new approach to appearance based loop detection from metric 3D maps, exploiting the NDT surface representation. Locations are described with feature histograms based on surface orientation and smoothness, and loop closure can be detected by matching feature histograms. We also present a quantitative performance evaluation using two realworld data sets, showing that the proposed method works well in different environments.© 2009 IEEE. }, ISBN = {9781424427888}, ISBN = {9781424427895}, year = {2009} } @article{Magnusson274842, author = {Magnusson, Martin and Andreasson, Henrik and N{\"u}chter, Andreas and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Jacobs University Bremen}, journal = {Journal of Field Robotics}, number = {11-12}, pages = {892--914}, title = {Automatic appearance-based loop detection from three-dimensional laser data using the normal distributions transform}, volume = {26}, DOI = {10.1002/rob.20314}, abstract = {We propose a new approach to appearance-based loop detection for mobile robots, usingthree-dimensional (3D) laser scans. Loop detection is an important problem in the simultaneouslocalization and mapping (SLAM) domain, and, because it can be seen as theproblem of recognizing previously visited places, it is an example of the data associationproblem. Without a flat-floor assumption, two-dimensional laser-based approaches arebound to fail in many cases. Two of the problems with 3D approaches that we address inthis paper are how to handle the greatly increased amount of data and how to efficientlyobtain invariance to 3D rotations.We present a compact representation of 3D point cloudsthat is still discriminative enough to detect loop closures without false positives (i.e.,detecting loop closure where there is none). A low false-positive rate is very important becausewrong data association could have disastrous consequences in a SLAM algorithm.Our approach uses only the appearance of 3D point clouds to detect loops and requires nopose information. We exploit the normal distributions transform surface representationto create feature histograms based on surface orientation and smoothness. The surfaceshape histograms compress the input data by two to three orders of magnitude. Becauseof the high compression rate, the histograms can be matched efficiently to compare theappearance of two scans. Rotation invariance is achieved by aligning scans with respectto dominant surface orientations. We also propose to use expectation maximization to fit a gamma mixture model to the output similarity measures in order to automatically determinethe threshold that separates scans at loop closures from nonoverlapping ones.Wediscuss the problem of determining ground truth in the context of loop detection and thedifficulties in comparing the results of the few available methods based on range information.Furthermore, we present quantitative performance evaluations using three realworlddata sets, one of which is highly self-similar, showing that the proposed methodachieves high recall rates (percentage of correctly identified loop closures) at low falsepositiverates in environments with different characteristics. }, year = {2009} } @inproceedings{Lilienthal274903, author = {Lilienthal, Achim J. and Asadi, Sahar and Reggente, Matteo}, booktitle = {Olfaction and electronic nose : proceedings}, institution = {Örebro University, School of Science and Technology}, pages = {65--68}, title = {Estimating predictive variance for statistical gas distribution modelling}, series = {AIP conference proceedings}, number = {1137}, DOI = {10.1063/1.3156628}, keywords = {Gas distribution modelling, gas sensing, mobile robot olfaction, density estimation, model evaluation}, abstract = {Recent publications in statistical gas distribution modelling have proposed algorithms that model mean and variance of a distribution. This paper argues that estimating the predictive concentration variance entails not only a gradual improvement but is rather a significant step to advance the field. This is, first, since the models much better fit the particular structure of gas distributions, which exhibit strong fluctuations with considerable spatial variations as a result of the intermittent character of gas dispersal. Second, because estimating the predictive variance allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. It also enables solid comparisons of different modelling approaches, and provides the means to learn meta parameters of the model, to determine when the model should be updated or re-initialised, or to suggest new measurement locations based on the current model. We also point out directions of related ongoing or potential future research work. }, ISBN = {978-0-7354-0674-2}, year = {2009} } @inproceedings{Magnusson274922, author = {Magnusson, Martin and N{\"u}chter, Andreas and L{\"o}rken, Christopher and Lilienthal, Achim J. and Hertzberg, Joachim}, booktitle = {Proceedings of the 2009 IEEE international conference on Robotics and Automation, ICRA'09 : }, institution = {Örebro University, School of Science and Technology}, institution = {Jacobs University Bremen, Bremen, Germany; Knowledge Systems Research Group of the Institute of Computer Science, University of Osnabrück, Germany}, institution = {Institute of Computer Science, University of Osnabrück, Germany}, institution = {Institute of Computer Science, University of Osnabrück, Germany}, note = {Funding Agency:Atlas Copco Rock Drills }, pages = {2263--2268}, title = {Evaluation of 3D registration reliability and speed : a comparison of ICP and NDT}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ROBOT.2009.5152538}, abstract = {To advance robotic science it is important to perform experiments that can be replicated by other researchers to compare different methods. However, these comparisons tend to be biased, since re-implementations of reference methods often lack thoroughness and do not include the hands-on experience obtained during the original development process. This paper presents a thorough comparison of 3D scan registration algorithms based on a 3D mapping field experiment, carried out by two research groups that are leading in the field of 3D robotic mapping. The iterative closest points algorithm (ICP) is compared to the normal distributions transform (NDT). We also present an improved version of NDT with a substantially larger valley of convergence than previously published versions. }, ISBN = {9781424427888}, ISBN = {9781424427895}, year = {2009} } @inproceedings{Charusta274830, author = {Charusta, Krzysztof and Dimitrov, Dimitar and Lilienthal, Achim J. and Iliev, Boyko}, booktitle = {2009 International Conference on Advanced Robotics : }, institution = {Örebro University, School of Science and Technology}, pages = {1--6}, title = {Extraction of grasp-related features by human dual-hand object exploration}, keywords = {robotic grasping, programming-by-demonstration}, abstract = {We consider the problem of objects exploration for grasping purposes, specifically in cases where vision based methods are not applicable. A novel dual-hand object exploration method is proposed that takes benefits from a human demonstration to enrich knowledge about an object. The user handles an object freely using both hands, without restricting the object pose. A set of grasp-related features obtained during exploration is demonstrated and utilized to generate grasp oriented bounding boxes that are basis for pre-grasp hypothesis. We believe that such exploration done in a natural and user friendly way creates important link between an operator intention and a robot action. }, URL = {https://ieeexplore.ieee.org/document/5174680}, ISBN = {978-1-4244-4855-5}, year = {2009} } @article{Loutfi274847, author = {Loutfi, Amy and Coradeschi, Silvia and Lilienthal, Achim J. and Gonzalez, Javier}, institution = {Örebro University, School of Science and Technology}, institution = {Dept. of System Engineering and Automation, University of Malaga}, journal = {Robotica (Cambridge. Print)}, number = {2}, pages = {311--319}, title = {Gas Distribution Mapping of Multiple Odour Sources using a Mobile Robot}, volume = {27}, DOI = {10.1017/S0263574708004694}, abstract = {Mobile olfactory robots can be used in a number of relevant application areas where a better understanding of agas distribution is needed, such as environmental monitoring and safety and security related fields. In this paper wepresent a method to integrate the classification of odours together with gas distribution mapping. The resulting odourmap is then correlated with the spatial information collected from a laser range scanner to form a combined map.Experiments are performed using a mobile robot in large and unmodified indoor and outdoor environments. Multipleodour sources are used and are identified using only transient information from the gas sensor response. The resultingmulti level map can be used as a intuitive representation of the collected odour data for a human user. }, year = {2009} } @article{Stachniss274845, author = {Stachniss, Cyrill and Plagemann, Christian and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {University of Freiburg}, institution = {Stanford University}, journal = {Autonomous Robots}, number = {2-3}, pages = {187--202}, title = {Learning Gas Distribution Models Using Sparse Gaussian Process Mixtures}, volume = {26}, DOI = {10.1007/s10514-009-9111-5}, keywords = {Gas distribution modeling, Gas sensing, Gaussian processes, Mixture models}, abstract = {In this paper, we consider the problem of learning two-dimensional spatial models of gas distributions. To build models of gas distributions that can be used to accurately predict the gas concentration at query locations is a challenging task due to the chaotic nature of gas dispersal. We formulate this task as a regression problem. To deal with the specific properties of gas distributions, we propose a sparse Gaussian process mixture model, which allows us to accurately represent the smooth background signal and the areas with patches of high concentrations. We furthermore integrate the sparsification of the training data into an EM procedure that we apply for learning the mixture components and the gating function. Our approach has been implemented and tested using datasets recorded with a real mobile robot equipped with an electronic nose. The experiments demonstrate that our technique is well-suited for predicting gas concentrations at new query locations and that it outperforms alternative and previously proposed methods in robotics. }, year = {2009} } @inproceedings{Bouguerra274878, author = {Bouguerra, Abdelbaki and Andreasson, Henrik and Lilienthal, Achim J. and {\AA}strand, Bj{\"o}rn and R{\"o}gnvaldsson, Thorsteinn}, booktitle = {Proceedings of the 4th European conference on mobile robots (ECMR) : }, institution = {Örebro University, School of Science and Technology}, institution = {Halmstad University}, institution = {Halmstad University, Sweden}, pages = {93--98}, title = {MALTA : a system of multiple autonomous trucks for load transportation}, keywords = {Autonomous Vehicles, Load Handling, AGVs}, abstract = {This paper presents an overview of an autonomousrobotic material handling system. The goal of the system is toextend the functionalities of traditional AGVs to operate in highlydynamic environments. Traditionally, the reliable functioning ofAGVs relies on the availability of adequate infrastructure tosupport navigation. In the target environments of our system,such infrastructure is difficult to setup in an efficient way.Additionally, the location of objects to handle are unknown,which requires that the system be able to detect and track objectpositions at runtime. Another requirement of the system is to beable to generate trajectories dynamically, which is uncommon inindustrial AGV systems. }, ISBN = {978-953-6037-54-4}, year = {2009} } @inproceedings{Stoyanov524115, author = {Stoyanov, Todor and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE International Conference on Advanced Robotics (ICAR) : }, institution = {Örebro University, School of Science and Technology}, note = {Proceedings athttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5166725}, title = {Maximum Likelihood Point Cloud Acquisition from a Rotating Laser Scanner on a Moving Platform}, abstract = {This paper describes an approach to acquire locally consistent range data scans from a moving sensor platform. Data from a vertically mounted rotating laser scanner and odometry position estimates are fused and used to estimate maximum likelihood point clouds. An estimation algorithm is applied to reduce the accumulated error after a full rotation of the range finder. A configuration consisting of a SICK laser scanner mounted on a rotational actuator is described and used to evaluate the proposed approach. The data sets analyzed suggest a significant improvement in point cloud consistency, even over a short travel distance. }, URL = {https://ieeexplore.ieee.org/abstract/document/5174672}, year = {2009} } @inproceedings{Stoyanov274893, author = {Stoyanov, Todor and Lilienthal, Achim J.}, booktitle = {International conference on advanced robotics, ICAR 2009. : }, institution = {Örebro University, School of Science and Technology}, pages = {1--6}, title = {Maximum likelihood point cloud acquisition from a mobile platform}, abstract = {This paper describes an approach to acquire locally consistent range data scans from a moving sensor platform. Data from a vertically mounted rotating laser scanner and odometry position estimates are fused and used to estimate maximum likelihood point clouds. An estimation algorithm is applied to reduce the accumulated error after a full rotation of the range finder. A configuration consisting of a SICK laser scanner mounted on a rotational actuator is described and used to evaluate the proposed approach. The data sets analyzed suggest a significant improvement in point cloud consistency, even over a short travel distance. }, ISBN = {978-1-4244-4855-5}, year = {2009} } @inproceedings{Reggente274869, author = {Reggente, Matteo and Lilienthal, Achim J.}, booktitle = {Proceedings of Eurosensors XXIII conference : }, institution = {Örebro University, School of Science and Technology}, note = {Ing{\aa}r i: Procedia Chemistry (ISSN: 1876-6196) Volume 1, Issue 1, 2009}, pages = {481--484}, title = {Statistical evaluation of the kernel DM+V/W algorithm for building gas distribution maps in uncontrolled environments}, series = {Procedia Chemistry}, number = {1}, volume = {1}, DOI = {10.1016/j.proche.2009.07.120}, keywords = {gas distribution; e-nose; gas sensing; mobile robots; kernel density estimation; model evaluation}, abstract = {In this paper we present a statistical evaluation of the Kernel DM+V/W algorithm to build two-dimensional gas distribution maps with a mobile robot. In addition to gas sensor measurements from an "e-nose" the Kernel DM+V/W algorithm also takes into account wind information received from an ultrasonic anemometer. We evaluate the method based on real measurements in three uncontrolled environments with very different properties. As a measure for the model quality we compute how well unseen measurements are predicted in terms of the data likelihood. A paired Wilcoxon signed rank test shows a significant improvement (at a confidence level of 95%) of the model quality when using wind information. }, year = {2009} } @inproceedings{Reggente274906, author = {Reggente, Matteo and Lilienthal, Achim J.}, booktitle = {Olfaction and electronic nose : }, institution = {Örebro University, School of Science and Technology}, pages = {109--112}, title = {Three-dimensional statistical gas distribution mapping in an uncontrolled indoor environment}, series = {AIP conference proceedings}, number = {1137}, DOI = {10.1063/1.3156484}, keywords = {3D-gas distribution, e-nose, gas sensing, mobile robots, kernel density estimation, model evaluation}, abstract = {In this paper we present a statistical method to build three-dimensional gas distribution maps (3D-DM). The proposed mapping technique uses kernel extrapolation with a tri-variate Gaussian kernel that models the likelihood that a reading represents the concentration distribution at a distant location in the three dimensions. The method is evaluated using a mobile robot equipped with three "e-noses" mounted at different heights. Initial experiments in an uncontrolled indoor environment are presented and evaluated with respect to the ability of the 3D map, computed from the lower and upper nose, to predict the map from the middle nose. }, ISBN = {978-0-7354-0674-2}, year = {2009} } @inproceedings{Reggente274849, author = {Reggente, Matteo and Lilienthal, Achim J.}, booktitle = {IEEE sensors, vols 1-3 : }, institution = {Örebro University, School of Science and Technology}, pages = {1637--1642}, title = {Using local wind information for gas distribution mapping in outdoor environments with a mobile robot}, DOI = {10.1109/ICSENS.2009.5398498}, abstract = {In this paper we introduce a statistical method tobuild two-dimensional gas distribution maps (Kernel DM+V/Walgorithm). In addition to gas sensor measurements, the proposedmethod also takes into account wind information by modelingthe information content of the gas sensor measurements as abivariate Gaussian kernel whose shape depends on the measuredwind vector. We evaluate the method based on real measurementsin an outdoor environment obtained with a mobile robot thatwas equipped with gas sensors and an ultrasonic anemometerfor wind measurements. As a measure of the model quality wecompute how well unseen measurements are predicted in termsof the data likelihood. The initial results are encouraging andshow a clear improvement of the proposed method compared tothe case where wind is not considered. }, ISBN = {978-1-4244-4548-6}, year = {2009} } @incollection{Reggente613834, author = {Reggente, Matteo and Lilienthal, Achim J.}, booktitle = {2009 IEEE SENSORS, VOLS 1-3 : }, institution = {Örebro University, School of Science and Technology}, pages = {1715--1720}, title = {Using local wind information for gas distribution mapping in outdoor environments with a mobile robot}, series = {2009 IEEE SENSORS, VOLS 1-3}, DOI = {10.1109/ICSENS.2009.5398498}, abstract = {In this paper we introduce a statistical method to build two-dimensional gas distribution maps (Kernel DM+V/W algorithm). In addition to gas sensor measurements, the proposed method also takes into account wind information by modeling the information content of the gas sensor measurements as a bivariate Gaussian kernel whose shape depends on the measured wind vector. We evaluate the method based on real measurements in an outdoor environment obtained with a mobile robot that was equipped with gas sensors and an ultrasonic anemometer for wind measurements. As a measure of the model quality we compute how well unseen measurements are predicted in terms of the data likelihood. The initial results are encouraging and show a clear improvement of the proposed method compared to the case where wind is not considered. }, ISBN = {978-1-4244-4548-6}, year = {2009} } @inproceedings{Magnusson137559, author = {Magnusson, Martin and N{\"u}chter, Andreas and L{\"o}rken, Christopher and Lilienthal, Achim J. and Hertzberg, Joachim}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Workshop : }, institution = {Örebro University, Department of Technology}, institution = {Institute of Computer Science, University of Osnabrück, Osnabrück, Germany}, institution = {Institute of Computer Science, University of Osnabrück, Osnabrück, Germany}, institution = {Institute of Computer Science, University of Osnabrück, Osnabrück, Germany}, title = {3D mapping the Kvarntorp mine : a rield experiment for evaluation of 3D scan matching algorithms}, keywords = {Scan matching, registration, SLAM}, abstract = {This paper presents the results of a field experiment in the Kvarntorp mine outside of Örebro in Sweden. 3D mapping of the underground mine has been used to compare two scan matching methods, namely the iterative closest point algorithm (ICP) and the normal distributions transform (NDT). The experimental results of the algorithm are compared in terms of robustness and speed. For robustness we measure how reliably 3D scans are registered with respect to different starting pose estimates. Speed is evaluated running the authors’ best implementations on the same hardware. This leads to an unbiased comparison. In these experiments, NDT was shown to converge form a larger range of initial pose estimates than ICP, and to perform faster. }, year = {2008} } @article{Andreasson158115, author = {Andreasson, Henrik and Duckett, Tom and Lilienthal, Achim J.}, institution = {Örebro University, Department of Technology}, institution = {University of Lincoln, University of Lincoln, UK}, journal = {IEEE Transactions on Robotics}, number = {5}, pages = {991--1001}, title = {A Minimalistic Approach to Appearance-Based Visual SLAM}, volume = {24}, DOI = {10.1109/TRO.2008.2004642}, keywords = {Omnidirectional vision, simultaneous localization and mapping (SLAM)}, abstract = {This paper presents a vision-based approach to SLAM in indoor / outdoor environments with minimalistic sensing and computational requirements. The approach is based on a graph representation of robot poses, using a relaxation algorithm to obtain a globally consistent map. Each link corresponds to a relative measurement of the spatial relation between the two nodes it connects. The links describe the likelihood distribution of the relative pose as a Gaussian distribution. To estimate the covariance matrix for links obtained from an omni-directional vision sensor, a novel method is introduced based on the relative similarity of neighbouring images. This new method does not require determining distances to image features using multiple view geometry, for example. Combined indoor and outdoor experiments demonstrate that the approach can handle qualitatively different environments (without modification of the parameters), that it can cope with violations of the “flat floor assumption” to some degree, and that it scales well with increasing size of the environment, producing topologically correct and geometrically accurate maps at low computational cost. Further experiments demonstrate that the approach is also suitable for combining multiple overlapping maps, e.g. for solving the multi-robot SLAM problem with unknown initial poses. }, year = {2008} } @article{Persson137571, author = {Persson, Martin and Duckett, Tom and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Örebro University, Department of Natural Sciences}, institution = {Department of Computing and Informatics, University of Lincoln, Lincoln, UK}, journal = {Robotics and Autonomous Systems}, number = {6}, pages = {483--492}, title = {Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping}, volume = {56}, DOI = {10.1016/j.robot.2008.03.002}, keywords = {Semantic Mapping, Aerial Images, Mobile Robotics}, abstract = {This work investigates the use of semantic information to link ground level occupancy maps and aerial images. A ground level semantic map, which shows open ground and indicates the probability of cells being occupied by walls of buildings, is obtained by a mobile robot equipped with an omni-directional camera, GPS and a laser range finder. This semantic information is used for local and global segmentation of an aerial image. The result is a map where the semantic information has been extended beyond the range of the robot sensors and predicts where the mobile robot can find buildings and potentially driveable ground. }, year = {2008} } @inproceedings{Stachniss137562, author = {Stachniss, Cyril and Plagemann, Christian and Lilienthal, Achim J. and Burgard, Wolfram}, booktitle = {Robotics : science and systems IV}, institution = {Örebro University, School of Science and Technology}, institution = {Dept. for Computer Science, Albert-Ludwigs-University, Freiburg, Germany}, institution = {Dept. for Computer Science, Albert-Ludwigs-University, Freiburg, Germany}, institution = {Dept. for Computer Science, Albert-Ludwigs-University, Freiburg, Germany}, note = {Accepted as oral presentation (acceptance rate {\&}lt;15{\%}), selected from these papers as one of the best conference papers}, pages = {310--317}, title = {Gas distribution modeling using sparse Gaussian process mixture models}, volume = {4}, DOI = {10.15607/rss.2008.iv.040}, keywords = {Gas distribution modeling, gas sensing, Gaussian processes, mixture models}, abstract = {In this paper, we consider the problem of learning a two dimensional spatial model of a gas distribution with a mobile robot. Building maps that can be used to accurately predict the gas concentration at query locations is a challenging task due to the chaotic nature of gas dispersal. We present an approach that formulates this task as a regression problem. To deal with the specific properties of typical gas distributions, we propose a sparse Gaussian process mixture model. This allows us to accurately represent the smooth background signal as well as areas of high concentration. We integrate the sparsification of the training data into an EM procedure used for learning the mixture components and the gating function. Our approach has been implemented and tested using datasets recorded with a real mobile robot equipped with an electronic nose. We demonstrate that our models are well suited for predicting gas concentrations at new query locations and that they outperform alternative methods used in robotics to carry out in this task. }, ISBN = {9780262513098}, year = {2008} } @inproceedings{Persson137594, author = {Persson, Martin and Duckett, Tom and Lilienthal, Achim J.}, booktitle = {Recent Progress in Robotics : Viable Robotic Service to Human}, institution = {Örebro University, School of Science and Technology}, institution = {Department of Computing and Informatics, University of Lincoln, Lincoln, UK}, pages = {157--169}, title = {Improved mapping and image segmentation by using semantic information to link aerial images and ground-level information}, series = {Lecture Notes in Control and Information Sciences}, number = {370}, DOI = {10.1007/978-3-540-76729-9_13}, keywords = {Semantic Mapping, Aerial Images, Mobile Robotics}, abstract = {This paper investigates the use of semantic information to link ground-level occupancy maps and aerial images. A ground-level semantic map is obtained by a mobile robot equipped with an omnidirectional camera, differential GPS and a laser range finder. The mobile robot uses a virtual sensor for building detection (based on omnidirectional images) to compute the ground-level semantic map, which indicates the probability of the cells being occupied by the wall of a building. These wall estimates from a ground perspective are then matched with edges detected in an aerial image. The result is used to direct a region- and boundary-based segmentation algorithm for building detection in the aerial image. This approach addresses two difficulties simultaneously: 1) the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in monocular aerial images. With the suggested method building outlines can be detected faster than the mobile robot can explore the area by itself, giving the robot an ability to “see” around corners. At the same time, the approach can compensate for the absence of elevation data in segmentation of aerial images. Our experiments demonstrate that ground-level semantic information (wall estimates) allows to focus the segmentation of the aerial image to find buildings and produce a ground-level semantic map that covers a larger area than can be built using the onboard sensors. }, ISBN = {978-3-540-76728-2}, year = {2008} } @inproceedings{Valgren137555, author = {Valgren, Christoffer and Lilienthal, Achim J.}, booktitle = {2008 IEEE international conference on robotics and automation : }, institution = {Örebro University, Department of Technology}, institution = {Örebro University, Örebro, Sweden}, pages = {1856--1861}, eid = {4543477}, title = {Incremental spectral clustering and seasons : appearance-based localization in outdoor environments}, series = {IEEE International Conference on Robotics and Automation}, DOI = {10.1109/ROBOT.2008.4543477}, keywords = {Apperance based localisation, topological mapping, spectral clustering}, abstract = {The problem of appearance-based mapping and navigation in outdoor environments is far from trivial. In this paper, an appearance-based topological map, covering a large, mixed indoor and outdoor environment, is built incrementally by using panoramic images. The map is based on image similarity, so that the resulting segmentation of the world corresponds closely to the human concept of a place. Using high-resolution images and the epipolar constraint, the resulting map is shown to be very suitable for localization, even when the environment has undergone seasonal changes. }, ISBN = {978-1-4244-1646-2}, year = {2008} } @inproceedings{Huhle139019, author = {Huhle, Benjamin and Magnusson, Martin and Straßer, Wolfgang and Lilienthal, Achim J.}, booktitle = {2008 IEEE international conference on robotics and automation : }, institution = {Örebro University, Department of Technology}, institution = {Department of Graphical Interactive Systems WSI/GRIS, University of Tübingen, Germany}, institution = {Department of Graphical Interactive Systems WSI/GRIS, University of Tübingen, Germany}, pages = {4025--4030}, eid = {4543829}, title = {Registration of colored 3D point clouds with a Kernel-based extension to the normal distributions transform}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ROBOT.2008.4543829}, abstract = {We present a new algorithm for scan registration of colored 3D point data which is an extension to the Normal Distributions Transform (NDT). The probabilistic approach of NDT is extended to a color-aware registration algorithm by modeling the point distributions as Gaussian mixture-models in color space. We discuss different point cloud registration techniques, as well as alternative variants of the proposed algorithm. Results showing improved robustness of the proposed method using real-world data acquired with a mobile robot and a time-of-flight camera are presented. }, ISBN = {978-1-4244-1646-2}, year = {2008} } @inproceedings{Trincavelli138918, author = {Trincavelli, Marco and Reggente, Matteo and Coradeschi, Silvia and Loutfi, Amy and Ishida, Hiroshi and Lilienthal, Achim J.}, booktitle = {2008 IEEE/RSJ International Conference on Intelligent Robots and Systems : }, institution = {Örebro University, Department of Technology}, institution = {Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan}, note = {Funding Agency:Japan Society for the Promotion of Science}, pages = {2210--2215}, eid = {4650755}, title = {Towards environmental monitoring with mobile robots}, DOI = {10.1109/IROS.2008.4650755}, keywords = {Mobile, robot, olfaction}, abstract = {In this paper we present initial experiments towards environmental monitoring with a mobile platform. A prototype of a pollution monitoring robot was set up which measures the gas distribution using an “electronic nose” and provides three dimensional wind measurements using an ultrasonic anemometer. We describe the design of the robot and the experimental setup used to run trials under varying environmental conditions. We then present the results of the gas distribution mapping. The trials which were carried out in three uncontrolled environments with very different properties: an enclosed indoor area, a part of a long corridor with open ends and a high ceiling, and an outdoor scenario are presented and discussed. }, ISBN = {978-1-4244-2057-5}, year = {2008} } @inproceedings{Lilienthal138564, author = {Lilienthal, Achim J. and Loutfi, Amy and Blanco, Jose Luis and Galindo, Cipriano and Gonzalez, Javier}, booktitle = {ECMR 2007 : Proceedings of the European Conference on Mobile Robots}, institution = {Örebro University, Department of Technology}, institution = {Dept. of System Engineering and Automation, University of Malaga, Malaga, Spain}, institution = {Dept. of System Engineering and Automation, University of Malaga, Malaga, Spain}, institution = {Dept. of System Engineering and Automation, University of Malaga, Malaga, Spain}, pages = {126--131}, title = {A Rao-Blackwellisation approach to GDM-SLAM : integrating SLAM and gas distribution mapping (GDM)}, abstract = {In this paper we consider the problem of creating a two dimensional spatial representation of gas distribution with a mobile robot. In contrast to previous approaches to the problem of gas distribution mapping (GDM) we do not assume that the robot has perfect knowledge about its position. Instead we develop a probabilistic framework for simultaneous localisation and occupancy and gas distribution mapping (GDM/SLAM) that allows to account for the uncertainty about the robot’s position when computing the gas distribution map. Considering the peculiarities of gas sensing in real-world environments, we show which dependencies in the posterior over occupancy and gas distribution maps can be neglected under certain practical assumptions. We develop a Rao-Blackwellised particle filter formulation of the GDM/SLAM problem that allows to plug in any algorithm to compute a gas distribution map from a sequence of gas sensor measurements and a known trajectory. In this paper we use the Kernel Based Gas Distribution Mapping (Kernel- GDM) method. As a first step towards outdoor gas distribution mapping we present results obtained in a large, uncontrolled, partly open indoor environment. }, URL = {http://ecmr07.informatik.uni-freiburg.de/proceedings/ECMR07_0060.pdf}, year = {2007} } @inproceedings{Persson138561, author = {Persson, Martin and Duckett, Tom and Lilienthal, Achim J.}, booktitle = {Proceedings of the IROS Workshop "From Sensors to Human Spatial Concepts" : }, institution = {Örebro University, Department of Technology}, institution = {Department of Computing and Informatics, University of Lincoln, Lincoln, Uk}, pages = {17--24}, title = {Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping}, abstract = {This paper investigates the use of semantic information to link ground-level occupancy maps and aerial images. A ground-level semantic map is obtained by a mobile robot equipped with an omnidirectional camera, differential GPS and a laser range finder. The mobile robot uses a virtual sensor for building detection (based on omnidirectional images) to compute the ground-level semantic map, which indicates the probability of the cells being occupied by the wall of a building. These wall estimates from a ground perspective are then matched with edges detected in an aerial image. The result is used to direct a region- and boundary-based segmentation algorithm for building detection in the aerial image. This approach addresses two difficulties simultaneously: 1) the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in monocular aerial images. With the suggested method building outlines can be detected faster than the mobile robot can explore the area by itself, giving the robot an ability to "see" around corners. At the same time, the approach can compensate for the absence of elevation data in segmentation of aerial images. Our experiments demonstrate that ground-level semantic information (wall estimates) allows to focus the segmentation of the aerial image to find buildings and produce a ground-level semantic map that covers a larger area than can be built using the onboard sensors. }, year = {2007} } @inproceedings{Andreasson138559, author = {Andreasson, Henrik and Magnusson, Martin and Lilienthal, Achim}, booktitle = {2007 IEEE/RSJ international conference on intelligent robots and systems : }, institution = {Örebro University, Department of Technology}, institution = {Örebro University, Department of Natural Sciences}, pages = {3429--3435}, eid = {4399381}, title = {Has something changed here? : Autonomous difference detection for security patrol robots}, DOI = {10.1109/IROS.2007.4399381}, abstract = {This paper presents a system for autonomous change detection with a security patrol robot. In an initial step a reference model of the environment is created and changes are then detected with respect to the reference model as differences in coloured 3D point clouds, which are obtained from a 3D laser range scanner and a CCD camera. The suggested approach introduces several novel aspects, including a registration method that utilizes local visual features to determine point correspondences (thus essentially working without an initial pose estimate) and the 3D-NDT representation with adaptive cell size to efficiently represent both the spatial and colour aspects of the reference model. Apart from a detailed description of the individual parts of the difference detection system, a qualitative experimental evaluation in an indoor lab environment is presented, which demonstrates that the suggested system is able register and detect changes in spatial 3D data and also to detect changes that occur in colour space and are not observable using range values only. }, ISBN = {978-1-4244-0912-9}, year = {2007} } @inproceedings{Cielniak137568, author = {Cielniak, Grzegorz and Duckett, Tom and Lilienthal, Achim J.}, booktitle = {2007 IEEE/RSJ international conference on intelligent robots and systems : }, institution = {Örebro University, Department of Technology}, institution = {Department of Computing and Informatics, University of Lincoln, Lincoln, United Kingdom}, institution = {Department of Computing and Informatics, University of Lincoln, Lincoln, United Kingdom}, pages = {3436--3441}, title = {Improved data association and occlusion handling for vision-based people tracking by mobile robots}, DOI = {10.1109/IROS.2007.4399507}, keywords = {Person tracking, robot vision, occlusion handling}, abstract = {This paper presents an approach for tracking multiple persons using a combination of colour and thermal vision sensors on a mobile robot. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is then incorporated into the tracker. }, ISBN = {978-1-4244-0912-9}, year = {2007} } @inproceedings{Persson138566, author = {Persson, Martin and Duckett, Tom and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE international conference on advanced robotics : ICAR 2007}, institution = {Örebro University, Department of Technology}, institution = {Department of Computing and Informatics, University of Lincoln, Lincoln, United Kingdom}, pages = {924--929}, title = {Improved mapping and image segmentation by using semantic information to link aerial images and ground-level information}, abstract = {This paper investigates the use of semantic information to link ground-level occupancy maps and aerial images. In the suggested approach a ground-level semantic map is obtained by a mobile robot equipped with an omnidirectional camera, differential GPS and a laser range finder. The mobile robot uses a virtual sensor for building detection (based on omnidirectional images) to compute the ground-level semantic map, which indicates the probability of the cells being occupied by the wall of a building. These wall estimates from a ground perspective are then matched with edges detected in an aerial image. The result is used to direct a region- and boundary-based segmentation algorithm for building detection in the aerial image. This approach addresses two difficulties simultaneously: 1) the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in monocular aerial images. With the suggested method building outlines can be detected faster than the mobile robot can explore the area by itself, giving the robot an ability to "see" around corners. At the same time, the approach can compensate for the absence of elevation data in segmentation of aerial images. Our experiments demonstrate that ground-level semantic information (wall estimates) allows to focus the segmentation of the aerial image to find buildings and produce a groundlevel semantic map that covers a larger area than can be built using the onboard sensors along the robot trajectory. }, year = {2007} } @inproceedings{Valgren139071, author = {Valgren, Christoffer and Duckett, Tom and Lilienthal, Achim J.}, booktitle = {2007 IEEE international conference on robotics and automation (ICRA) : }, institution = {Örebro University, Department of Technology}, institution = {University of Lincoln, United Kingdom}, note = {Funding Agency:The Swedish Defence Material Administration}, pages = {4283--4288}, title = {Incremental spectral clustering and its application to topological mapping}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ROBOT.2007.364138}, abstract = {This paper presents a novel use of spectral clustering algorithms to support cases where the entries in the affinity matrix are costly to compute. The method is incremental – the spectral clustering algorithm is applied to the affinity matrix after each row/column is added – which makes it possible to inspect the clusters as new data points are added. The method is well suited to the problem of appearance-based, on-line topological mapping for mobile robots. In this problem domain, we show that we can reduce environment-dependent parameters of the clustering algorithm to just a single, intuitive parameter. Experimental results in large outdoor and indoor environments show that we can close loops correctly by computing only a fraction of the entries in the affinity matrix. The accompanying video clip shows how an example map is produced by the algorithm. }, ISBN = {978-1-4244-0601-2}, year = {2007} } @inproceedings{Lilienthal138318, author = {Lilienthal, Achim J. and Loutfi, Amy and Blanco, Jose Luis and Galindo, Cipriano and Gonzalez, Javier}, booktitle = {Proceedings of ICRA Workshop on Robotic Olfaction : Towards Real Applications. ICRA 2007}, institution = {Örebro University, Department of Technology}, institution = {System Engineering and Automation Department, University of Malaga, Malaga, Spain}, institution = {System Engineering and Automation Department, University of Malaga, Malaga, Spain}, institution = {System Engineering and Automation Department, University of Malaga, Malaga, Spain}, pages = {21--28}, title = {Integrating SLAM into gas distribution mapping}, abstract = {In this paper we consider the problem of creating a spatial representation of a gas distribution in an environment using a mobile robot equipped with gas sensors. The gas distribution mapping method used models the information content of a given measurement about the average concentration distribution with respect to the point of measurement. In this paper, we present an extension which can consider the uncertainty about the robot’s position in the gas distribution mapping. We present a preliminary result where a mobile robot equipped with gas sensors creates a map of a large indoor environment, using both spatial and olfactory information. }, year = {2007} } @inproceedings{Andreasson138560, author = {Andreasson, Henrik and Duckett, Tom and Lilienthal, Achim J.}, booktitle = {2007 IEEE international conference on robotics and automation (ICRA) : }, institution = {Örebro University, Department of Technology}, institution = {Dept. of Computing & Informatics, University of Lincoln, Lincoln, United Kingdom}, pages = {4096--4101}, eid = {4209726}, title = {Mini-SLAM : minimalistic visual SLAM in large-scale environments based on a new interpretation of image similarity}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ROBOT.2007.364108}, abstract = {This paper presents a vision-based approach to SLAM in large-scale environments with minimal sensing and computational requirements. The approach is based on a graphical representation of robot poses and links between the poses. Links between the robot poses are established based on odometry and image similarity, then a relaxation algorithm is used to generate a globally consistent map. To estimate the covariance matrix for links obtained from the vision sensor, a novel method is introduced based on the relative similarity of neighbouring images, without requiring distances to image features or multiple view geometry. Indoor and outdoor experiments demonstrate that the approach scales well to large-scale environments, producing topologically correct and geometrically accurate maps at minimal computational cost. Mini-SLAM was found to produce consistent maps in an unstructured, large-scale environment (the total path length was 1.4 km) containing indoor and outdoor passages. }, ISBN = {978-1-4244-0601-2}, year = {2007} } @inproceedings{Andreasson138558, author = {Andreasson, Henrik and Triebel, Rudolph and Lilienthal, Achim J.}, booktitle = {Autonomos Agents and Robots : }, institution = {Örebro University, Department of Technology}, institution = {Department of Computer Science, University of Freiburg, Freiburg, Germany}, pages = {83--90}, eid = {4399381}, publisher = {Springer}, title = {Non-iterative Vision-based Interpolation of 3D Laser Scans}, series = {Studies in Computational Intelligence}, number = {76}, volume = {76}, DOI = {10.1007/978-3-540-73424-6_10}, keywords = {3D range sensor, laser range scanner, vision-based depth interpolation, 3D vision}, abstract = {3D range sensors, particularly 3D laser range scanners, enjoy a rising popularity and are used nowadays for many different applications. The resolution 3D range sensors provide in the image plane is typically much lower than the resolution of a modern colour camera. In this chapter we focus on methods to derive a highresolution depth image from a low-resolution 3D range sensor and a colour image. The main idea is to use colour similarity as an indication of depth similarity, based on the observation that depth discontinuities in the scene often correspond to colour or brightness changes in the camera image. We present five interpolation methods and compare them with an independently proposed method based on Markov random fields. The proposed algorithms are non-iterative and include a parameter-free vision-based interpolation method. In contrast to previous work, we present ground truth evaluation with real world data and analyse both indoor and outdoor data. }, ISBN = {978-3-540-73423-9}, year = {2007} } @inproceedings{Persson138567, author = {Persson, Martin and Duckett, Tom and Valgren, Christoffer and Lilienthal, Achim J.}, booktitle = {Proceedings of the 2007 IEEE International symposium on computational intelligence in robotics and automation, CIRA 2007 : }, institution = {Örebro University, Department of Technology}, institution = {Department of Computing and Informatics, University of Lincoln, Lincoln, United Kingdom}, institution = {Department of Technology, Örebro University, Örebro, Sweden}, note = {Funding Agency:Swedish Defence Material Administration}, pages = {236--242}, eid = {4269870}, title = {Probabilistic semantic mapping with a virtual sensor for building/nature detection}, DOI = {10.1109/CIRA.2007.382870}, abstract = {In human-robot communication it is often important to relate robot sensor readings to concepts used by humans. We believe that access to semantic maps will make it possible for robots to better communicate information to a human operator and vice versa. The main contribution of this paper is a method that fuses data from different sensor modalities, range sensors and vision sensors are considered, to create a probabilistic semantic map of an outdoor environment. The method combines a learned virtual sensor (understood as one or several physical sensors with a dedicated signal processing unit for recognition of real world concepts) for building detection with a standard occupancy map. The virtual sensor is applied on a mobile robot, combining classifications of sub-images from a panoramic view with spatial information (location and orientation of the robot) giving the likely locations of buildings. This information is combined with an occupancy map to calculate a probabilistic semantic map. Our experiments with an outdoor mobile robot show that the method produces semantic maps with correct labeling and an evident distinction between "building" objects from "nature" objects }, ISBN = {978-1-4244-0789-7}, year = {2007} } @article{Magnusson138557, author = {Magnusson, Martin and Lilienthal, Achim J. and Duckett, Tom}, institution = {Örebro University, Department of Technology}, institution = {Department of Computing and Informatics, University of Lincoln, Lincoln, United Kingdom}, journal = {Journal of Field Robotics}, note = {Special issue on mining robotics.}, number = {10}, pages = {803--827}, title = {Scan registration for autonomous mining vehicles using 3D-NDT}, volume = {24}, DOI = {10.1002/rob.20204}, abstract = {Scan registration is an essential sub-task when building maps based on range finder data from mobile robots. The problem is to deduce how the robot has moved between consecutive scans, based on the shape of overlapping portions of the scans. This paper presents a new algorithm for registration of 3D data. The algorithm is a generalisation and improvement of the normal distributions transform (NDT) for 2D data developed by Biber and Straßer, which allows for accurate registration using a memory-efficient representation of the scan surface. A detailed quantitative and qualitative comparison of the new algorithm with the 3D version of the popular ICP (iterative closest point) algorithm is presented. Results with actual mine data, some of which were collected with a new prototype 3D laser scanner, show that the presented algorithm is faster and slightly more reliable than the standard ICP algorithm for 3D registration, while using a more memory-efficient scan surface representation. }, year = {2007} } @inproceedings{Valgren138562, author = {Valgren, Christoffer and Lilienthal, Achim J.}, booktitle = {ECMR 2007 : Proceedings of the European Conference on Mobile Robots}, institution = {Örebro University, Department of Technology}, institution = {Örebro University, Örebro, Sweden}, pages = {253--258}, title = {SIFT, SURF and seasons : long-term outdoor localization using local features}, abstract = {Local feature matching has become a commonly used method to compare images. For mobile robots, a reliable method for comparing images can constitute a key component for localization and loop closing tasks. In this paper, we address the issues of outdoor appearance-based topological localization for a mobile robot over time. Our data sets, each consisting of a large number of panoramic images, have been acquired over a period of nine months with large seasonal changes (snowcovered ground, bare trees, autumn leaves, dense foliage, etc.). Two different types of image feature algorithms, SIFT and the more recent SURF, have been used to compare the images. We show that two variants of SURF, called U-SURF and SURF-128, outperform the other algorithms in terms of accuracy and speed. }, URL = {http://ecmr07.informatik.uni-freiburg.de/proceedings/ECMR07_0050.pdf}, year = {2007} } @article{Persson447970, author = {Persson, Martin and Duckett, Tom and Lilienthal, Achim J.}, institution = {Örebro University, Department of Technology}, institution = {Department of Computing and Informatics, University of Lincoln, Lincoln, UK}, journal = {Robotics and Autonomous Systems}, number = {5}, pages = {383--390}, title = {Virtual sensors for human concepts : building detection by an outdoor mobile robot}, volume = {55}, DOI = {10.1016/j.robot.2006.12.002}, abstract = {In human-robot communication it is often important to relate robot sensor readings to concepts used by humans. We suggest the use of a virtual sensor (one or several physical sensors with a dedicated signal processing unit for the recognition of real world concepts) and a method with which the virtual sensor can learn from a set of generic features. The virtual sensor robustly establishes the link between sensor data and a particular human concept. In this work, we present a virtual sensor for building detection that uses vision and machine learning to classify the image content in a particular direction as representing buildings or non-buildings. The virtual sensor is trained on a diverse set of image data, using features extracted from grey level images. The features are based on edge orientation, the configurations of these edges, and on grey level clustering. To combine these features, the AdaBoost algorithm is applied. Our experiments with an outdoor mobile robot show that the method is able to separate buildings from nature with a high classification rate, and to extrapolate well to images collected under different conditions. Finally, the virtual sensor is applied on the mobile robot, combining its classifications of sub-images from a panoramic view with spatial information (in the form of location and orientation of the robot) in order to communicate the likely locations of buildings to a remote human operator. (c) 2006 Elsevier B.V. All rights reserved. }, year = {2007} } @inproceedings{Andreasson138563, author = {Andreasson, Henrik and Lilienthal, Achim}, booktitle = {ECMR 2007 : Proceedings of the European Conference on Mobile Robots}, institution = {Örebro University, Department of Technology}, institution = {Örebro University, Department of Natural Sciences}, institution = {aass}, pages = {192--197}, title = {Vision aided 3D laser scanner based registration}, keywords = {Registration, Vision}, abstract = {This paper describes a vision and 3D laser based registration approach which utilizes visual features to identify correspondences. Visual features are obtained from the images of a standard color camera and the depth of these features is determined by interpolating between the scanning points of a 3D laser range scanner, taking into consideration the visual information in the neighbourhood of the respective visual feature. The 3D laser scanner is also used to determine a position covariance estimate of the visual feature. To exploit these covariance estimates, an ICP algorithm based on the Mahalanobis distance is applied. Initial experimental results are presented in a real world indoor laboratory environment }, URL = {http://ecmr07.informatik.uni-freiburg.de/proceedings/ECMR07_0059.pdf}, year = {2007} } @article{Lilienthal137695, author = {Lilienthal, Achim J. and Loutfi, Amy and Duckett, Tom}, institution = {Örebro University, Department of Technology}, institution = {Dept. of Computing and Informatics, UKUniversity of Lincoln, Lincoln}, journal = {Sensors}, number = {11}, pages = {1616--1678}, publisher = {M D P I AG}, title = {Airborne chemical sensing with mobile robots}, volume = {6}, DOI = {10.3390/s6111616}, keywords = {Mobile robot olfaction, gas distribution mapping, trail guidance, gas source localisation, gas source tracing, gas source declaration}, abstract = {Airborne chemical sensing with mobile robots has been an active research area since the beginning of the 1990s. This article presents a review of research work in this field, including gas distribution mapping, trail guidance, and the different subtasks of gas source localisation. Due to the difficulty of modelling gas distribution in a real world environment with currently available simulation techniques, we focus largely on experimental work and do not consider publications that are purely based on simulations. }, year = {2006} } @inproceedings{Valgren138255, author = {Valgren, Christoffer and Lilienthal, Achim J. and Duckett, Tom}, booktitle = {2006 IEEE/RSJ international conference on intelligent robots and systems : }, institution = {Örebro University, Department of Technology}, institution = {Örebro University, Örebro, Sweden}, institution = {Department of Computing and Informatics, University of Lincoln, Brayford Pool, Lincoln, United Kingdom}, pages = {3441--3447}, eid = {4058933}, title = {Incremental topological mapping using omnidirectional vision}, DOI = {10.1109/IROS.2006.282583}, abstract = {This paper presents an algorithm that builds topological maps, using omnidirectional vision as the only sensor modality. Local features are extracted from images obtained in sequence, and are used both to cluster the images into nodes and to detect links between the nodes. The algorithm is incremental, reducing the computational requirements of the corresponding batch algorithm. Experimental results in a complex, indoor environment show that the algorithm produces topologically correct maps, closing loops without suffering from perceptual aliasing or false links. Robustness to lighting variations was further demonstrated by building correct maps from combined multiple datasets collected over a period of 2 months. }, ISBN = {978-1-4244-0258-8}, year = {2006} } @inproceedings{Lilienthal138258, author = {Lilienthal, Achim J. and Duckett, Tom and Ishida, Hiroshi and Werner, Felix}, booktitle = {The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006, BioRob 2006 : }, institution = {Örebro University, Department of Technology}, institution = {Tokyo Univ. of Agriculture and Technology, Dept. of Mechanical Systems Engineering, Tokyo, Japan}, institution = {Wilhelm-Schickard Institute, University of Tübingen, Tübingen, Germany}, pages = {733--738}, eid = {1639177}, title = {Indicators of gas source proximity using metal oxide sensors in a turbulent environment}, series = {Proceedings of the IEEE RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics}, DOI = {10.1109/BIOROB.2006.1639177}, keywords = {Mobile nose, gas source localisation, turbulent gas distribution}, abstract = {This paper addresses the problem of estimating proximity to a gas source using concentration measurements. In particular, we consider the problem of gas source declaration by a mobile robot equipped with metal oxide sensors in a turbulent indoor environment. While previous work has shown that machine learning classifiers can be trained to detect close proximity to a gas source, it is difficult to interpret the learned models. This paper investigates possible underlying indicators of gas source proximity, comparing three different statistics derived from the sensor measurements of the robot. A correlation analysis of 1056 trials showed that response variance (measured as standard deviation) was a better indicator than average response. An improved result was obtained when the standard deviation was normalized to the average response for each trial, a strategy that also reduces calibration problems. }, ISBN = {978-1-4244-0039-3}, year = {2006} } @inproceedings{Jun138256, author = {Jun, Li and Lilienthal, Achim J. and Martìnez-Marìn, Tomas and Duckett, Tom}, booktitle = {2006 IEEE/RSJ international conference on intelligent robots and systems : }, institution = {Örebro University, Department of Technology}, institution = {Department of Physics, System Engineering and Signal Theory, University of Alicante, Alicante, Spain}, pages = {2656--2662}, eid = {4058792}, title = {Q-RAN : a constructive reinforcement learning approach for robot behavior learning}, DOI = {10.1109/IROS.2006.281986}, abstract = {This paper presents a learning system that uses Q-learning with a resource allocating network (RAN) for behavior learning in mobile robotics. The RAN is used as a function approximator, and Q-learning is used to learn the control policy in `off-policy' fashion that enables learning to be bootstrapped by a prior knowledge controller, thus speeding up the reinforcement learning. Our approach is verified on a PeopleBot robot executing a visual servoing based docking behavior in which the robot is required to reach a goal pose. Further experiments show that the RAN network can also be used for supervised learning prior to reinforcement learning in a layered architecture, thus further improving the performance of the docking behavior. }, ISBN = {978-1-4244-0258-8}, year = {2006} } @inproceedings{Skoglund138390, author = {Skoglund, Alexander and Duckett, Tom and Iliev, Boyko and Lilienthal, Achim J. and Palm, Rainer}, booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) ,2006 : }, institution = {Örebro University, Department of Technology}, pages = {4339--4341}, title = {Teaching by demonstration of robotic manipulators in non-stationary environments}, abstract = {In this paper we propose a system consisting of a manipulator equipped with range sensors, that is instructed to follow a trajectory demonstrated by a human teacher wearing a motion capturing device. During the demonstration a three dimensional occupancy grid of the environment is built using the range sensor information and the trajectory. The demonstration is followed by an exploration phase, where the robot undergoes self-improvement of the task, during which the occupancy grid is used to avoid collisions. In parallel a reinforcement learning (RL) agent, biased by the demonstration, learns a point-to-point task policy. When changes occur in the workspace, both the occupancy grid and the learned policy will be updated online by the system. }, year = {2006} } @inproceedings{Persson138257, author = {Persson, Martin and Duckett, Tom and Lilienthal, Achim J.}, booktitle = {Proceedings of the IROS 2006 workshop : From Sensors toHuman Spatial Concepts}, institution = {Örebro University, Department of Technology}, institution = {Department of Computing and Informatics, University of Lincoln, Lincoln, UK}, pages = {21--26}, title = {Virtual sensors for human concepts : building detection by an outdoor mobile robot}, keywords = {Human–robot communication, Human concepts, Virtual sensor, Automatic building detection, AdaBoost}, abstract = {In human–robot communication it is often important to relate robot sensor readings to concepts used by humans. We suggest the use of a virtual sensor (one or several physical sensors with a dedicated signal processing unit for the recognition of real world concepts) and a method with which the virtual sensor can learn from a set of generic features. The virtual sensor robustly establishes the link between sensor data and a particular human concept. In this work, we present a virtual sensor for building detection that uses vision and machine learning to classify the image content in a particular direction as representing buildings or non-buildings. The virtual sensor is trained on a diverse set of image data, using features extracted from grey level images. The features are based on edge orientation, the configurations of these edges, and on grey level clustering. To combine these features, the AdaBoost algorithm is applied. Our experiments with an outdoor mobile robot show that the method is able to separate buildings from nature with a high classification rate, and to extrapolate well to images collected under different conditions. Finally, the virtual sensor is applied on the mobile robot, combining its classifications of sub-images from a panoramic view with spatial information (in the form of location and orientation of the robot) in order to communicate the likely locations of buildings to a remote human operator. }, year = {2006} } @inproceedings{Andreasson138254, author = {Andreasson, Henrik and Lilienthal, Achim J. and Triebel, Rudolph}, booktitle = {Proceedings of the Third International Conference on Autonomous Robots and Agents : }, institution = {Örebro University, Department of Technology}, institution = {Department of Computer Science, University of Freiburg, Germany}, pages = {455--460}, title = {Vision based interpolation of 3D laser scans}, keywords = {3D range sensor, laser range scanner, vision-based depth interpolation, 3D vision}, abstract = {3D range sensors, particularly 3D laser range scanners, enjoy a rising popularity and are used nowadays for many different applications. The resolution 3D range sensors provide in the image plane is typically much lower than the resolution of a modern color camera. In this paper we focus on methods to derive a high-resolution depth image from a low-resolution 3D range sensor and a color image. The main idea is to use color similarity as an indication of depth similarity, based on the observation that depth discontinuities in the scene often correspond to color or brightness changes in the camera image. We present five interpolation methods and compare them with an independently proposed method based on Markov Random Fields. The algorithms proposed in this paper are non-iterative and include a parameter-free vision-based interpolation method. In contrast to previous work, we present ground truth evaluation with real world data and analyse both indoor and outdoor data. Further, we suggest and evaluate four methods to determine a confidence measure for the accuracy of interpolated range values. }, year = {2006} } @inproceedings{Lilienthal138296, author = {Lilienthal, Achim J. and Streichert, Felix and Zell, Andreas}, booktitle = {Proceedings of the 2005 IEEE International Conference on Robotics and Automation : }, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, pages = {3564--3569}, eid = {1570662}, title = {Model-based shape analysis of gas concentration grinmaps for improved gas source localisation}, DOI = {10.1109/ROBOT.2005.1570662}, keywords = {Gas concentration mapping, gas source localisation}, abstract = {This work addresses the capability to use concentration gridmaps to locate a static gas source. In previous works it was found that depending on the shape of the mapped gas distribution (corresponding to different airflow conditions) the gas source location can be sometimes approximated with high accuracy by the maximum in the concentration map while this is not possible in other cases. This paper introduces a method to distinguish both cases by analysing the shape of the obtained concentration map in terms of a model of the time-averaged gas distribution known from physics. The parameters of the model that approximates the concentration map most closely are determined by nonlinear least squares fitting using evolution strategies (ES). The best fit also provides a better estimate of the gas source position in situations where the concentration maximum estimate fails. Different methods to select the most truthful estimate are introduced in this work and a comparison regarding their accuracy is presented, based on a total of 34h of concentration mapping experiments. }, ISBN = {0-7803-8914-X}, year = {2005} } @article{Lilienthal137827, author = {Lilienthal, Achim J. and Duckett, Tom}, institution = {Örebro University, Department of Technology}, institution = {University of Tübingen, WSI, Tübingen, Germany}, journal = {Robotics and Autonomous Systems}, number = {1}, pages = {3--16}, title = {Building gas concentration gridmaps with a mobile robot}, volume = {48}, DOI = {10.1016/j.robot.2004.05.002}, keywords = {Mobile nose, Gas distribution mapping, Gas source localisation}, abstract = {This paper addresses the problem of mapping the structure of a gas distribution by creating concentration gridmaps from the data collected by a mobile robot equipped with gas sensors. By contrast to metric gridmaps extracted from sonar or laser range scans, a single measurement from a gas sensor provides information about a comparatively small area. To overcome this problem, a mapping technique is introduced that uses a Gaussian weighting function to model the decreasing likelihood that a particular reading represents the true concentration with respect to the distance from the point of measurement. This method is evaluated in terms of its suitability regarding the slow response and recovery of the gas sensors, and experimental comparisons of different exploration strategies are presented. The stability of the mapped structures and the capability to use concentration gridmaps to locate a gas source are also discussed. }, year = {2004} } @article{Lilienthal137816, author = {Lilienthal, Achim J. and Duckett, Tom}, institution = {Örebro University, Department of Technology}, institution = {W.-Schickard-Inst. for Comp. Science, University of Tübingen, Tübingen, Germany}, journal = {Advanced Robotics}, number = {8}, pages = {817--834}, title = {Experimental analysis of gas-sensitive Braitenberg vehicles}, volume = {18}, DOI = {10.1163/1568553041738103}, keywords = {Gas-sensitive mobile robot, gas source localization, turbulent gas distribution, Braitenberg vehicle}, abstract = {This article addresses the problem of localising a static gas source in an indoor environment by a mobile robot. In contrast to previous works, the environment is not artificially ventilated to produce a strong unidirectional airflow. Here, the dominant transport mechanisms of gas molecules are turbulence and convection flow rather than diffusion, which results in a patchy, chaotically fluctuating gas distribution. Two Braitenberg-type strategies (positive and negative tropotaxis) based on the instantaneously measured spatial concentration gradient were investigated. Both strategies were shown to be of potential use for gas source localisation. As a possible solution to the problem of gas source declaration (the task of determining with certainty that the gas source has been found), an indirect localisation strategy based on exploration and concentration peak avoidance is suggested. Here, a gas source is located by exploiting the fact that local concentration maxima occur more frequently near the gas source compared to distant regions }, year = {2004} } @inproceedings{Lilienthal138299, author = {Lilienthal, Achim J. and Ulmer, Holger and Fr{\"o}hlich, Holger and St{\"u}tzle, Andreas and Werner, Felix and Zell, Andreas}, booktitle = {2004 IEEE International Conference on Robotics and Automation : }, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, pages = {1430--1435}, title = {Gas source declaration with a mobile robot}, DOI = {10.1109/ROBOT.2004.1308025}, abstract = {As a sub-task of the general gas source localisation problem, gas source declaration is the process of determining the certainty that a source is in the immediate vicinity. Due to the turbulent character of gas transport in a natural indoor environment, it is not sufficient to search for instantaneous concentration maxima, in order to solve this task. Therefore, this paper introduces a method to classify whether an object is a gas source or not from a series of concentration measurements, recorded while the robot performs a rotation manoeuvre in front of a possible source. For three different gas source positions, a total of 288 declaration experiments were carried out at different robot-to-source distances. Based on these readings, two machine learning techniques (ANN, SVM) were evaluated in terms of their classification performance. With learning parameters that were optimised by grid search, a maximal hit rate of approximately 87.5% could be obtained using a support vector machine }, ISBN = {0-7803-8232-3}, year = {2004} } @inproceedings{Lilienthal138298, author = {Lilienthal, Achim J. and Ulmer, Holger and Fr{\"o}hlich, Holger and Werner, Felix and Zell, Andreas}, booktitle = {2004 IEEE/RSJ international conference on intelligent robots and systems, 2004 (IROS 2004) : }, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, pages = {1444--1449}, title = {Learning to detect proximity to a gas source with a mobile robot}, volume = {4}, DOI = {10.1109/IROS.2004.1389599}, abstract = {As a sub-task of the general gas source localisation problem, gas source declaration is the process of determining the certainty that a source is in the immediate vicinity. Due to the turbulent character of gas transport in a natural indoor environment, it is not sufficient to search for instantaneous concentration maxima, in order to solve this task. Therefore, this paper introduces a method to classify whether an object is a gas source from a series of concentration measurements, recorded while the robot performs a rotation manoeuvre in front of a possible source. For three different gas source positions, a total of 1056 declaration experiments were carried out at different robot-to-source distances. Based on these readings, support vector machines (SVM) with optimised learning parameters were trained and the cross-validation classification performance was evaluated. The results demonstrate the feasibility of the approach to detect proximity to a gas source using only gas sensors. The paper presents also an analysis of the classification rate depending on the desired declaration accuracy, and a comparison with the classification rate that can be achieved by selecting an optimal threshold value regarding the mean sensor signal. }, ISBN = {0-7803-8463-6}, year = {2004} } @inproceedings{Lilienthal138319, author = {Lilienthal, Achim J. and Duckett, Tom}, booktitle = {ROSE 2003 - 1st IEEE International Workshop on Robotic Sensing 2003 : }, institution = {Örebro University, Department of Technology}, institution = {University of Tübingen, Tübingen, Germany}, eid = {1218709}, title = {A stereo electronic nose for a mobile inspection robot}, DOI = {10.1109/ROSE.2003.1218709}, abstract = {This paper describes the design of a gas-sensitive system that is suitable for use on a mobile robot ("mobile nose"). The stereo architecture comprises two equivalent sets of gas sensors mounted inside separated ventilated tubes (or "nostrils"). To characterise the dynamic response, the whole system is modelled as a first-order sensor. The corresponding parameters, including the response and recovery time, can be obtained by fitting this model to the values recorded during a simple experiment described in this paper. Our experiments confirmed the suitability of the applied model and permitted a quantitative comparison of different set-ups. It is shown that using suction fans lowers the recovery time of the metal oxide gas sensors by a factor of two, while a solid separation between the tubes (a "septum") is necessary to maintain the sensitivity of the mobile nose to concentration gradients. }, year = {2003} } @inproceedings{Lilienthal138320, author = {Lilienthal, Achim J. and Duckett, Tom}, booktitle = {ROSE 2003 - 1st IEEE International Workshop on Robotic Sensing 2003: Sensing and Perception in 21st Century Robotics : Sensing and Perception in 21st Century Robotics}, institution = {Örebro University, Department of Technology}, institution = {University of Tübingen, Tübingen, Germany}, eid = {1218705}, title = {An absolute positioning system for 100 euros}, DOI = {10.1109/ROSE.2003.1218705}, abstract = {This paper describes an absolute positioning system, which provides accurate and reliable measurements using low-cost equipment that is easy to set up. The system uses a number of fixed web-cameras to track a distinctly coloured object. In order to calculate the (x,y) position of this object, estimates calculated by triangulation from each combination of two cameras are combined, resulting in centimeter-level accuracy. Example applications, including tracking of mobile robots and persons, are described. An extended set-up is also introduced, which allows determination of the heading of a two coloured object from single images }, year = {2003} } @inproceedings{Cielniak138321, author = {Cielniak, Grzegorz and Miladinovic, Mihajlo and Hammarin, Daniel and G{\"o}ransson, Linus and Lilienthal, Achim J. and Duckett, Tom}, booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops : }, institution = {Örebro University, Department of Technology}, institution = {Dept. of Technology, AASS, Örebro University, Örebro, Sweden}, institution = {Dept. of Technology, AASS, Örebro University, Örebro, Sweden}, institution = {Dept. of Technology, AASS, Örebro University, Örebro, Sweden}, eid = {4624346}, title = {Appearance-based tracking of persons with an omnidirectional vision sensor}, volume = {7}, DOI = {10.1109/CVPRW.2003.10072}, abstract = {This paper addresses the problem of tracking a moving person with a single, omnidirectional camera. An appearance-based tracking system is described which uses a self-acquired appearance model and a Kalman filter to estimate the position of the person. Features corresponding to ``depth cues'' are first extracted from the panoramic images, then an artificial neural network is trained to estimate the distance of the person from the camera. The estimates are combined using a discrete Kalman filter to track the position of the person over time. The ground truth information required for training the neural network and the experimental analysis was obtained from another vision system, which uses multiple webcams and triangulation to calculate the true position of the person. Experimental results show that the tracking system is accurate and reliable, and that its performance can be further improved by learning multiple, person-specific appearance models }, ISBN = {0769519008}, year = {2003} } @inproceedings{Lilienthal138301, author = {Lilienthal, Achim J. and Duckett, Tom}, booktitle = {Autonome Mobile Systeme 2003 : }, institution = {Örebro University, Department of Technology}, institution = {WSI, University of Tübingen, Tübingen, Germany}, pages = {161--171}, title = {Approaches to gas source tracing and declaration by pure chemo-tropotaxis}, volume = {18}, DOI = {10.1007/978-3-642-18986-9_17}, abstract = {This paper addresses the problem of localising a static gas source in an uncontrolled indoor environment by a mobile robot. In contrast to previous works, especially the condition of an environment that is not artificially ventilated to produce a strong unidirectional airflow is considered. Here, the propagation of the analyte molecules is dominated by turbulence and convection flow rather than diffusion, thus creating a patchy distribution of spatially distributed eddies. Positive and negative tropotaxis, based on the spatial concentration gradient measured by a pair of electrochemical gas sensor arrays, were investigated. Both strategies were implemented utilising a direct sensor-motor coupling (a Braitenberg vehicle) and were shown to be useful to accomplish the gas source localisation task. As a possible solution to the problem of gas source declaration (the task of determining with certainty that the gas source has been found), an indirect localisation strategy based on exploration and concentration peak avoidance is suggested. Here, a gas source is located by exploiting the fact that local concentration maxima occur more frequently near the gas source compared to distant regions }, ISBN = {978-3-540-20142-7}, ISBN = {978-3-642-18986-9}, year = {2003} } @inproceedings{Lilienthal138302, author = {Lilienthal, Achim J. and Duckett, Tom}, booktitle = {Proceedings : 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003)}, institution = {Örebro University, Department of Technology}, institution = {University of Tübingen, Tübingen, Germany}, pages = {118--123}, title = {Creating gas concentration gridmaps with a mobile robot}, volume = {3}, DOI = {10.1109/IROS.2003.1250615}, abstract = {This paper addresses the problem of mapping the features of a gas distribution by creating concentration gridmaps from the data collected by a mobile robot equipped with an electronic nose. By contrast to metric gridmaps extracted from sonar or laser range scans, a single measurement of the electronic nose provides information about a comparatively small area. To overcome this problem, a mapping technique is introduced that uses a Gaussian density function to model the decreasing likelihood that a particular reading represents the true concentration with respect to the distance from the point of measurement. This method is evaluated in terms of its suitability regarding the slow response and recovery of the gas sensors. The stability of the mapped features and the capability to use concentration gridmaps to locate a gas source are also discussed. }, ISBN = {0-7803-7860-1}, year = {2003} } @inproceedings{Lilienthal138304, author = {Lilienthal, Achim J. and Duckett, Tom}, booktitle = {Proceedings of the 11th International Conference on Advanced Robotics 2003 : }, institution = {Örebro University, Department of Technology}, institution = {University of Tübingen, Tübingen, Germany}, note = {[Best Paper Award on ICAR 2003]}, pages = {375--380}, publisher = {Coimbra, University}, title = {Experimental analysis of smelling Braitenberg vehicles}, volume = {1-3}, abstract = {This paper addresses the problem of localisation of a static odour source in an unstructured indoor environment by a mobile robot using electrochemical gas sensors. In particular, reactive localisation strategies based on the instantaneously measured spatial concentration gradient are considered. In contrast to previous works, the environment is not artificially ventilated to produce a strong constant airflow, and thus the distribution of the odour molecules is dominated by turbulence. An experimental set-up is presented that enables different strategies for odour source localisation to be compared directly in a precisely measured experiment. Two alternative strategies that utilise a direct sensor-motor coupling are then investigated and a detailed numerical analysis of the results is presented, including tests of statistical significance. Both tested strategies proved to be useful to accomplish the localisation task. As a possible solution to the problem of detecting that the odour source - which is usually not corresponding to the global concentration maximum - was found, one of the tested strategies exploits the fact that local concentration maxima occur more frequently near the odour source compared to distant regions }, ISBN = {972-96889-8-2}, year = {2003} } @inproceedings{Wandel138311, author = {Wandel, Michael and Lilienthal, Achim J. and Duckett, Tom and Weimar, Udo and Zell, Andreas}, booktitle = {Proceedings of the IEEE international conference on advanced robotics 2003 : }, institution = {Örebro University, Department of Technology}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, pages = {507--512}, publisher = {University of Coimbra}, title = {Gas distribution in unventilated indoor environments inspected by a mobile robot}, volume = {1-3}, abstract = {Gas source localisation with robots is usually performed in environments with a strong, unidirectional airflow created by artificial ventilation. This tends to create a strong, well defined analyte plume and enables upwind searching. By contrast, this paper presents experiments conducted in unventilated rooms. Here, the measured concentrations also indicate an analyte plume with, however, different properties concerning its shape, width, concentration profile and stability over time. In the results presented in this paper, two very different mobile robotic systems for odour sensing were investigated in different environments, and the similarities as well as differences in the analyte gas distributions measured are discussed. }, ISBN = {972-96889-8-2}, year = {2003} } @inproceedings{Lilienthal138303, author = {Lilienthal, Achim J. and Duckett, Tom}, booktitle = {Proceedings of the European conference on mobile robots : ECMR 2003}, institution = {Örebro University, Department of Technology}, institution = {University of Tübingen, Tübingen, Germany}, pages = {159--164}, title = {Gas source localisation by constructing concentration gridmaps with a mobile robot}, abstract = {This paper addresses the problem of mapping the features of a gas distribution by creating concentration gridmaps with a mobile robot equipped with a gas-sensitive system ("mobile nose"). By contrast to metric gridmaps extracted from sonar or laser range scans, a gas sensor measurement provides information about a comparatively small area. To overcome this problem, a mapping technique is introduced that uses a Gaussian density function to model the decreasing likelihood that a particular reading represents the true concentration with respect to the distance from the point of measurement. The structure of the mapped features is discussed with respect to the parameters of the applied density function, the evolution of the gas distribution over time, and the capability to locate a gas source. }, year = {2003} } @inproceedings{Lilienthal138300, author = {Lilienthal, Achim J. and Reiman, Denis and Zell, Andreas}, booktitle = {Autonome mobile systeme 2003 : }, institution = {WSI, University of Tubingen, Tübingen, Germany}, institution = {WSI, University of Tubingen, Tübingen, Germany}, institution = {WSI, University of Tubingen, Tübingen, Germany}, pages = {150--160}, title = {Gas source tracing with a mobile robot using an adapted moth strategy}, volume = {18}, DOI = {10.1007/978-3-642-18986-9_16}, abstract = {As a sub-task of the general gas source localisation problem, gas source tracing is supposed to guide a gas-sensitive mobile system towards a source by using the cues determined from the gas distribution sensed along a driven path. This paper reports on an investigation of a biologically inspired gas source tracing strategy. Similar to the behaviour of the silkworm moth Bombyx mori, the implemented behaviour consists of a fixed motion pattern that realises a local search, and a mechanism that (re-)starts this motion pattern if an increased gas concentration is sensed. While the moth uses the local airflow direction to orient the motion pattern, this is not possible for a mobile robot due to the detection limits of currently available anemometers. Thus, an alternative method was implemented that uses an asymmetric motion pattern, which is biased towards the side where higher gas sensor readings were obtained. The adaptated strategy was implemented and tested on an experimental platform. This paper describes the strategy and evaluates its performance in terms of the ability to drive the robot towards a gas source and to keep it within close proximity of the source }, ISBN = {978-3-540-20142-7}, ISBN = {978-3-642-18986-9}, year = {2003} } @inproceedings{Lilienthal138312, author = {Lilienthal, Achim J. and Wandel, Michael R. and Weimar, Udo and Zell, Andreas}, booktitle = {Robotik 2002 : Leistungsstand - Anwendungen - Visionen - Trends}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, pages = {689--694}, publisher = {V D I-V D E - VERLAG GMBH}, title = {Detection and Localization of an Odour Source by an autonomous mobile Robot}, series = {VDI Berichte}, number = {1679}, volume = {1679}, abstract = {This paper presents studies concerning the use of an electronic nose on an autonomous mobile robot. In particular experiments were introduced in which a mobile robot generates two dimensional concentration maps of a known target gas in an unventilated room. It was shown that these concentration maps are clearly related to the position of the odour source. Moreover our results show that if accurate localization of the odour source itself is desired one has to consider weak air currents which usually occur even in closed unventilated rooms (often caused by convection). }, ISBN = {3-18-091679-6}, year = {2002} } @inproceedings{Wandel138322, author = {Wandel, Michael and Lilienthal, Achim J. and Zell, Andreas and Weimar, Udo}, booktitle = {Proceedings of the international symposium on olfaction and electronic nose : ISOEN 2002}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, pages = {128--129}, title = {Mobile robot using different senses}, year = {2002} } @inproceedings{Lilienthal138313, author = {Lilienthal, Achim J. and Zell, Andreas and Wandel, Michael R. and Weimar, Udo}, booktitle = {Proceedings of EUROBOT 2001, 4th European workshop on advanced mobile robots : }, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, pages = {1--8}, title = {Experiences using gas sensors on an autonomous mobile robot}, abstract = {This paper reports on experiences concerning the deployment of gas sensors on an autonomous mobile robot. It particularly addresses the suitability of the developed system to localize a distant odour source. First experiments were undertaken in which the robot was ordered to move along different weakly ventilated corridors, while keeping track of its center (framing a '1D' scenario). The measured sensor values show evident peaks that roughly indicate the location of the odour source, if the robot moves with a speed not too low. In this case the system proved to be well suited to detect even weak odour sources. Otherwise the observed course of the received values show many peaks hardly correlated with the location of the odour source. Several investigations were performed to clear up this behaviour but it is still not possible to make concluding statements about the reasons. Finally the setup to perform experiments in a '2D' scenario is described and concerning results of first investigations are presented. It was shown that the utilized system is also capable of detecting a distant odour source in a 2D environment and that the somewhat harder localization task has to account for some weak airflow even in closed, unventilated rooms. }, year = {2001} } @inproceedings{Wandel138314, author = {Wandel, Michael R. and Weimar, Udo and Lilienthal, Achim J. and Zell, Andreas}, booktitle = {The 8th IEEE international conference on electronics, circuits and systems : ICECS 2001}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, pages = {1247--1250}, eid = {957441}, title = {Leakage localisation with a mobile robot carrying chemical sensors}, series = {Proceedings of the IEEE International Conference on Electronics, Circuits, and Systems}, number = {3}, volume = {3}, DOI = {10.1109/ICECS.2001.957441}, abstract = {On the way to developing an electronic watchman one more sense, i.e. gas sensing facilities, are added to an autonomous mobile robot. For the gas detection, up to eight metal oxide sensors are operated using a commercial sensor system. The robot is able to move and navigate autonomously. The geometric information is extracted from laser range finder data. This input is used to build up an internal map while driving. Using the new sensor the localisation of a gas source in unventilated in-house environments is performed. First experiments in a one-dimensional case show a very good correlation between the peak and the gas source. The one-dimensional concentration profile is repeatedly recorded and stable for at least two hours. The two-dimensional experiments exhibit a circulation of the air within the room due to temperature and hence density effects. The latter is limiting the available recording time for the two-dimensional mapping }, ISBN = {0-7803-7057-0}, year = {2001} } @inproceedings{Lilienthal138315, author = {Lilienthal, Achim J. and Wandel, Michael and Weimar, Udo and Zell, Andreas}, booktitle = {Proceedings 2001 ICRA : IEEE international conference on robotics and automation}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, pages = {4005--4010}, title = {Sensing odour sources in indoor environments without a constant airflow by a mobile robot}, volume = {1}, DOI = {10.1109/ROBOT.2001.933243}, abstract = {This paper describes the assembly of a mobile odour sensing system and investigates its practical operation in an indoor environment without a constant airflow. Lacking a constant airflow leads to a problem which cannot be neglected in real world applications. The response of the metal oxide gas sensors used is dominated by air turbulence rather than concentration differences. We show that this problem can be overcome by driving the robot with a constant speed, thus adding an extra constant airflow relative to the gas sensors location. If the robot's speed is not too low the system described proved to be well suited to detect even weak odour sources. Since driving with constant speed is an indispensable condition to perform the basic tasks of a mobile odour sensing system, a new localization strategy is proposed, which takes this into account. }, ISBN = {0-7803-6576-3}, year = {2001} } @inproceedings{Lilienthal138317, author = {Lilienthal, Achim J. and Wandel, Michael and Weimar, Udo and Zell, Andreas}, booktitle = {Autonome Mobile Systeme 2000 : }, institution = {The University of Tübingen, Tübingen, Germany}, institution = {The University of Tübingen, Tübingen, Germany}, institution = {The University of Tübingen, Tübingen, Germany}, institution = {The University of Tübingen, Tübingen, Germany}, pages = {201--209}, title = {Ein autonomer mobiler Roboter mit elektronischer Nase}, series = {Informatik Aktuell}, number = {16}, volume = {16}, ISBN = {3-540-41214-X}, year = {2000} } @article{Baumanns1836113, author = {Baumanns, Lukas and Pitta-Pantazi, Demetra and Demosthenous, Eleni and Lilienthal, Achim J. and Christou, Constantinos and Schindler, Maike}, institution = {Örebro University, School of Science and Technology}, institution = {Technical University Dortmund, Dortmund, Germany}, institution = {University of Cyprus, Nicosia, Cyprus}, institution = {University of Cyprus, Nicosia, Cyprus}, institution = {TU Munich, Munich, Germany}, institution = {University of Cyprus, Nicosia, Cyprus}, institution = {University of Cologne, Cologne, Germany}, journal = {International Journal of Science and Mathematics Education}, note = {Open Access funding enabled and organized by Projekt DEAL. This publication has received funding from the Erasmus + grant program of the European Union under grant agreement No 2020–1-DE03-KA201-077597.}, title = {Pattern-Recognition Processes of First-Grade Students : An Explorative Eye-Tracking Study}, DOI = {10.1007/s10763-024-10441-x}, keywords = {Pattern recognition, Eye tracking, Mathematical difficulties, First-grade students}, abstract = {Recognizing patterns is an essential skill in early mathematics education. However, first graders often have difficulties with tasks such as extending patterns of the form ABCABC. Studies show that this pattern-recognition ability is a good predictor of later pre-algebraic skills and mathematical achievement in general, or the development of mathematical difficulties on the other hand. To be able to foster children's pattern-recognition ability, it is crucial to investigate and understand their pattern-recognition processes early on. However, only a few studies have investigated the processes used to recognize patterns and how these processes are adapted to different patterns. These studies used external observations or relied on children's self-reports, yet young students often lack the ability to properly report their strategies. This paper presents the results of an empirical study using eye-tracking technology to investigate the pattern-recognition processes of 22 first-grade students. In particular, we investigated students with and without the risk of developing mathematical difficulties. The analyses of the students' eye movements reveal that the students used four different processes to recognize patterns-a finding that refines knowledge about pattern-recognition processes from previous research. In addition, we found that for patterns with different units of repeat (i.e. ABABAB versus ABCABCABC), the pattern-recognition processes used differed significantly for students at risk of developing mathematical difficulties but not for students without such risk. Our study contributes to a better understanding of the pattern-recognition processes of first-grade students, laying the foundation for enhanced, targeted support, especially for students at risk of developing mathematical difficulties. }, year = {2024} } @article{Pitta-Pantazi1836032, author = {Pitta-Pantazi, Demetra and Demosthenous, Eleni and Schindler, Maike and Lilienthal, Achim J. and Christou, Constantinos}, institution = {Örebro University, School of Science and Technology}, institution = {University of Cyprus, Nicosia, Cyprus}, institution = {University of Cyprus, Nicosia, Cyprus}, institution = {University of Cologne, Cologne, Germany}, institution = {TU München, Munich, Germany}, institution = {University of Cyprus, Nicosia, Cyprus}, journal = {Educational Studies in Mathematics}, title = {Structure sense in students' quantity comparison and repeating pattern extension tasks : an eye-tracking study with first graders}, DOI = {10.1007/s10649-023-10290-5}, keywords = {Eye tracking, Quantity comparison, Repeating pattern extension, Structure sense, Serial strategies}, abstract = {There is growing evidence that the ability to perceive structure is essential for students' mathematical development. Looking at students' structure sense in basic numerical and patterning tasks seems promising for understanding how these tasks set the foundation for the development of later mathematical skills. Previous studies have shown how students use structure sense in enumeration tasks. However, little is known about students' use of structure sense in other early mathematical tasks. The main aim of this study is to investigate the ways in which structure sense is manifested in first-grade students' work across tasks, in quantity comparison and repeating pattern extension tasks. We investigated students' strategies in quantity comparison and pattern extension tasks and how students employ structure sense. We conducted an eye-tracking study with 21 first-grade students, which provided novel insights into commonalities among strategies for these types of tasks. We found that for both tasks, quantity comparison and repeating pattern extension tasks, strategies can be distinguished into those employing structure sense and serial strategies. }, year = {2024} } @inproceedings{Schreiter1830088, author = {Schreiter, Tim and Morillo-Mendez, Lucas and Chadalavada, Ravi T. and Rudenko, Andrey and Billing, Erik and Magnusson, Martin and Arras, Kai O. and Lilienthal, Achim J.}, booktitle = {2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) : Proceedings}, institution = {Örebro University, School of Science and Technology}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany}, institution = {Interaction Lab, University of Skövde, Skövde, Sweden}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany}, institution = {TU Munich, Germany}, pages = {293--300}, title = {Advantages of Multimodal versus Verbal-Only Robot-to-Human Communication with an Anthropomorphic Robotic Mock Driver}, series = {IEEE RO-MAN}, DOI = {10.1109/RO-MAN57019.2023.10309629}, abstract = {Robots are increasingly used in shared environments with humans, making effective communication a necessity for successful human-robot interaction. In our work, we study a crucial component: active communication of robot intent. Here, we present an anthropomorphic solution where a humanoid robot communicates the intent of its host robot acting as an "Anthropomorphic Robotic Mock Driver" (ARMoD). We evaluate this approach in two experiments in which participants work alongside a mobile robot on various tasks, while the ARMoD communicates a need for human attention, when required, or gives instructions to collaborate on a joint task. The experiments feature two interaction styles of the ARMoD: a verbal-only mode using only speech and a multimodal mode, additionally including robotic gaze and pointing gestures to support communication and register intent in space. Our results show that the multimodal interaction style, including head movements and eye gaze as well as pointing gestures, leads to more natural fixation behavior. Participants naturally identified and fixated longer on the areas relevant for intent communication, and reacted faster to instructions in collaborative tasks. Our research further indicates that the ARMoD intent communication improves engagement and social interaction with mobile robots in workplace settings. }, ISBN = {9798350336702}, ISBN = {9798350336719}, year = {2023} } @inproceedings{Zhu1832141, author = {Zhu, Yufei and Rudenko, Andrey and Kucner, Tomasz and Palmieri, Luigi and Arras, Kai and Lilienthal, Achim and Magnusson, Martin}, booktitle = {2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 01-05 October 2023, Detroit, MI, USA : }, institution = {Örebro University, School of Science and Technology}, institution = {Bosch Corporate Research, Robert Bosch GmbH, Stuttgart, Germany}, institution = {Finnish Center for Artificial Intelligence, School of Electrical Engineering, Aalto University, Finland}, institution = {Bosch Corporate Research, Robert Bosch GmbH, Stuttgart, Germany}, institution = {Bosch Corporate Research, Robert Bosch GmbH, Stuttgart, Germany}, institution = {TU Munich, Germany}, pages = {3795--3802}, title = {CLiFF-LHMP : Using Spatial Dynamics Patterns for Long-Term Human Motion Prediction}, series = {IEEE International Conference on Intelligent Robots and Systems. Proceedings}, DOI = {10.1109/IROS55552.2023.10342031}, abstract = {Human motion prediction is important for mobile service robots and intelligent vehicles to operate safely and smoothly around people. The more accurate predictions are, particularly over extended periods of time, the better a system can, e.g., assess collision risks and plan ahead. In this paper, we propose to exploit maps of dynamics (MoDs, a class of general representations of place-dependent spatial motion patterns, learned from prior observations) for long-term human motion prediction (LHMP). We present a new MoD-informed human motion prediction approach, named CLiFF-LHMP, which is data efficient, explainable, and insensitive to errors from an upstream tracking system. Our approach uses CLiFF -map, a specific MoD trained with human motion data recorded in the same environment. We bias a constant velocity prediction with samples from the CLiFF-map to generate multi-modal trajectory predictions. In two public datasets we show that this algorithm outperforms the state of the art for predictions over very extended periods of time, achieving 45 % more accurate prediction performance at 50s compared to the baseline. }, ISBN = {9781665491914}, ISBN = {9781665491907}, year = {2023} } @article{Adolfsson1727222, author = {Adolfsson, Daniel and Magnusson, Martin and Alhashimi, Anas and Lilienthal, Achim and Andreasson, Henrik}, institution = {Örebro University, School of Science and Technology}, institution = {Örebro University, Örebro, Sweden; Computer Engineering Department, University of Baghdad, Baghdad, Iraq}, journal = {IEEE Transactions on robotics}, number = {2}, pages = {1476--1495}, title = {Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large-Scale Radar Odometry in Diverse Environments}, volume = {39}, DOI = {10.1109/tro.2022.3221302}, keywords = {Radar, Sensors, Spinning, Azimuth, Simultaneous localization and mapping, Estimation, Location awareness, Localization, radar odometry, range sensing, SLAM}, abstract = {This article presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments—outdoors, from urban to woodland, and indoors in warehouses and mines—without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach conservative filtering for efficient and accurate radar odometry (CFEAR), we present an in-depth investigation on a wider range of datasets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar simultaneous localization and mapping (SLAM) and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5 Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160 Hz. }, URL = {https://doi.org/10.48550/arXiv.2211.02445}, year = {2023} } @article{Gupta1761421, author = {Gupta, Himanshu and Lilienthal, Achim and Andreasson, Henrik and Kurtser, Polina}, institution = {Örebro University, School of Science and Technology}, institution = {Perception for Intelligent Systems, TechnicalUniversity of Munich, Munich, Germany}, institution = {Centre for Applied Autonomous SensorSystems, Institutionen för naturvetenskap &teknik, Örebro University, Örebro, Sweden; Department of Radiation Science, RadiationPhysics, Umeå University, Umeå, Sweden}, journal = {Journal of Field Robotics}, number = {6}, pages = {1603--1619}, title = {NDT-6D for color registration in agri-robotic applications}, volume = {40}, DOI = {10.1002/rob.22194}, keywords = {agricultural robotics, color pointcloud, in-field sensing, machine perception, RGB-D registration, stereo IR, vineyard}, abstract = {Registration of point cloud data containing both depth and color information is critical for a variety of applications, including in-field robotic plant manipulation, crop growth modeling, and autonomous navigation. However, current state-of-the-art registration methods often fail in challenging agricultural field conditions due to factors such as occlusions, plant density, and variable illumination. To address these issues, we propose the NDT-6D registration method, which is a color-based variation of the Normal Distribution Transform (NDT) registration approach for point clouds. Our method computes correspondences between pointclouds using both geometric and color information and minimizes the distance between these correspondences using only the three-dimensional (3D) geometric dimensions. We evaluate the method using the GRAPES3D data set collected with a commercial-grade RGB-D sensor mounted on a mobile platform in a vineyard. Results show that registration methods that only rely on depth information fail to provide quality registration for the tested data set. The proposed color-based variation outperforms state-of-the-art methods with a root mean square error (RMSE) of 1.1-1.6 cm for NDT-6D compared with 1.1-2.3 cm for other color-information-based methods and 1.2-13.7 cm for noncolor-information-based methods. The proposed method is shown to be robust against noises using the TUM RGBD data set by artificially adding noise present in an outdoor scenario. The relative pose error (RPE) increased similar to 14% for our method compared to an increase of similar to 75% for the best-performing registration method. The obtained average accuracy suggests that the NDT-6D registration methods can be used for in-field precision agriculture applications, for example, crop detection, size-based maturity estimation, and growth modeling. }, year = {2023} } @inproceedings{Gupta1812049, author = {Gupta, Himanshu and Andreasson, Henrik and Magnusson, Martin and Julier, Simon and Lilienthal, Achim J.}, booktitle = {2023 European Conference on Mobile Robots (ECMR) : }, institution = {Örebro University, School of Science and Technology}, institution = {Department of Computer Science, University College London, London, England}, institution = {Perception for Intelligent Systems, Technical University of Munich, Germany }, pages = {43--48}, publisher = {IEEE}, title = {Revisiting Distribution-Based Registration Methods}, series = {European Conference on Mobile Robots}, DOI = {10.1109/ECMR59166.2023.10256416}, abstract = {Normal Distribution Transformation (NDT) registration is a fast, learning-free point cloud registration algorithm that works well in diverse environments. It uses the compact NDT representation to represent point clouds or maps as a spatial probability function that models the occupancy likelihood in an environment. However, because of the grid discretization in NDT maps, the global minima of the registration cost function do not always correlate to ground truth, particularly for rotational alignment. In this study, we examined the NDT registration cost function in-depth. We evaluated three modifications (Student-t likelihood function, inflated covariance/heavily broadened likelihood curve, and overlapping grid cells) that aim to reduce the negative impact of discretization in classical NDT registration. The first NDT modification improves likelihood estimates for matching the distributions of small population sizes; the second modification reduces discretization artifacts by broadening the likelihood tails through covariance inflation; and the third modification achieves continuity by creating the NDT representations with overlapping grid cells (without increasing the total number of cells). We used the Pomerleau Dataset evaluation protocol for our experiments and found significant improvements compared to the classic NDT D2D registration approach (27.7% success rate) using the registration cost functions "heavily broadened likelihood NDT" (HBL-NDT) (34.7% success rate) and "overlapping grid cells NDT" (OGC-NDT) (33.5% success rate). However, we could not observe a consistent improvement using the Student-t likelihood-based registration cost function (22.2% success rate) over the NDT P2D registration cost function (23.7% success rate). A comparative analysis with other state-of-art registration algorithms is also presented in this work. We found that HBL-NDT worked best for easy initial pose difficulties scenarios making it suitable for consecutive point cloud registration in SLAM application. }, ISBN = {9798350307047}, ISBN = {9798350307054}, year = {2023} } @article{Gupta1770024, author = {Gupta, Himanshu and Andreasson, Henrik and Lilienthal, Achim J. and Kurtser, Polina}, institution = {Örebro University, School of Science and Technology}, institution = {Perception for Intelligent Systems, Technical University of Munich, Munich, Germany}, institution = {Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden; Department of Radiation Science, Radiation Physics, Umeå University, Umeå, Sweden}, journal = {Sensors}, number = {10}, eid = {4736}, title = {Robust Scan Registration for Navigation in Forest Environment Using Low-Resolution LiDAR Sensors}, volume = {23}, DOI = {10.3390/s23104736}, keywords = {tree segmentation, LiDAR mapping, forest inventory, SLAM, forestry robotics, scan registration}, abstract = {Automated forest machines are becoming important due to human operators' complex and dangerous working conditions, leading to a labor shortage. This study proposes a new method for robust SLAM and tree mapping using low-resolution LiDAR sensors in forestry conditions. Our method relies on tree detection to perform scan registration and pose correction using only low-resolution LiDAR sensors (16Ch, 32Ch) or narrow field of view Solid State LiDARs without additional sensory modalities like GPS or IMU. We evaluate our approach on three datasets, including two private and one public dataset, and demonstrate improved navigation accuracy, scan registration, tree localization, and tree diameter estimation compared to current approaches in forestry machine automation. Our results show that the proposed method yields robust scan registration using detected trees, outperforming generalized feature-based registration algorithms like Fast Point Feature Histogram, with an above 3 m reduction in RMSE for the 16Chanel LiDAR sensor. For Solid-State LiDAR the algorithm achieves a similar RMSE of 3.7 m. Additionally, our adaptive pre-processing and heuristic approach to tree detection increased the number of detected trees by 13% compared to the current approach of using fixed radius search parameters for pre-processing. Our automated tree trunk diameter estimation method yields a mean absolute error of 4.3 cm (RSME = 6.5 cm) for the local map and complete trajectory maps. }, year = {2023} } @article{Kucner1792782, author = {Kucner, Tomasz Piotr and Magnusson, Martin and Mghames, Sariah and Palmieri, Luigi and Verdoja, Francesco and Swaminathan, Chittaranjan Srinivas and Krajnik, Tomas and Schaffernicht, Erik and Bellotto, Nicola and Hanheide, Marc and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Mobile Robotics Group, School of Electrical Engineering, Aalto University, Finland; Finnish Center for Artificial Intelligence, Finland}, institution = {L-CAS, School of Computer Science, University of Lincoln, Lincoln, UK}, institution = {BOSCH Corporate Research, Renningen, Germany}, institution = {Intelligent Robotics Group, School of Electrical Engineering, Aalto University, Finland}, institution = {Artificial Intelligence Center, Czech Technical University, Praha, Czechia}, institution = {L-CAS, School of Computer Science, University of Lincoln, Lincoln, UK; Department of Information Engineering, Univeristy of Padua, Padova, Italy}, institution = {L-CAS, School of Computer Science, University of Lincoln, Lincoln, UK}, institution = {Technical Univeristy of Munich, Munich, Germany}, journal = {The international journal of robotics research}, note = {Funding agencies:Czech Ministry of Education by OP VVV CZ.02.1.01/0.0/0.0/16 019/0000765Business Finland 9249/31/2021 }, number = {11}, pages = {977--1006}, title = {Survey of maps of dynamics for mobile robots}, volume = {42}, DOI = {10.1177/02783649231190428}, keywords = {mapping, maps of dynamics, localization and mapping, acceptability and trust, human-robot interaction, human-aware motion planning}, abstract = {Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area. }, year = {2023} } @article{Molina1797296, author = {Molina, Sergi and Mannucci, Anna and Magnusson, Martin and Adolfsson, Daniel and Andreasson, Henrik and Hamad, Mazin and Abdolshah, Saeed and Chadalavada, Ravi Teja and Palmieri, Luigi and Linder, Timm and Swaminathan, Chittaranjan Srinivas and Kucner, Tomasz Piotr and Hanheide, Marc and Fernandez-Carmona, Manuel and Cielniak, Grzegorz and Duckett, Tom and Pecora, Federico and Bokesand, Simon and Arras, Kai O. and Haddadin, Sami and Lilienthal, Achim J}, institution = {Örebro University, School of Science and Technology}, institution = {University of Lincoln, Lincoln, U.K}, institution = {Robert Bosch GmbH, Renningen, Germany}, institution = {Technical University of Munich, Munich, Germany}, institution = {Technical University of Munich, Munich, Germany}, institution = {Robert Bosch GmbH, Renningen, Germany}, institution = {Robert Bosch GmbH, Renningen, Germany}, institution = {Aalto University, Aalto, Finland}, institution = {University of Lincoln, Lincoln, U.K.}, institution = {University of Lincoln, Lincoln, U.K.}, institution = {University of Lincoln, Lincoln, U.K.}, institution = {University of Lincoln, Lincoln, U.K.}, institution = {Kollmorgen Automation AB, Mölndal, Sweden}, institution = {Robert Bosch GmbH, Renningen, Germany}, institution = {Technical University of Munich, Munich, Germany}, journal = {IEEE robotics & automation magazine}, title = {The ILIAD Safety Stack : Human-Aware Infrastructure-Free Navigation of Industrial Mobile Robots}, DOI = {10.1109/MRA.2023.3296983}, keywords = {Robots, Safety, Navigation, Mobile robots, Human-robot interaction, Hidden Markov models, Trajectory}, abstract = {Current intralogistics services require keeping up with e-commerce demands, reducing delivery times and waste, and increasing overall flexibility. As a consequence, the use of automated guided vehicles (AGVs) and, more recently, autonomous mobile robots (AMRs) for logistics operations is steadily increasing. }, year = {2023} } @inproceedings{Almeida1808690, author = {Almeida, Tiago and Rudenko, Andrey and Schreiter, Tim and Zhu, Yufei and Guti{\’e;}rrez Maestro, Eduardo and Morillo-Mendez, Lucas and Kucner, Tomasz P. and Martinez Mozos, Oscar and Magnusson, Martin and Palmieri, Luigi and Arras, Kai O. and Lilienthal, Achim}, booktitle = {2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) : }, institution = {Örebro University, School of Science and Technology}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany}, institution = {Mobile Robotics Group, Department of Electrical Engineering and Automation, Aalto University, Finland; FCAI, Finnish Center for Artificial Intelligence, Finland}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany}, pages = {2192--2201}, title = {TH{\"O}R-Magni : Comparative Analysis of Deep Learning Models for Role-Conditioned Human Motion Prediction}, series = {IEEE International Conference on Computer Vision Workshop (ICCVW)}, DOI = {10.1109/ICCVW60793.2023.00234}, abstract = {Autonomous systems, that need to operate in human environments and interact with the users, rely on understanding and anticipating human activity and motion. Among the many factors which influence human motion, semantic attributes, such as the roles and ongoing activities of the detected people, provide a powerful cue on their future motion, actions, and intentions. In this work we adapt several popular deep learning models for trajectory prediction with labels corresponding to the roles of the people. To this end we use the novel THOR-Magni dataset, which captures human activity in industrial settings and includes the relevant semantic labels for people who navigate complex environments, interact with objects and robots, work alone and in groups. In qualitative and quantitative experiments we show that the role-conditioned LSTM, Transformer, GAN and VAE methods can effectively incorporate the semantic categories, better capture the underlying input distribution and therefore produce more accurate motion predictions in terms of Top-K ADE/FDE and log-likelihood metrics. }, URL = {https://openaccess.thecvf.com/content/ICCV2023W/JRDB/papers/de_Almeida_THOR-Magni_Comparative_Analysis_of_Deep_Learning_Models_for_Role-Conditioned_Human_ICCVW_2023_paper.pdf}, ISBN = {9798350307450}, ISBN = {9798350307443}, year = {2023} } @article{Swaminathan1713186, author = {Swaminathan, Chittaranjan Srinivas and Kucner, Tomasz Piotr and Magnusson, Martin and Palmieri, Luigi and Molina, Sergi and Mannucci, Anna and Pecora, Federico and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Finnish Centre for Artificial Intelligence (FCAI), Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland}, institution = {Robert Bosch GmbH Corporate Research, Stuttgart, Germany}, institution = {Lincoln Centre for Autonomous Systems, School of Computer Science, University of Lincoln, Lincoln, United Kingdom}, institution = {School of Science and Technology, Örebro University, Örebro, Sweden}, journal = {Frontiers in Robotics and AI}, eid = {916153}, title = {Benchmarking the utility of maps of dynamics for human-aware motion planning}, volume = {9}, DOI = {10.3389/frobt.2022.916153}, keywords = {ATC, benchmarking, dynamic environments, human-aware motion planning, human-populated environments, maps of dynamics}, abstract = {Robots operating with humans in highly dynamic environments need not only react to moving persons and objects but also to anticipate and adhere to patterns of motion of dynamic agents in their environment. Currently, robotic systems use information about dynamics locally, through tracking and predicting motion within their direct perceptual range. This limits robots to reactive response to observed motion and to short-term predictions in their immediate vicinity. In this paper, we explore how maps of dynamics (MoDs) that provide information about motion patterns outside of the direct perceptual range of the robot can be used in motion planning to improve the behaviour of a robot in a dynamic environment. We formulate cost functions for four MoD representations to be used in any optimizing motion planning framework. Further, to evaluate the performance gain through using MoDs in motion planning, we design objective metrics, and we introduce a simulation framework for rapid benchmarking. We find that planners that utilize MoDs waste less time waiting for pedestrians, compared to planners that use geometric information alone. In particular, planners utilizing both intensity (proportion of observations at a grid cell where a dynamic entity was detected) and direction information have better task execution efficiency. }, year = {2022} } @article{Adolfsson1689786, author = {Adolfsson, Daniel and Castellano-Quero, Manuel and Magnusson, Martin and Lilienthal, Achim J. and Andreasson, Henrik}, institution = {Örebro University, School of Science and Technology}, journal = {Robotics and Autonomous Systems}, eid = {104136}, title = {CorAl : Introspection for robust radar and lidar perception in diverse environments using differential entropy}, volume = {155}, DOI = {10.1016/j.robot.2022.104136}, keywords = {Radar, Introspection, Localization}, abstract = {Robust perception is an essential component to enable long-term operation of mobile robots. It depends on failure resilience through reliable sensor data and pre-processing, as well as failure awareness through introspection, for example the ability to self-assess localization performance. This paper presents CorAl: a principled, intuitive, and generalizable method to measure the quality of alignment between pairs of point clouds, which learns to detect alignment errors in a self-supervised manner. CorAl compares the differential entropy in the point clouds separately with the entropy in their union to account for entropy inherent to the scene. By making use of dual entropy measurements, we obtain a quality metric that is highly sensitive to small alignment errors and still generalizes well to unseen environments. In this work, we extend our previous work on lidar-only CorAl to radar data by proposing a two-step filtering technique that produces high-quality point clouds from noisy radar scans. Thus, we target robust perception in two ways: by introducing a method that introspectively assesses alignment quality, and by applying it to an inherently robust sensor modality. We show that our filtering technique combined with CorAl can be applied to the problem of alignment classification, and that it detects small alignment errors in urban settings with up to 98% accuracy, and with up to 96% if trained only in a different environment. Our lidar and radar experiments demonstrate that CorAl outperforms previous methods both on the ETH lidar benchmark, which includes several indoor and outdoor environments, and the large-scale Oxford and MulRan radar data sets for urban traffic scenarios. The results also demonstrate that CorAl generalizes very well across substantially different environments without the need of retraining. }, year = {2022} } @article{Fan1655127, author = {Fan, Han and Schaffernicht, Erik and Lilienthal, Achim}, institution = {Örebro University, School of Science and Technology}, institution = {Mobile Robotics & Olfaction Lab}, institution = {Mobile Robotics & Olfaction Lab}, institution = {Mobile Robotics & Olfaction Lab}, journal = {Frontiers in Chemistry}, eid = {863838}, title = {Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications}, volume = {10}, DOI = {10.3389/fchem.2022.863838}, keywords = {electronic nose, metal oxide semiconductor sensor, gas detection, gas sensing, open sampling systems, ensemble learning, robotic olfaction}, abstract = {Detecting chemical compounds using electronic noses is important in many gas sensing related applications. A gas detection system is supposed to indicate a significant event, such as the presence of new chemical compounds or a noteworthy change of concentration levels. Existing gas detection methods typically rely on prior knowledge of target analytes to prepare a dedicated, supervised learning model. However, in some scenarios, such as emergency response, not all the analytes of concern are a priori known and their presence are unlikely to be controlled. In this paper, we take a step towards addressing this issue by proposing an ensemble learning-based approach (ELBA) that integrates several one-class classifiers and learns online. The proposed approach is initialized by training several one-class models using clean air only. During the sampling process, the initialized system detects the presence of chemicals, allowing to learn another one-class model and update existing models with self-labelled data. We validated the proposed approach with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an uncontrolled environment. We demonstrated that the ELBA algorithm not only can detect gas exposures but also recognize baseline responses under a suspect short-term sensor drift condition. Depending on the problem setups in practical applications, the present work can be easily hybridized to integrate other supervised learning models when the prior knowledge of target analytes is partially available. }, year = {2022} } @inproceedings{Wiedemann1698762, author = {Wiedemann, Thomas and Schaab, Marius and Gomez, Juan Marchal and Shutin, Dmitriy and Scheibe, Monika and Lilienthal, Achim J.}, booktitle = {2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) : }, institution = {Örebro University, School of Science and Technology}, institution = {Institute of Communications and Navigation, German Aerospace Center (DLR) Oberpfaffenhofen, Germany}, institution = {Institute of Communications and Navigation, German Aerospace Center (DLR) Oberpfaffenhofen, Germany}, institution = {Institute of Communications and Navigation, German Aerospace Center (DLR) Oberpfaffenhofen, Germany}, institution = {Institute of Communications and Navigation, German Aerospace Center (DLR) Oberpfaffenhofen, Germany}, institution = {Institute of Atmospheric Physics, German Aerospace Center (DLR), Oberpfaffenhofen, Germany}, publisher = {IEEE}, title = {Gas Source Localization Based on Binary Sensing with a UAV}, DOI = {10.1109/ISOEN54820.2022.9789553}, keywords = {gas source localization, unmanned aerial vehicle, gas dispersion model, airborne sensing}, abstract = {Precise gas concentration measurements are often difficult, especially by in-situ sensors mounted on an Unmanned Aerial Vehicle (UAV). Simple gas detection, on the other hand, is more robust and reliable, yet brings significantly less information for gas source localization. In this paper, we compensate for the lack of information by a physical model of gas propagation based on the advection-diffusion Partial Differential Equation (PDE). By linking binary gas detection measurements to computed gas concentration using the physical model and an appropriately designed likelihood function, it becomes possible to identify the most likely gas source distribution. The approach was validated in two experiments with ethanol and smoke as "toy" gasses. It is shown that the method is able to successfully localize the source locations in experiments based on gas detection measurements taken by a UAV. }, ISBN = {9781665458603}, ISBN = {9781665458610}, year = {2022} } @inproceedings{Winkler1720220, author = {Winkler, Nicolas P. and Neumann, Patrick P. and Schaffernicht, Erik and Lilienthal, Achim and Poikkim{\"a}ki, Mikko and Kangas, Anneli and S{\"a}{\"a}m{\"a}nen, Arto}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Finnish Institute of Occupational Health, Työterveyslaitos, Finland}, institution = {Finnish Institute of Occupational Health, Työterveyslaitos, Finland}, institution = {Finnish Institute of Occupational Health, Työterveyslaitos, Finland}, title = {Gather Dust and Get Dusted : Long-Term Drift and Cleaning of Sharp GP2Y1010AU0F Dust Sensor in a Steel Factory}, keywords = {Dust sensor, Low-cost, Sensor drift, Sensor network}, abstract = {The Sharp GP2Y1010AU0F is a widely used low-cost dust sensor, but despite its popularity, the manufacturer provides little information on the sensor. We installed 16 sensing nodes with Sharp dust sensors in a hot rolling mill of a steel factory. Our analysis shows a clear correlation between sensor drift and accumulated production of the steel factory. An eye should be kept on the long-term drift of the sensors to prevent early saturation. Two of 16 sensors experienced full saturation, each after around eight and ten months of operation. }, year = {2022} } @inproceedings{Winkler1720225, author = {Winkler, Nicolas P. and Kotlyar, Oleksandr and Schaffernicht, Erik and Fan, Han and Matsukura, Haruka and Ishida, Hiroshi and Neumann, Patrick P. and Lilienthal, Achim}, booktitle = {ROBOT2022 : Fifth Iberian Robotics Conference: Advances in Robotics, Volume 2}, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, Japan}, institution = {Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei, Tokyo, Japan}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, note = {Funding agency:Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science 22H04952}, pages = {178--188}, title = {Learning From the Past : Sequential Deep Learning for Gas Distribution Mapping}, series = {Lecture Notes in Networks and Systems}, number = {590}, volume = {590}, DOI = {10.1007/978-3-031-21062-4_15}, keywords = {Convolutional LSTM, Gas Distribution Mapping, Sequential Learning, Spatial Interpolation}, abstract = {To better understand the dynamics in hazardous environments, gas distribution mapping aims to map the gas concentration levels of a specified area precisely. Sampling is typically carried out in a spatially sparse manner, either with a mobile robot or a sensor network and concentration values between known data points have to be interpolated. In this paper, we investigate sequential deep learning models that are able to map the gas distribution based on a multiple time step input from a sensor network. We propose a novel hybrid convolutional LSTM - transpose convolutional structure that we train with synthetic gas distribution data. Our results show that learning the spatial and temporal correlation of gas plume patterns outperforms a non-sequential neural network model. }, ISBN = {9783031210617}, ISBN = {9783031210624}, year = {2022} } @article{Schindler1694158, author = {Schindler, Maike and Doderer, Jan H. and Simon, Anna L. and Schaffernicht, Erik and Lilienthal, Achim J. and Sch{\"a}fer, Karolin}, institution = {Örebro University, School of Science and Technology}, institution = {Department of Special Education and Rehabilitation, Faculty of Human Sciences, University of Cologne, Cologne, Germany}, institution = {Department of Special Education and Rehabilitation, Faculty of Human Sciences, University of Cologne, Cologne, Germany}, institution = {Department of Special Education and Rehabilitation, Faculty of Human Sciences, University of Cologne, Cologne, Germany}, institution = {Department of Special Education and Rehabilitation, Faculty of Human Sciences, University of Cologne, Cologne, Germany}, journal = {Frontiers in Psychology}, eid = {909775}, title = {Small number enumeration processes of deaf or hard-of-hearing students : A study using eye tracking and artificial intelligence}, volume = {13}, DOI = {10.3389/fpsyg.2022.909775}, keywords = {Artificial Intelligence, deaf or hard-of-hearing students, eye tracking, mathematical difficulties, mathematics education, small number enumeration}, abstract = {Students who are deaf or hard-of-hearing (DHH) often show significant difficulties in learning mathematics. Previous studies have reported that students who are DHH lag several years behind in their mathematical development compared to hearing students. As possible reasons, limited learning opportunities due to a lesser incidental exposure to numerical ideas, delays in language and speech development, and further idiosyncratic difficulties of students who are DHH are discussed; however, early mathematical skills and their role in mathematical difficulties of students who are DHH are not explored sufficiently. In this study, we investigate whether students who are DHH differ from hearing students in their ability to enumerate small sets (1-9)-an ability that is associated with mathematical difficulties and their emergence. Based on a study with N = 63 who are DHH and N = 164 hearing students from third to fifth grade attempting 36 tasks, we used eye tracking, the recording of students' eye movements, to qualitatively investigate student enumeration processes. To reduce the effort of qualitative analysis of around 8,000 student enumeration processes (227 students x 36 tasks), we used Artificial Intelligence, in particular, a clustering algorithm, to identify student enumeration processes from the heatmaps of student gaze distributions. Based on the clustering, we found that gaze distributions of students who are DHH and students with normal hearing differed significantly on a group level, indicating differences in enumeration processes, with students who are DHH using advantageous processes (e.g., enumeration "at a glance") more often than hearing students. The results indicate that students who are DHH do not lag behind in small number enumeration as compared to hearing students but, rather, appear to perform better than their hearing peers in small number enumeration processes, as well as when conceptual knowledge about the part-whole relationship is involved. Our study suggests that the mathematical difficulties of students who are DHH are not related to difficulties in the small number enumeration, which offers interesting perspectives for further research. }, year = {2022} } @article{Schindler1633896, author = {Schindler, Maike and Lilienthal, Achim}, institution = {Örebro University, School of Science and Technology}, institution = {University of Cologne, Cologne, Germany}, journal = {ZDM - the International Journal on Mathematics Education}, note = {Funding agency:Projekt DEAL}, number = {1}, pages = {163--178}, title = {Students’ collaborative creative process and its phases in mathematics : an explorative study using dual eye tracking and stimulated recall interviews}, volume = {54}, DOI = {10.1007/s11858-022-01327-9}, keywords = {Mathematics Education, Creativity, Collaboration, Eye Tracking, Stimulated Recall Interviews}, abstract = {In the age of artificial intelligence where standard problems are increasingly processed by computers, creative problem solving, the ability to think outside the box is in high demand. Collaboration is also increasingly significant, which makes creative collaboration an important twenty-first-century skill. In the research described in this paper, we investigated students’ collaborative creative process in mathematics and explored the collaborative creative process in its phases. Since little is known about the collaborative creative process, we conducted an explorative case study, where two students jointly worked on a multiple solution task. For in-depth insight into the dyad’s collaborative creative process, we used a novel research design in mathematics education, DUET SRI: both students wore eye-tracking glasses during their collaborative work for dual eye-tracking (DUET) and they each participated in a subsequent stimulated recall interview (SRI) where eye-tracking videos from their joint work served as stimulus. Using an inductive data analysis method, we then identified the phases of the students’ collaborative creative process. We found that the collaborative creative process and its phases had similarities to those previously found for solo creative work, yet the process was more complex and volatile and involved different branches. Based on our findings, we present a tentative model of the dyad’s collaborative process in its phases, which can help researchers and educators trace and foster the collaborative creative process more effectively. }, year = {2022} } @inproceedings{Winkler1698792, author = {Winkler, Nicolas P. and Matsukura, Haruka and Neumann, Patrick P. and Schaffernicht, Erik and Ishida, Hiroshi and Lilienthal, Achim J.}, booktitle = {2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {University of Electro-Communications, Tokyo, Japan}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Tokyo University of Agriculture and Technology, Tokyo, Japan}, note = {Funding agency:Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) Japan Society for the Promotion of Science 19H02103}, publisher = {IEEE}, title = {Super-Resolution for Gas Distribution Mapping : Convolutional Encoder-Decoder Network}, DOI = {10.1109/ISOEN54820.2022.9789555}, keywords = {gas distribution mapping, spatial interpolation, deep learning, super-resolution, sensor network}, abstract = {Gas distribution mapping is important to have an accurate understanding of gas concentration levels in hazardous environments. A major problem is that in-situ gas sensors are only able to measure concentrations at their specific location. The gas distribution in-between the sampling locations must therefore be modeled. In this research, we interpret the task of spatial interpolation between sparsely distributed sensors as a task of enhancing an image's resolution, namely super-resolution. Because autoencoders are proven to perform well for this super-resolution task, we trained a convolutional encoder-decoder neural network to map the gas distribution over a spatially sparse sensor network. Due to the difficulty to collect real-world gas distribution data and missing ground truth, we used synthetic data generated with a gas distribution simulator for training and evaluation of the model. Our results show that the neural network was able to learn the behavior of gas plumes and outperforms simpler interpolation techniques. }, ISBN = {9781665458603}, ISBN = {9781665458610}, year = {2022} } @inproceedings{Rudenko1718542, author = {Rudenko, Andrey and Palmieri, Luigi and Huang, Wanting and Lilienthal, Achim J. and Arras, Kai O.}, booktitle = {2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) : }, institution = {Örebro University, School of Science and Technology}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany; Mobile Robotics and Olfaction Lab, Örebro University, Örebro, Sweden}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany; TU München, Germany}, institution = {Mobile Robotics and Olfaction Lab}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany}, pages = {636--643}, publisher = {IEEE}, title = {The Atlas Benchmark : an Automated Evaluation Framework for Human Motion Prediction}, series = {IEEE RO-MAN proceedings}, DOI = {10.1109/RO-MAN53752.2022.9900656}, abstract = {Human motion trajectory prediction, an essential task for autonomous systems in many domains, has been on the rise in recent years. With a multitude of new methods proposed by different communities, the lack of standardized benchmarks and objective comparisons is increasingly becoming a major limitation to assess progress and guide further research. Existing benchmarks are limited in their scope and flexibility to conduct relevant experiments and to account for contextual cues of agents and environments. In this paper we present Atlas, a benchmark to systematically evaluate human motion trajectory prediction algorithms in a unified framework. Atlas offers data preprocessing functions, hyperparameter optimization, comes with popular datasets and has the flexibility to setup and conduct underexplored yet relevant experiments to analyze a method's accuracy and robustness. In an example application of Atlas, we compare five popular model- and learning-based predictors and find that, when properly applied, early physics-based approaches are still remarkably competitive. Such results confirm the necessity of benchmarks like Atlas. }, ISBN = {9781728188591}, ISBN = {9781665406802}, year = {2022} } @inproceedings{Schreiter1720267, author = {Schreiter, Tim and Morillo-Mendez, Lucas and Chadalavada, Ravi Teja and Rudenko, Andrey and Billing, Erik Alexander and Lilienthal, Achim J.}, booktitle = {SCRITA Workshop Proceedings (arXiv:2208.11090) : }, institution = {Örebro University, School of Science and Technology}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany}, institution = {Interaction Lab, University of Skövde, Sweden}, title = {The Effect of Anthropomorphism on Trust in an Industrial Human-Robot Interaction}, DOI = {10.48550/arXiv.2208.14637}, abstract = {Robots are increasingly deployed in spaces shared with humans, including home settings and industrial environments. In these environments, the interaction between humans and robots (HRI) is crucial for safety, legibility, and efficiency. A key factor in HRI is trust, which modulates the acceptance of the system. Anthropomorphism has been shown to modulate trust development in a robot, but robots in industrial environments are not usually anthropomorphic. We designed a simple interaction in an industrial environment in which an anthropomorphic mock driver (ARMoD) robot simulates to drive an autonomous guided vehicle (AGV). The task consisted of a human crossing paths with the AGV, with or without the ARMoD mounted on the top, in a narrow corridor. The human and the system needed to negotiate trajectories when crossing paths, meaning that the human had to attend to the trajectory of the robot to avoid a collision with it. There was a significant increment in the reported trust scores in the condition where the ARMoD was present, showing that the presence of an anthropomorphic robot is enough to modulate the trust, even in limited interactions as the one we present here.  }, year = {2022} } @inproceedings{Schreiter1720261, author = {Schreiter, Tim and Almeida, Tiago Rodrigues de and Zhu, Yufei and Guti{\’e;}rrez Maestro, Eduardo and Morillo-Mendez, Lucas and Rudenko, Andrey and Kucner, Tomasz P. and Martinez Mozos, Oscar and Magnusson, Martin and Palmieri, Luigi and Arras, Kai O. and Lilienthal, Achim}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany }, institution = {Mobile Robotics Group, Department of Electrical Engineering and Automation, Aalto University, Finland}, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany }, institution = {Robert Bosch GmbH, Corporate Research, Stuttgart, Germany }, title = {The Magni Human Motion Dataset : Accurate, Complex, Multi-Modal, Natural, Semantically-Rich and Contextualized}, DOI = {10.48550/arXiv.2208.14925}, keywords = {Dataset, Human Motion Prediction, Eye Tracking}, abstract = {Rapid development of social robots stimulates active research in human motion modeling, interpretation and prediction, proactive collision avoidance, human-robot interaction and co-habitation in shared spaces. Modern approaches to this end require high quality datasets for training and evaluation. However, the majority of available datasets suffers from either inaccurate tracking data or unnatural, scripted behavior of the tracked people. This paper attempts to fill this gap by providing high quality tracking information from motion capture, eye-gaze trackers and on-board robot sensors in a semantically-rich environment. To induce natural behavior of the recorded participants, we utilise loosely scripted task assignment, which induces the participants navigate through the dynamic laboratory environment in a natural and purposeful way. The motion dataset, presented in this paper, sets a high quality standard, as the realistic and accurate data is enhanced with semantic information, enabling development of new algorithms which rely not only on the tracking information but also on contextual cues of the moving agents, static and dynamic environment.  }, year = {2022} } @inproceedings{Fan1696626, author = {Fan, Han and Jonsson, Daniel and Schaffernicht, Erik and Lilienthal, Achim}, booktitle = {2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) : Proceedings}, institution = {Örebro University, School of Science and Technology}, institution = {Örebro University, Örebro, Sweden}, title = {Towards Gas Identification in Unknown Mixtures Using an Electronic Nose with One-Class Learning}, DOI = {10.1109/ISOEN54820.2022.9789607}, keywords = {gas identification, gas mixture, unknown interferent, one-class learning, electronic nose}, abstract = {Gas identification using an electronic nose (e-nose) typically relies on a multi-class classifier trained with extensive data of a limited set of target analytes. Usually, classification performance degrades in the presence of mixtures that include interferents not represented in the training data. This issue limits the applicability of e-noses in real-world scenarios where interferents are a priori unknown. This paper investigates the feasibility of tackling this particular gas identification problem using one-class learning. We propose several training strategies for a one-class support vector machine to deal with gas mixtures composed of a target analyte and an interferent at different concentration levels. Our evaluation indicates that accurate identification of the presence of a target analyte is achievable if it is dominant in a mixture. For interferent-dominant mixtures, extensive training is required, which implies that an improvement in the generalization ability of the one-class model is needed. }, ISBN = {9781665458610}, ISBN = {9781665458603}, year = {2022} } @article{Montazeri1620582, author = {Montazeri, Amir and Lilienthal, Achim and Albertson, John D.}, institution = {Örebro University, School of Science and Technology}, institution = {Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca NY, USA }, institution = {School of Civil and Environmental Engineering, Cornell University, Ithaca NY, USA }, journal = {Atmospheric Environment: X}, note = {Funding agencies:David R. Atkinson Center for a Sustainable Future (ACSF) at Cornell UniversityUnited States Department of Energy (DOE) DE-AR0000749}, eid = {100126}, title = {A spatial land use clustering framework for investigating the role of land use in mediating the effect of meteorology on urban air quality}, volume = {12}, DOI = {10.1016/j.aeaoa.2021.100126}, keywords = {Air pollution profiles, Cluster analysis, Mobile monitoring, Land use effects, K-means, Exceedance probabilities, Unsupervised learning, Machine learning}, abstract = {Air pollution in urban areas is driven by emission sources and modulated by local meteorology, including the effects of urban form on wind speed and ventilation, and thus varies markedly in space and time. Recently, mobile measurement campaigns have been conducted in urban areas to measure the spatial distribution of air pollutant concentrations. While the main focus of these studies has been revealing spatial patterns in mean (or median) concentrations, they have mostly ignored the temporal aspects of air pollution. However, assessing the temporal variability of air pollution is essential in understanding the integrated exposure of individuals to pollutants above critical thresholds. Here, we examine the role of urban land use in mediating the effect of regional meteorology on Nitrogen Dioxide (NO2) concentrations measured in different regions of Oakland, CA. Inspired by Land Use Regression (LUR) models, we cluster 30-m road segments in the urban area based on their land use. The concentration data from the resulting clusters are stratified based on seasonality and conditionally averaged based on concurrent wind speeds. The clustering analysis yielded 7 clusters, with 4 of them chosen for further statistical analysis due to their large sample sizes. Two of the four clusters demonstrated in winter a strong negative linear relationship between NO2 concentration and wind speed (R-2 > 0.87) with a slope of approximately 3 ppb/m s(-1). A weaker correlation and flatter slope was found for the cluster representing road segments belonging to interstate highways (R-2 > 0.73 and slope < 2 ppb/m s(-1)). No significant relationship was found during the summer season. These findings are consistent with the concept of strong vertical mixing due to highway traffic and increased surface heat fluxes during summer weakening the relationship between wind speed and NO2 concentrations. In summary, the clustering analysis framework presented here provides a novel tool for use with large-scale mobile measurements to reveal the effect of urban land form on the temporal dynamics of pollutant concentrations and ultimately human exposure. }, year = {2021} } @inproceedings{Machado1633897, author = {Machado, Tyrone and Fassbender, David and Taheri, Abdolreza and Eriksson, Daniel and Gupta, Himanshu and Molaei, Amirmasoud and Forte, Paolo and Rai, Prashant and Ghabcheloo, Reza and M{\"a}kinen, Saku and Lilienthal, Achim and Andreasson, Henrik and Geimer, Marcus}, booktitle = {Proceedings of the IEEE ICTE Leading Digital Transformation in Business and Society Conference : }, institution = {Örebro University, School of Science and Technology}, institution = {Bosch Rexroth AG, Elchingen, Germany}, institution = {Bosch Rexroth AG, Elchingen, Germany}, institution = {Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland}, institution = {R&D Wheel Loader-Emerging Technologies, Liebherr-Werk Bischofshofen GmbH, Bischofshofen, Austria}, institution = {Institute of Vehicle System Technology, Karlsruhe Institute of Technology, Karlsruhe, Germany}, institution = {Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland}, institution = {Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland}, institution = {Faculty of Management and Business, Tampere University, Tampere, Finland}, institution = {Institute of Vehicle System, Technology Karlsruhe Institute of Technology, Karlsruhe, Germany}, title = {Autonomous Heavy-Duty Mobile Machinery : A Multidisciplinary Collaborative Challenge}, DOI = {10.1109/ICTE51655.2021.9584498}, keywords = {automation, augmentation, autonomous, collaboration, mobile machinery, transaction cost economics}, abstract = {Heavy-duty mobile machines (HDMMs), are a wide range of off-road machinery used in diverse and critical application areas which are currently facing several issues like skilled labor shortage, safety requirements, and harsh work environments in general. Consequently, efforts are underway to increase automation in HDMMs for increased productivity and safety, eventually transitioning to operator-less autonomous HDMMs to address skilled labor shortages. However, HDMM are complex machines requiring continuous physical and cognitive inputs from human operators. Thus, developing autonomous HDMM is a huge challenge, with current research and developments being performed in several independent research domains. Through this study, we use the bounded rationality concept to propose multidisciplinary collaborations for new autonomous HDMMs and apply the transaction cost economics framework to suggest future implications in the HDMM industry. Furthermore, we introduce and provide a conceptual understanding of the autonomous HDMM industry collaborations as a unified approach, while highlighting the practical implications and challenges of the complex nature of such multidisciplinary collaborations. The collaborative challenges and potentials are mapped out between the following topics: mechanical systems, AI methods, software systems, sensors, data and connectivity, simulations and process optimization, business cases, organization theories, and finally, regulatory frameworks. }, ISBN = {9781665438957}, ISBN = {9781665445986}, year = {2021} } @inproceedings{Alhashimi1803369, author = {Alhashimi, Anas and Adolfsson, Daniel and Magnusson, Martin and Andreasson, Henrik and Lilienthal, Achim}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {School of Science and Technology, Örebro University, Örebro, Sweden; Computer Engineering Department, University of Baghdad, Baghdad, Iraq}, title = {BFAR – Bounded False Alarm Rate detector for improved radar odometry estimation}, abstract = {This paper presents a new detector for filtering noise from true detections in radar data, which improves the state of the art in radar odometry. Scanning Frequency-Modulated Continuous Wave (FMCW) radars can be useful for localisation and mapping in low visibility, but return a lot of noise compared to (more commonly used) lidar, which makes the detection task more challenging. Our Bounded False-Alarm Rate (BFAR) detector is different from the classical Constant False-Alarm Rate (CFAR) detector in that it applies an affine transformation on the estimated noise level after which the parameters that minimize the estimation error can be learned. BFAR is an optimized combination between CFAR and fixed-level thresholding. Only a single parameter needs to be learned from a training dataset. We apply BFAR tothe use case of radar odometry, and adapt a state-of-the-art odometry pipeline (CFEAR), replacing its original conservative filtering with BFAR. In this way we reduce the state-of-the-art translation/rotation odometry errors from 1.76%/0.5◦/100 m to 1.55%/0.46◦/100 m; an improvement of 12.5%. }, URL = {https://doi.org/10.48550/arXiv.2109.09669}, year = {2021} } @inproceedings{Adolfsson1595903, author = {Adolfsson, Daniel and Magnusson, Martin and Alhashimi, Anas and Lilienthal, Achim and Andreasson, Henrik}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021) : }, institution = {Örebro University, School of Science and Technology}, pages = {5462--5469}, title = {CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry}, series = {IEEE International Conference on Intelligent Robots and Systems. Proceedings}, DOI = {10.1109/IROS51168.2021.9636253}, keywords = {Localization SLAM Mapping Radar}, abstract = {This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for large-scale radar odometry estimation. By using a filtering technique that keeps the k strongest returns per azimuth and by additionally filtering the radar data in Cartesian space, we are able to compute a sparse set of oriented surface points for efficient and accurate scan matching. Registration is carried out by minimizing a point-to-line metric and robustness to outliers is achieved using a Huber loss. We were able to additionally reduce drift by jointly registering the latest scan to a history of keyframes and found that our odometry method generalizes to different sensor models and datasets without changing a single parameter. We evaluate our method in three widely different environments and demonstrate an improvement over spatially cross-validated state-of-the-art with an overall translation error of 1.76% in a public urban radar odometry benchmark, running at 55Hz merely on a single laptop CPU thread. }, URL = {https://doi.org/10.48550/arXiv.2105.01457}, ISBN = {9781665417143}, ISBN = {9781665417150}, year = {2021} } @inproceedings{Adolfsson1596301, author = {Adolfsson, Daniel and Magnusson, Martin and Liao, Qianfang and Lilienthal, Achim and Andreasson, Henrik}, booktitle = {10th European Conference on Mobile Robots (ECMR 2021) : }, institution = {Örebro University, School of Science and Technology}, title = {CorAl – Are the point clouds Correctly Aligned?}, volume = {10}, DOI = {10.1109/ECMR50962.2021.9568846}, abstract = {In robotics perception, numerous tasks rely on point cloud registration. However, currently there is no method that can automatically detect misaligned point clouds reliably and without environment-specific parameters. We propose "CorAl", an alignment quality measure and alignment classifier for point cloud pairs, which facilitates the ability to introspectively assess the performance of registration. CorAl compares the joint and the separate entropy of the two point clouds. The separate entropy provides a measure of the entropy that can be expected to be inherent to the environment. The joint entropy should therefore not be substantially higher if the point clouds are properly aligned. Computing the expected entropy makes the method sensitive also to small alignment errors, which are particularly hard to detect, and applicable in a range of different environments. We found that CorAl is able to detect small alignment errors in previously unseen environments with an accuracy of 95% and achieve a substantial improvement to previous methods. }, URL = {https://doi.org/10.48550/arXiv.2109.09820}, year = {2021} } @inproceedings{Winkler1631751, author = {Winkler, Nicolas P. and Neumann, Patrick P. and Kohlhoff, Harald and Erdmann, Jessica and Schaffernicht, Erik and Lilienthal, Achim}, booktitle = {SMSI 2021 Proceedings : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany}, pages = {260--261}, title = {Development of a Low-Cost Sensing Node with Active Ventilation Fan for Air Pollution Monitoring}, DOI = {10.5162/SMSI2021/D3.5}, keywords = {wireless sensing node, environmental monitoring, air pollution, sensor network}, abstract = {A fully designed low-cost sensing node for air pollution monitoring and calibration results for several low-cost gas sensors are presented. As the state of the art is lacking information on the importance of an active ventilation system, the effect of an active fan is compared to the passive ventilation of a lamellar structured casing. Measurements obtained in an urban outdoor environment show that readings of the low-cost dust sensor (Sharp GP2Y1010AU0F) are distorted by the active ventilation system. While this behavior requires further research, a correlation with temperature and humidity inside the node shown. }, year = {2021} } @article{Cheng1582071, author = {Cheng, Lu and Meng, Qing-Hao and Lilienthal, Achim and Qi, Pei-Feng}, institution = {Örebro University, School of Science and Technology}, institution = {School of Electrical and Information Engineering, Tianjin University, Tianjin, People's Republic of China }, institution = {School of Electrical and Information Engineering, Tianjin University, Tianjin, People's Republic of China }, institution = {National Computer Network Emergency Response Technical Team/Coordination Center of China (CNCERT/CC), Beijing, People's Republic of China }, journal = {Measurement science and technology}, note = {Funding Agencies:National Key R{\&}amp;D Program of China 2017YFC0306200Natural Science Foundation of Tianjin20JCZDJC00150 20JCYBJC00320}, number = {6}, eid = {062002}, publisher = {IOP Publishing}, title = {Development of compact electronic noses : a review}, volume = {32}, DOI = {10.1088/1361-6501/abef3b}, keywords = {compact electronic nose (e-nose), gas sensor array, hardware circuit, gas path and sampling, on-chip calculation, wearable e-nose, mobile e-nose}, abstract = {An electronic nose (e-nose) is a measuring instrument that mimics human olfaction and outputs 'fingerprint' information of mixed gases or odors. Generally speaking, an e-nose is mainly composed of two parts: a gas sensing system (gas sensor arrays, gas transmission paths) and an information processing system (microprocessor and related hardware, pattern recognition algorithms). It has been more than 30 years since the e-nose concept was introduced in the 1980s. Since then, e-noses have evolved from being large in size, expensive, and power-hungry instruments to portable, low cost devices with low power consumption. This paper reviews the development of compact e-nose design and calculation over the last few decades, and discusses possible future trends. Regarding the compact e-nose design, which is related to its size and weight, this paper mainly summarizes the development of sensor array design, hardware circuit design, gas path (i.e. the path through which the mixed gases to be measured flow inside the e-nose system) and sampling design, as well as portable design. For the compact e-nose calculation, which is directly related to its rapidity of detection, this review focuses on the development of on-chip calculation and wireless computing. The future trends of compact e-noses include the integration with the internet of things, wearable e-noses, and mobile e-nose systems. }, year = {2021} } @inproceedings{Wiedemann1633892, author = {Wiedemann, Thomas and Shutin, Dmitriy and Lilienthal, Achim}, booktitle = {2021 IEEE International Conference on Autonomous Systems (ICAS) : }, institution = {Örebro University, School of Science and Technology}, institution = {German Aerospace Center, Institute of Communications and Navigation, Oberpfaffenhofen, Germany}, institution = {German Aerospace Center, Institute of Communications and Navigation, Oberpfaffenhofen, Germany}, title = {Experimental Validation of Domain Knowledge Assisted Robotic Exploration and Source Localization}, DOI = {10.1109/ICAS49788.2021.9551145}, keywords = {mobile robot olfaction, gas source localization, Bayesian inference, swarm exploration}, abstract = {In situations where toxic or dangerous airborne material is leaking, mobile robots equipped with gas sensors are a safe alternative to human reconnaissance. This work presents the Domain Knowledge Assisted Robotic Exploration and Source Localization (DARES) approach. It allows a multi-robot system to localize multiple sources or leaks autonomously and independently of a human operator. The probabilistic approach builds upon domain knowledge in the form of a physical model of gas dispersion and the a priori assumption that the dispersion process is driven by multiple but sparsely distributed sources. A formal criterion is used to guide the robots to informative measurement locations and enables inference of the source distribution based on gas concentration measurements. Small-scale indoor experiments under controlled conditions are presented to validate the approach. In all three experiments, three rovers successfully localized two ethanol sources. }, ISBN = {9781728172897}, ISBN = {9781728172903}, year = {2021} } @incollection{Palm1633895, author = {Palm, Rainer and Lilienthal, Achim}, booktitle = {Computational Intelligence : 11th International Joint Conference, IJCCI 2019, Vienna, Austria, September 17–19, 2019, Revised Selected Papers}, institution = {Örebro University, School of Science and Technology}, pages = {191--221}, title = {Fuzzy Geometric Approach to Collision Estimation Under Gaussian Noise in Human-Robot Interaction}, series = {Studies in Computational Intelligence}, number = {922}, DOI = {10.1007/978-3-030-70594-7_8}, keywords = {Human-robot systems, Navigation, Gaussian noise, Kalman filters, Fuzzy modeling}, abstract = {Humans and mobile robots while sharing the same work areas require a high level of safety especially at possible intersections of trajectories. An issue of the human-robot navigation is the computation of the intersection point in the presence of noisy measurements or fuzzy information. For Gaussian distributions of positions/orientations (inputs) of robot and human agent and their parameters the corresponding parameters at the intersections (outputs) are computed by analytical and fuzzy methods.This is done both for the static and the dynamic case using Kalman filters for robot/human positions and orientations and thus for the estimation of the intersection positions. For the overdetermined case (6 inputs, 2 outputs) a so-called ’energetic’ approach is used for the estimation of the point of intersection. The inverse task is discussed, specifying the parameters of the output distributions and looking for the parameters of the input distributions. For larger standard deviations (stds) mixed Gaussian models are suggested as approximation of non-Gaussian distributions. }, ISBN = {9783030705930}, ISBN = {9783030705947}, ISBN = {9783030705961}, year = {2021} } @article{Palmieri1581852, author = {Palmieri, Luigi and Rudenko, Andrey and Mainprice, Jim and Hanheide, Marc and Alahi, Alexandre and Lilienthal, Achim and Arras, Kai O.}, institution = {Örebro University, School of Science and Technology}, institution = {Robert Bosch GmbH Corp Res, Gerlingen, Germany}, institution = {Robert Bosch GmbH Corp Res, Gerlingen, Germany}, institution = {University of Stuttgart, Stuttgart, Germany}, institution = {University of Lincoln, Lincoln, England}, institution = {Ecole Polytech Fed Lausanne, Lausanne, Switzerland}, institution = {Robert Bosch GmbH Corp Res, Robot Program, Gerlingen, Germany}, journal = {IEEE Robotics and Automation Letters}, number = {3}, pages = {5613--5617}, title = {Guest Editorial : Introduction to the Special Issue on Long-Term Human Motion Prediction}, volume = {6}, DOI = {10.1109/LRA.2021.3077964}, keywords = {Human-robot interaction, Human motion prediction, motion planning}, abstract = {The articles in this special section focus on long term human motion prediction. This represents a key ability for advanced autonomous systems, especially if they operate in densely crowded and highly dynamic environments. In those settings understanding and anticipating human movements is fundamental for robust long-term operation of robotic systems and safe human-robot collaboration. Foreseeing how a scene with multiple agents evolves over time and incorporating predictions in a proactive manner allows for novel ways of planning and control, active perception, or humanrobot interaction. Recent planning and control approaches use predictive techniques to better cope with the dynamics of the environment, thus allowing the generation of smoother and more legible robot motion. Predictions can be provided as input to the planning or optimization algorithm (e.g. as a cost term or heuristic function), or as additional dimension to consider in the problem formulation (leading to an increased computational complexity). Recent perception techniques deeply interconnect prediction modules with detection, segmentation and tracking, to generally increase the accuracy of different inference tasks, i.e. filtering, predicting. As also indicated by some of the scientific works accepted in this special issue, novel deep learning architectures allow better interleaving of the aforementioned units. }, year = {2021} } @article{Rudenko1546418, author = {Rudenko, Andrey and Palmieri, Luigi and Doellinger, Johannes and Lilienthal, Achim and Arras, Kai O.}, institution = {Örebro University, School of Science and Technology}, institution = {Bosch Corporate Research, Renningen, Germany}, institution = {Bosch Corporate Research, Renningen, Germany}, institution = {Bosch Center for Artificial Intelligence, Renningen, Germany}, institution = {Bosch Corporate Research, Renningen, Germany}, journal = {IEEE Robotics and Automation Letters}, note = {Funding Agency:European Commission 732737}, number = {2}, pages = {3248--3255}, title = {Learning Occupancy Priors of Human Motion From Semantic Maps of Urban Environments}, volume = {6}, DOI = {10.1109/LRA.2021.3062010}, keywords = {Deep learning for visual perception, human detection and tracking, human motion analysis, human motion prediction, semantic scene understanding}, abstract = {Understanding and anticipating human activity is an important capability for intelligent systems in mobile robotics, autonomous driving, and video surveillance. While learning from demonstrations with on-site collected trajectory data is a powerful approach to discover recurrent motion patterns, generalization to new environments, where sufficient motion data are not readily available, remains a challenge. In many cases, however, semantic information about the environment is a highly informative cue for the prediction of pedestrian motion or the estimation of collision risks. In this work, we infer occupancy priors of human motion using only semantic environment information as input. To this end, we apply and discuss a traditional Inverse Optimal Control approach, and propose a novel approach based on Convolutional Neural Networks (CNN) to predict future occupancy maps. Our CNN method produces flexible context-aware occupancy estimations for semantically uniform map regions and generalizes well already with small amounts of training data. Evaluated on synthetic and real-world data, it shows superior results compared to several baselines, marking a qualitative step-up in semantic environment assessment. }, year = {2021} } @inproceedings{Adolfsson1803356, author = {Adolfsson, Daniel and Magnusson, Martin and Alhashimi, Anas and Lilienthal, Achim and Andreasson, Henrik}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {School of Science and Technology, Örebro University, Örebro, Sweden}, title = {Oriented surface points for efficient and accurate radar odometry}, abstract = {This paper presents an efficient and accurate radar odometry pipeline for large-scale localization. We propose a radar filter that keeps only the strongest reflections per-azimuth that exceeds the expected noise level. The filtered radar data is used to incrementally estimate odometry by registering the current scan with a nearby keyframe. By modeling local surfaces, we were able to register scans by minimizing a point-to-line metric and accurately estimate odometry from sparse point sets, hence improving efficiency. Specifically, we found that a point-to-line metric yields significant improvements compared to a point-to-point metric when matching sparse sets of surface points. Preliminary results from an urban odometry benchmark show that our odometry pipeline is accurate and efficient compared to existing methods with an overall translation error of 2.05%, down from 2.78% from the previously best published method, running at 12.5ms per frame without need of environmental specific training.  }, URL = {https://doi.org/10.48550/arXiv.2109.09994}, year = {2021} } @inproceedings{Kucner1633891, author = {Kucner, Tomasz Piotr and Luperto, Matteo and Lowry, Stephanie and Magnusson, Martin and Lilienthal, Achim}, booktitle = {2021 IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, institution = {Applied Intelligent System Lab (AISLab), Università degli Studi di Milano, Milano, Italy}, pages = {1715--1721}, title = {Robust Frequency-Based Structure Extraction}, series = {IEEE International Conference on Robotics and Automation (ICRA)}, DOI = {10.1109/ICRA48506.2021.9561381}, keywords = {Mapping, semantic understanding, indoor environments}, abstract = {State of the art mapping algorithms can produce high-quality maps. However, they are still vulnerable to clutter and outliers which can affect map quality and in consequence hinder the performance of a robot, and further map processing for semantic understanding of the environment. This paper presents ROSE, a method for building-level structure detection in robotic maps. ROSE exploits the fact that indoor environments usually contain walls and straight-line elements along a limited set of orientations. Therefore metric maps often have a set of dominant directions. ROSE extracts these directions and uses this information to segment the map into structure and clutter through filtering the map in the frequency domain (an approach substantially underutilised in the mapping applications). Removing the clutter in this way makes wall detection (e.g. using the Hough transform) more robust. Our experiments demonstrate that (1) the application of ROSE for decluttering can substantially improve structural feature retrieval (e.g., walls) in cluttered environments, (2) ROSE can successfully distinguish between clutter and structure in the map even with substantial amount of noise and (3) ROSE can numerically assess the amount of structure in the map. }, ISBN = {9781728190778}, ISBN = {9781728190785}, year = {2021} } @article{Arain1504014, author = {Arain, Muhammad Asif and Hernandez Bennetts, Victor and Schaffernicht, Erik and Lilienthal, Achim}, institution = {Örebro University, School of Science and Technology}, institution = {Mobile Robotics and Olfaction (MRO) Lab, Center for Applied Autonomous Sensor Systems (AASS), School of Science and Technology, Örebro University, Örebro, Sweden}, journal = {The international journal of robotics research}, note = {Funding Agencies:European Commission ICT-23-2014 645101SURVEYOR (Vinnova) 2017-05468project RAISE 20130196}, number = {4-5}, pages = {782--814}, title = {Sniffing out fugitive methane emissions : autonomous remote gas inspection with a mobile robot}, volume = {40}, DOI = {10.1177/0278364920954907}, keywords = {Environmental monitoring, autonomous exploration, remote gas inspection, mobile robot olfaction, fugitivemethane emissions}, abstract = {Air pollution causes millions of premature deaths every year, and fugitive emissions of, e.g., methane are major causes of global warming. Correspondingly, air pollution monitoring systems are urgently needed. Mobile, autonomous monitoring can provide adaptive and higher spatial resolution compared with traditional monitoring stations and allows fast deployment and operation in adverse environments. We present a mobile robot solution for autonomous gas detection and gas distribution mapping using remote gas sensing. Our ‘‘Autonomous Remote Methane Explorer’’ (ARMEx) is equipped with an actuated spectroscopy-based remote gas sensor, which collects integral gas measurements along up to 30 m long optical beams. State-of-the-art 3D mapping and robot localization allow the precise location of the optical beams to be determined, which then facilitates gas tomography (tomographic reconstruction of local gas distributions from sets of integral gas measurements). To autonomously obtain informative sampling strategies for gas tomography, we reduce the search space for gas inspection missions by defining a sweep of the remote gas sensor over a selectable field of view as a sensing configuration. We describe two different ways to find sequences of sensing configurations that optimize the criteria for gas detection and gas distribution mapping while minimizing the number of measurements and distance traveled. We evaluated anARMExprototype deployed in a large, challenging indoor environment with eight gas sources. In comparison with human experts teleoperating the platform from a distant building, the autonomous strategy produced better gas maps with a lower number of sensing configurations and a slightly longer route. }, year = {2021} } @inproceedings{Winkler1631754, author = {Winkler, Nicolas P. and Neumann, Patrick P. and Schaffernicht, Erik and Lilienthal, Achim}, booktitle = {2021 IEEE SENSORS : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, title = {Using Redundancy in a Sensor Network to Compensate Sensor Failures}, keywords = {Environmental monitoring, wireless sensor network, sensor placement, machine learning}, abstract = {Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks. }, year = {2021} } @inproceedings{Winkler1640648, author = {Winkler, Nicolas P. and Neumann, Patrick P. and Schaffernicht, Erik and Lilienthal, Achim J.}, booktitle = {2021 IEEE SENSORS : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, note = {Funding agency:SAFeRA}, publisher = {IEEE}, title = {Using Redundancy in a Sensor Network to Compensate Sensor Failures}, series = {Proceedings of IEEE Sensors}, DOI = {10.1109/SENSORS47087.2021.9639479}, keywords = {environmental monitoring, wireless sensor network, sensor placement, machine learning}, abstract = {Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks. }, ISBN = {9781728195018}, ISBN = {9781728195025}, year = {2021} } @inproceedings{Rudenko1524236, author = {Rudenko, Andrey and Kucner, Tomasz Piotr and Swaminathan, Chittaranjan Srinivas and Chadalavada, Ravi Teja and Arras, Kai Oliver and Lilienthal, Achim}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {Bosch Corporate Research, Renningen, Germany}, title = {Benchmarking Human Motion Prediction Methods}, keywords = {human motion prediction, benchmarking, datasets}, abstract = {In this extended abstract we present a novel dataset for benchmarking motion prediction algorithms. We describe our approach to data collection which generates diverse and accurate human motion in a controlled weakly-scripted setup. We also give insights for building a universal benchmark for motion prediction. }, year = {2020} } @article{Chadalavada1374911, author = {Chadalavada, Ravi Teja and Andreasson, Henrik and Schindler, Maike and Palm, Rainer and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Faculty of Human Sciences, University of Cologne, Germany}, journal = {Robotics and Computer-Integrated Manufacturing}, note = {Funding Agencies:KKS SIDUS project AIR: "Action and Intention Recognition in Human Interaction with Autonomous Systems"  20140220H2020 project ILIAD: "Intra-Logistics with Integrated Automatic Deployment: Safe and Scalable Fleets in Shared Spaces"  732737}, eid = {101830}, title = {Bi-directional navigation intent communication using spatial augmented reality and eye-tracking glasses for improved safety in human-robot interaction}, volume = {61}, DOI = {10.1016/j.rcim.2019.101830}, keywords = {Human-robot interaction (HRI), Mobile robots, Intention communication, Eye-tracking, Intention recognition, Spatial augmented reality, Stimulated recall interview, Obstacle avoidance, Safety, Logistics}, abstract = {Safety, legibility and efficiency are essential for autonomous mobile robots that interact with humans. A key factor in this respect is bi-directional communication of navigation intent, which we focus on in this article with a particular view on industrial logistic applications. In the direction robot-to-human, we study how a robot can communicate its navigation intent using Spatial Augmented Reality (SAR) such that humans can intuitively understand the robot's intention and feel safe in the vicinity of robots. We conducted experiments with an autonomous forklift that projects various patterns on the shared floor space to convey its navigation intentions. We analyzed trajectories and eye gaze patterns of humans while interacting with an autonomous forklift and carried out stimulated recall interviews (SRI) in order to identify desirable features for projection of robot intentions. In the direction human-to-robot, we argue that robots in human co-habited environments need human-aware task and motion planning to support safety and efficiency, ideally responding to people's motion intentions as soon as they can be inferred from human cues. Eye gaze can convey information about intentions beyond what can be inferred from the trajectory and head pose of a person. Hence, we propose eye-tracking glasses as safety equipment in industrial environments shared by humans and robots. In this work, we investigate the possibility of human-to-robot implicit intention transference solely from eye gaze data and evaluate how the observed eye gaze patterns of the participants relate to their navigation decisions. We again analyzed trajectories and eye gaze patterns of humans while interacting with an autonomous forklift for clues that could reveal direction intent. Our analysis shows that people primarily gazed on that side of the robot they ultimately decided to pass by. We discuss implications of these results and relate to a control approach that uses human gaze for early obstacle avoidance. }, year = {2020} } @incollection{Kucner1430083, author = {Kucner, Tomasz Piotr and Lilienthal, Achim and Magnusson, Martin and Palmieri, L. and Swaminathan, Chittaranjan Srinivas}, booktitle = {Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots : }, institution = {Örebro University, School of Science and Technology}, institution = {Corporate Research, Robert Bosch GmbH, Renningen, Germany}, pages = {143--151}, title = {Closing Remarks}, series = {Cognitive Systems Monographs}, number = {40}, DOI = {10.1007/978-3-030-41808-3_6}, abstract = {Dynamics is an inherent feature of reality. In spite of that, the domain of maps of dynamics has not received a lot of attention yet. In this book, we present solutions for building maps of dynamics and outline how to make use of them for motion planning. In this chapter, we present discuss related research question that as of yet remain to be answered, and derive possible future research directions.  }, ISBN = {978-3-030-41807-6}, ISBN = {978-3-030-41808-3}, year = {2020} } @inproceedings{Lindner1458344, author = {Lindner, Helen and Hill, Wendy and Norling Hermansson, Liselotte and Lilienthal, Achim J.}, booktitle = {MEC20 Symposium Proceedings : }, institution = {Örebro University, School of Health Sciences}, institution = {Örebro University, School of Science and Technology}, institution = {Institute of Biomedical engineering, UNB, Fredericton, Canda}, institution = {University Health Care Research Centre, Faculty of Medicine and Health, Örebro University, Örebro, Sweden; Dept. of Prosthetics and Orthotics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden}, publisher = {University of New Brunswick}, title = {Cognitive load in learning to use a multi-function hand}, keywords = {eye tracking, cognitive load, multi-function prosthetic hand}, abstract = {Despite the promising functions of a multi-function hand, it is challenging to learn to use a hand that has up to 36grip patterns. If it requires too much cognitive load to learn to operate a prosthetic hand, the user may eventually stopusing it. Measurement of cognitive load while learning to use a bionic hand will help the therapist to adjust the trainingpace and help the user to achieve success.An innovative, non-obtrusive method for measuring cognitive load is by tracking eye gaze. Gaze measuresprovide pupil diameters that indicate subjective task difficulty and mental effort. Three subjects wore a pair of Tobiieye-tracking glasses during control training and performed eight activities. Eye-tracking data were imported in TobiiPro Lab software for extracting pupil diameter during the activities. Pupil diameter (normal range: 2-4mm duringnormal light) was used to indicate the amount of cognitive load.Pupil diameters were below 4mm in 9 out of 23 training activities. Pupil diameters were above 4mm in all threesubjects when they used precision pinch to perform the activities “stack 4 1-inch wooden blocks” and “pick up smallobjects”. Subject 3 had pupil diameters over 4mm in all training activities. Pupil diameters were largest when thesubjects were adjusting the grip and when they had difficulties in initiating the grip.It seems appropriate to introduce no more than four grips during the first control training session. Further studyis required to determine if pupil diameters will decrease over time when adequate prosthetic training is given. }, URL = {https://conferences.lib.unb.ca/index.php/mec/article/view/65}, year = {2020} } @article{Burgues1380687, author = {Burgues, Javier and Hernandez Bennetts, Victor and Lilienthal, Achim J. and Marco, Santiago}, institution = {Örebro University, School of Science and Technology}, institution = {Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain; Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Barcelona, Spain}, institution = {Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain; Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Barcelona, Spain}, journal = {Sensors and actuators. B, Chemical}, note = {Funding Agencies:Spanish MINECO program  BES-2015-071698 TEC2014-59229-RH2020-ICT by the European Commission  645101}, eid = {127309}, title = {Gas Distribution Mapping and Source Localization Using a 3D Grid of Metal Oxide Semiconductor Sensors}, volume = {304}, DOI = {10.1016/j.snb.2019.127309}, keywords = {Mobile robotic olfaction, Metal oxide gas sensors, Signal processing, Sensor networks, Gas source localization, Gas distribution mapping}, abstract = {The difficulty to obtain ground truth (i.e. empirical evidence) about how a gas disperses in an environment is one of the major hurdles in the field of mobile robotic olfaction (MRO), impairing our ability to develop efficient gas source localization strategies and to validate gas distribution maps produced by autonomous mobile robots. Previous ground truth measurements of gas dispersion have been mostly based on expensive tracer optical methods or 2D chemical sensor grids deployed only at ground level. With the ever-increasing trend towards gas-sensitive aerial robots, 3D measurements of gas dispersion become necessary to characterize the environment these platforms can explore. This paper presents ten different experiments performed with a 3D grid of 27 metal oxide semiconductor (MOX) sensors to visualize the temporal evolution of gas distribution produced by an evaporating ethanol source placed at different locations in an office room, including variations in height, release rate and air flow. We also studied which features of the MOX sensor signals are optimal for predicting the source location, considering different lengths of the measurement window. We found strongly time-varying and counter-intuitive gas distribution patterns that disprove some assumptions commonly held in the MRO field, such as that heavy gases disperse along ground level. Correspondingly, ground-level gas distributions were rarely useful for localizing the gas source and elevated measurements were much more informative. We make the dataset and the code publicly available to enable the community to develop, validate, and compare new approaches related to gas sensing in complex environments. }, year = {2020} } @article{Hou1391197, author = {Hou, Hui-Rang and Lilienthal, Achim J. and Meng, Qing-Hao}, institution = {Örebro University, School of Science and Technology}, institution = {Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin, China}, institution = {Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin, China}, journal = {IEEE Access}, note = {Funding Agencies:National Natural Science Foundation of China61573253 National Key Research and Development Program of China  2017YFC0306200}, pages = {7227--7235}, eid = {8945323}, title = {Gas Source Declaration with Tetrahedral Sensing Geometries and Median Value Filtering Extreme Learning Machine}, volume = {8}, DOI = {10.1109/ACCESS.2019.2963059}, keywords = {Gas source declaration, tetrahedron, gas concentration measurement, wind information, extreme learning machine, median value filtering}, abstract = {Gas source localization (including gas source declaration) is critical for environmental monitoring, pollution control and chemical safety. In this paper we approach the gas source declaration problem by constructing a tetrahedron, each vertex of which consists of a gas sensor and a three-dimensional (3D) anemometer. With this setup, the space sampled around a gas source can be divided into two categories, i.e. inside (“source in”) and outside (“source out”) the tetrahedron, posing gas source declaration as a classification problem. For the declaration of the “source in” or “source out” cases, we propose to directly take raw gas concentration and wind measurement data as features, and apply a median value filtering based extreme learning machine (M-ELM) method. Our experimental results show the efficacy of the proposed method, yielding accuracies of 93.2% and 100% for gas source declaration in the regular and irregular tetrahedron experiments, respectively. These results are better than that of the ELM-MFC (mass flux criterion) and other variants of ELM algorithms. }, year = {2020} } @inproceedings{Winkler1506465, author = {Winkler, Nicolas P. and Neumann, Patrick P. and S{\"a}{\"a}m{\"a}nen, Arto and Schaffernicht, Erik and Lilienthal, Achim J.}, booktitle = {MATERIALS TODAY-PROCEEDINGS : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Occupational Safety, Finnish Institute of Occupational Health, Tampere, Finland}, note = {Funding Agency:SAF(sic)RA}, pages = {250--253}, title = {High-quality meets low-cost : Approaches for hybrid-mobility sensor networks}, series = {Materials Today: Proceedings}, volume = {32}, DOI = {10.1016/j.matpr.2020.05.799}, keywords = {Mobile robot olfaction, Air quality monitoring, Wireless sensor network, Gas distribution mapping, Occupational health}, abstract = {Air pollution within industrial scenarios is a major risk for workers, which is why detailed knowledge about the dispersion of dusts and gases is necessary. This paper introduces a system combining stationary low-cost and high-quality sensors, carried by ground robots and unmanned aerial vehicles. Based on these dense sampling capabilities, detailed distribution maps of dusts and gases will be created. This system enables various research opportunities, especially on the fields of distribution mapping and sensor planning. Standard approaches for distribution mapping can be enhanced with knowledge about the environment's characteristics, while the effectiveness of new approaches, utilizing neural networks, can be further investigated. The influence of different sensor network setups on the predictive quality of distribution algorithms will be researched and metrics for the quantification of a sensor network's quality will be investigated. }, year = {2020} } @inproceedings{Schindler1523964, author = {Schindler, Maike and Schaffernicht, Erik and Lilienthal, Achim}, booktitle = {Interim Proceedings of the 44th Conference of the International Group for the Psychology of Mathematics Education. Khon Kaen, Thailand: PME : }, institution = {Örebro University, School of Science and Technology}, institution = {University of Cologne, Germany}, pages = {518--527}, title = {Identifying student strategies through eye tracking and unsupervised learning : The case of quantity recognition}, abstract = {Identifying student strategies is an important endeavor in mathematics education research. Eye tracking (ET) has proven to be valuable for this purpose: E.g., analysis of ET videos allows for identification of student strategies, particularly in quantity recognition activities. Yet, “manual”, qualitative analysis of student strategies from ET videos is laborious—which calls for more efficient methods of analysis. Our methodological paper investigates opportunities and challenges of using unsupervised machine learning (USL) in combination with ET data: Based on empirical ET data of N = 164 students and heat maps of their gaze distributions on the task, we used a clustering algorithm to identify student strategies from ET data and investigate whether the clusters are consistent regarding student strategies. }, year = {2020} } @incollection{Kucner1430082, author = {Kucner, Tomasz Piotr and Lilienthal, Achim and Magnusson, Martin and Palmieri, L. and Swaminathan, Chittaranjan Srinivas}, booktitle = {Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots : }, institution = {Örebro University, School of Science and Technology}, institution = {Corporate Research, Robert Bosch GmbH, Renningen, Germany }, pages = {1--13}, title = {Introduction}, series = {Cognitive Systems Monographs}, number = {40}, DOI = {10.1007/978-3-030-41808-3_1}, abstract = {Change and motion are inherent features of reality. The ability to recognise patterns governing changes has allowed humans to thrive in a dynamic reality. Similarly, dynamics awareness can also improve the performance of robots. Dynamics awareness is an umbrella term covering a broad spectrum of concepts. In this chapter, we present the key aspects of dynamics awareness. We introduce two motivating examples presenting the challenges for robots operating in a dynamic environment. We discuss the benefits of using spatial models of dynamics and analyse the challenges of building such models. }, ISBN = {978-3-030-41807-6}, ISBN = {978-3-030-41808-3}, year = {2020} } @incollection{Kucner1429966, author = {Kucner, Tomasz Piotr and Lilienthal, Achim and Magnusson, Martin and Palmieri, L. and Swaminathan, Chittaranjan Srinivas}, booktitle = {Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots : }, institution = {Örebro University, School of Science and Technology}, institution = {Corporate Research, Robert Bosch GmbH, Renningen, Germany}, pages = {15--32}, title = {Maps of Dynamics}, series = {Cognitive Systems Monographs}, DOI = {10.1007/978-3-030-41808-3_2}, abstract = {The task of building maps of dynamics is the key focus of this book, as well as how to use them for motion planning. In this chapter, we present a categorisation and overview of different types of maps of dynamics. Furthermore, we give an overview of approaches to motion planning in dynamic environments, with a focus on motion planning over maps of dynamics.  }, ISBN = {978-3-030-41807-6}, ISBN = {978-3-030-41808-3}, year = {2020} } @incollection{Kucner1429775, author = {Kucner, Tomasz Piotr and Lilienthal, Achim and Magnusson, Martin and Palmieri, L. and Swaminathan, Chittaranjan Srinivas}, booktitle = {Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots : }, institution = {Örebro University, School of Science and Technology}, institution = {Corporate Research, Robert Bosch GmbH, Renningen, Germany}, pages = {65--113}, title = {Modelling Motion Patterns with Circular-Linear Flow Field Maps}, series = {Cognitive Systems Monographs}, number = {40}, DOI = {10.1007/978-3-030-41808-3_4}, abstract = {The shared feature of the flow of discrete objects and continuous media is that they both can be represented as velocity vectors encapsulating direction and speed of motion. In this chapter, we present a method for modelling the flow of discrete objects and continuous media as continuous Gaussian mixture fields. The proposed model associates to each part of the environment a Gaussian mixture model describing the local motion patterns. We also present a learning method, designed to build the model from a set of sparse, noisy and incomplete observations.  }, ISBN = {978-3-030-41807-6}, ISBN = {978-3-030-41808-3}, year = {2020} } @incollection{Kucner1429926, author = {Kucner, Tomasz Piotr and Lilienthal, Achim and Magnusson, Martin and Palmieri, L. and Swaminathan, Chittaranjan Srinivas}, booktitle = {Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots : }, institution = {Örebro University, School of Science and Technology}, institution = {Corporate Research, Robert Bosch GmbH, Renningen, Germany}, pages = {33--64}, title = {Modelling Motion Patterns with Conditional Transition Map}, series = {Cognitive Systems Monographs}, number = {40}, DOI = {10.1007/978-3-030-41808-3_3}, abstract = {The key idea of modelling flow of discrete objects is to capture the way they move through the environment. One method to capture the flow is to observe changes in occupancy caused by the motion of discrete objects. In this chapter, we present a method to model and learn occupancy shifts caused by an object moving through the environment. The key idea is observe temporal changes changes in the occupancy of adjacent cells, and based on the temporal offset infer the direction of the occupancy flow. }, ISBN = {978-3-030-41807-6}, ISBN = {978-3-030-41808-3}, year = {2020} } @incollection{Kucner1430037, author = {Kucner, Tomasz Piotr and Lilienthal, Achim and Magnusson, Martin and Palmieri, L. and Swaminathan, Chittaranjan Srinivas}, booktitle = {Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots : }, institution = {Örebro University, School of Science and Technology}, institution = {Corporate Research, Robert Bosch GmbH, Renningen, Germany}, pages = {115--141}, title = {Motion Planning Using MoDs}, series = {Cognitive Systems Monographs}, number = {40}, DOI = {10.1007/978-3-030-41808-3_5}, abstract = {Maps of dynamics can be beneficial for motion planning. Information about motion patterns in the environment can lead to finding flow-aware paths, allowing robots to align better to the expected motion: either of other agents in the environment or the flow of air or another medium. The key idea of flow-aware motion planning is to include adherence to the flow represented in the MoD into the motion planning algorithm’s sub-units (i.e. cost function, sampling mechanism), thereby biasing the motion planner into obeying local and implicit traffic rules.  }, ISBN = {978-3-030-41807-6}, ISBN = {978-3-030-41808-3}, year = {2020} } @inproceedings{Vintr1524193, author = {Vintr, Tomas and Yan, Zhi and Eyisoy, Kerem and Kubis, Filip and Blaha, Jan and Ulrich, Jiri and Swaminathan, Chittaranjan Srinivas and Molina, Sergi and Kucner, Tomasz Piotr and Magnusson, Martin and Cielniak, Grzegorz and Faigl, Jan and Duckett, Tom and Lilienthal, Achim J. and Krajnik, Tomas}, booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, institution = {Czech Technical University in Prague, Prague, the Czech Republic}, institution = {University of Technology of Belfort-Montbeliard (UTBM), France}, institution = {Department of Computer Engineering, Faculty of Engineering, Marmara University, Turkey}, institution = {Czech Technical University in Prague, Prague, the Czech Republic}, institution = {Czech Technical University in Prague, Prague, the Czech Republic}, institution = {Czech Technical University in Prague, Prague, the Czech Republic}, institution = {University of Lincoln, UK}, institution = {University of Lincoln, UK}, institution = {Czech Technical University in Prague, Prague, the Czech Republic}, institution = {University of Lincoln, UK}, institution = {Czech Technical University in Prague, Prague, the Czech Republic}, note = {Funding agencies:OP VVV CZ.02.101/0.0/0.0/16 019/0000765CSF projects GA18-18858S GC20-27034J SGS19/176/OHK3/3T/13 FR-8J18FR018PHC Barrande programme 40682ZHToyota Partner Robot joint research project (MACPOLO)}, pages = {11197--11204}, title = {Natural Criteria for Comparison of Pedestrian Flow Forecasting Models}, series = {IEEE International Conference on Intelligent Robots and Systems. Proceedings}, DOI = {10.1109/IROS45743.2020.9341672}, abstract = {Models of human behaviour, such as pedestrian flows, are beneficial for safe and efficient operation of mobile robots. We present a new methodology for benchmarking of pedestrian flow models based on the afforded safety of robot navigation in human-populated environments. While previous evaluations of pedestrian flow models focused on their predictive capabilities, we assess their ability to support safe path planning and scheduling. Using real-world datasets gathered continuously over several weeks, we benchmark state-of-theart pedestrian flow models, including both time-averaged and time-sensitive models. In the evaluation, we use the learned models to plan robot trajectories and then observe the number of times when the robot gets too close to humans, using a predefined social distance threshold. The experiments show that while traditional evaluation criteria based on model fidelity differ only marginally, the introduced criteria vary significantly depending on the model used, providing a natural interpretation of the expected safety of the system. For the time-averaged flow models, the number of encounters increases linearly with the percentage operating time of the robot, as might be reasonably expected. By contrast, for the time-sensitive models, the number of encounters grows sublinearly with the percentage operating time, by planning to avoid congested areas and times. }, ISBN = {9781728162133}, ISBN = {9781728162126}, year = {2020} } @article{Hoang1513204, author = {Hoang, Dinh-Cuong and Lilienthal, Achim and Stoyanov, Todor}, institution = {Örebro University, School of Science and Technology}, journal = {Robotics and Autonomous Systems}, eid = {103632}, title = {Object-RPE : Dense 3D Reconstruction and Pose Estimation with Convolutional Neural Networks}, volume = {133}, DOI = {10.1016/j.robot.2020.103632}, keywords = {Object pose estimation, 3D reconstruction, Semantic mapping, 3D registration}, abstract = {We present an approach for recognizing objects present in a scene and estimating their full pose by means of an accurate 3D instance-aware semantic reconstruction. Our framework couples convolutional neural networks (CNNs) and a state-of-the-art dense Simultaneous Localisation and Mapping(SLAM) system, ElasticFusion [1], to achieve both high-quality semantic reconstruction as well as robust 6D pose estimation for relevant objects. We leverage the pipeline of ElasticFusion as a back-bone and propose a joint geometric and photometric error function with per-pixel adaptive weights. While the main trend in CNN-based 6D pose estimation has been to infer an object’s position and orientation from single views of the scene, our approach explores performing pose estimation from multiple viewpoints, under the conjecture that combining multiple predictions can improve the robustness of an object detection system. The resulting system is capable of producing high-quality instance-aware semantic reconstructions of room-sized environments, as well as accurately detecting objects and their 6D poses. The developed method has been verified through extensive experiments on different datasets. Experimental results confirmed that the proposed system achieves improvements over state-of-the-art methods in terms of surface reconstruction and object pose prediction. Our code and video are available at https://sites.google.com/view/object-rpe. }, year = {2020} } @article{Hoang1427623, author = {Hoang, Dinh-Cuong and Lilienthal, Achim and Stoyanov, Todor}, institution = {Örebro University, School of Science and Technology}, journal = {IEEE Robotics and Automation Letters}, number = {2}, pages = {1962--1969}, title = {Panoptic 3D Mapping and Object Pose Estimation Using Adaptively Weighted Semantic Information}, volume = {5}, DOI = {10.1109/LRA.2020.2970682}, keywords = {RGB-D perception, object detection, segmen-tation and categorization, mapping}, abstract = {We present a system capable of reconstructing highly detailed object-level models and estimating the 6D pose of objects by means of an RGB-D camera. In this work, we integrate deep-learning-based semantic segmentation, instance segmentation, and 6D object pose estimation into a state of the art RGB-D mapping system. We leverage the pipeline of ElasticFusion as a backbone and propose modifications of the registration cost function to make full use of the semantic class labels in the process. The proposed objective function features tunable weights for the depth, appearance, and semantic information channels, which are learned from data. A fast semantic segmentation and registration weight prediction convolutional neural network (Fast-RGBD-SSWP) suited to efficient computation is introduced. In addition, our approach explores performing 6D object pose estimation from multiple viewpoints supported by the high-quality reconstruction system. The developed method has been verified through experimental validation on the YCB-Video dataset and a dataset of warehouse objects. Our results confirm that the proposed system performs favorably in terms of surface reconstruction, segmentation quality, and accurate object pose estimation in comparison to other state-of-the-art systems. Our code and video are available at https://sites.google.com/view/panoptic-mope. }, year = {2020} } @incollection{Kucner1430079, author = {Kucner, Tomasz Piotr and Magnusson, Martin and Swaminathan, Chittaranjan Srinivas and Lilienthal, Achim and Palmieri, L.}, booktitle = {Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots : }, institution = {Örebro University, School of Science and Technology}, institution = {Corporate Research Robert Bosch GmbH, Renningen, Germany}, pages = {vii--x}, title = {Preface}, series = {Cognitive Systems Monographs}, number = {40}, ISBN = {978-3-030-41807-6}, ISBN = {978-3-030-41808-3}, year = {2020} } @book{Kucner1427833, author = {Kucner, Tomasz Piotr and Lilienthal, Achim and Magnusson, Martin and Palmieri, Luigi and Swaminathan, Chittaranjan Srinivas}, institution = {Örebro University, School of Science and Technology}, institution = {Corporate Research, Robert Bosch GmbH, Renningen, Germany}, pages = {151}, publisher = {Springer International Publishing}, title = {Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots}, series = {Cognitive Systems Monographs}, number = {40}, DOI = {10.1007/978-3-030-41808-3}, keywords = {Mobile Robots, Probabilistic Mapping, Autonomous Robots, Robots, Cognitive Systems}, abstract = {This book describes how robots can make sense of motion in their surroundings and use the patterns they observe to blend in better in dynamic environments shared with humans.The world around us is constantly changing. Nonetheless, we can find our way and aren’t overwhelmed by all the buzz, since motion often follows discernible patterns. Just like humans, robots need to understand the patterns behind the dynamics in their surroundings to be able to efficiently operate e.g. in a busy airport. Yet robotic mapping has traditionally been based on the static world assumption, which disregards motion altogether. In this book, the authors describe how robots can instead explicitly learn patterns of dynamic change from observations, store those patterns in Maps of Dynamics (MoDs), and use MoDs to plan less intrusive, safer and more efficient paths. The authors discuss the pros and cons of recently introduced MoDs and approaches to MoD-informed motion planning, and provide an outlook on future work in this emerging, fascinating field.  }, ISBN = {978-3-030-41807-6}, ISBN = {978-3-030-41808-3}, year = {2020} } @inproceedings{Swaminathan1524187, author = {Swaminathan, Chittaranjan Srinivas and Kucner, Tomasz Piotr and Magnusson, Martin and Lilienthal, Achim J.}, booktitle = {HRI 2020 Workshop on Test Methods and Metrics for Effective HRI in Real World Human-Robot Teams : }, institution = {Örebro University, School of Science and Technology}, title = {Quantitative Metrics for Execution-Based Evaluation of Human-Aware Global Motion Planning}, URL = {https://hri-methods-metrics.github.io/Prev_years/2020/Swaminathan%20-%20Abstract.pdf}, year = {2020} } @article{Schindler1380651, author = {Schindler, Maike and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Faculty of Human Sciences, University of Cologne, Cologne, Germany}, journal = {International Journal of Science and Mathematics Education}, note = {Funding Agency:{\"O}rebro University}, number = {8}, pages = {1565--1586}, title = {Students' Creative Process in Mathematics : Insights from Eye-Tracking-Stimulated Recall Interview on Students' Work on Multiple Solution Tasks}, volume = {18}, DOI = {10.1007/s10763-019-10033-0}, keywords = {Creative process, Eye tracking (ET), Mathematical creativity, Multiple solution tasks (MSTs), Stimulated recall interview (SRI)}, abstract = {Students' creative process in mathematics is increasingly gaining significance in mathematics education research. Researchers often use Multiple Solution Tasks (MSTs) to foster and evaluate students' mathematical creativity. Yet, research so far predominantly had a product-view and focused on solutions rather than the process leading to creative insights. The question remains unclear how students' process solving MSTs looks like-and if existing models to describe (creative) problem solving can capture this process adequately. This article presents an explorative, qualitative case study, which investigates the creative process of a school student, David. Using eye-tracking technology and a stimulated recall interview, we trace David's creative process. Our findings indicate what phases his creative process in the MST involves, how new ideas emerge, and in particular where illumination is situated in this process. Our case study illustrates that neither existing models on the creative process, nor on problem solving capture David's creative process fully, indicating the need to partially rethink students' creative process in MSTs. }, year = {2020} } @article{Rudenko1387088, author = {Rudenko, Andrey and Kucner, Tomasz Piotr and Swaminathan, Chittaranjan Srinivas and Chadalavada, Ravi Teja and Arras, Kai O. and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Robotics Research, Bosch Corporate Research, Stuttgart, Germany}, institution = {Robotics Research, Bosch Corporate Research, Stuttgart, Germany}, journal = {IEEE Robotics and Automation Letters}, number = {2}, pages = {676--682}, title = {TH{\"O}R : Human-Robot Navigation Data Collection and Accurate Motion Trajectories Dataset}, volume = {5}, DOI = {10.1109/LRA.2020.2965416}, keywords = {Social Human-Robot Interaction, Motion and Path Planning, Human Detection and Tracking}, abstract = {Understanding human behavior is key for robots and intelligent systems that share a space with people. Accordingly, research that enables such systems to perceive, track, learn and predict human behavior as well as to plan and interact with humans has received increasing attention over the last years. The availability of large human motion datasets that contain relevant levels of difficulty is fundamental to this research. Existing datasets are often limited in terms of information content, annotation quality or variability of human behavior. In this paper, we present THÖR, a new dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for position, head orientation, gaze direction, social grouping, obstacles map and goal coordinates. THÖR also contains sensor data collected by a 3D lidar and involves a mobile robot navigating the space. We propose a set of metrics to quantitatively analyze motion trajectory datasets such as the average tracking duration, ground truth noise, curvature and speed variation of the trajectories. In comparison to prior art, our dataset has a larger variety in human motion behavior, is less noisy, and contains annotations at higher frequencies. }, URL = {https://arxiv.org/abs/1909.04403}, year = {2020} } @inproceedings{Mielle1356645, author = {Mielle, Malcolm and Magnusson, Martin and Lilienthal, Achim J.}, booktitle = {2019 European Conference on Mobile Robots (ECMR) : }, institution = {Örebro University, School of Science and Technology}, note = {Funding Agency:EIT Raw Materials project FIREMII  18011}, title = {A comparative analysis of radar and lidar sensing for localization and mapping}, DOI = {10.1109/ECMR.2019.8870345}, abstract = {Lidars and cameras are the sensors most commonly used for Simultaneous Localization And Mapping (SLAM). However, they are not effective in certain scenarios, e.g. when fire and smoke are present in the environment. While radars are much less affected by such conditions, radar and lidar have rarely been compared in terms of the achievable SLAM accuracy. We present a principled comparison of the accuracy of a novel radar sensor against that of a Velodyne lidar, for localization and mapping. We evaluate the performance of both sensors by calculating the displacement in position and orientation relative to a ground-truth reference positioning system, over three experiments in an indoor lab environment. We use two different SLAM algorithms and found that the mean displacement in position when using the radar sensor was less than 0.037 m, compared to 0.011m for the lidar. We show that while producing slightly less accurate maps than a lidar, the radar can accurately perform SLAM and build a map of the environment, even including details such as corners and small walls. }, ISBN = {978-1-7281-3605-9}, ISBN = {978-1-7281-3606-6}, year = {2019} } @inproceedings{Hullmann1350265, author = {H{\"u}llmann, Dino and Neumann, Patrick P. and Monroy, Javier and Lilienthal, Achim J.}, booktitle = {ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Machine Perception and Intelligent Robotics group (MAPIR), Universidad de Malaga, Spain}, eid = {8823330}, title = {A Realistic Remote Gas Sensor Model for Three-Dimensional Olfaction Simulations}, DOI = {10.1109/ISOEN.2019.8823330}, keywords = {gas detector, remote gas sensor, sensor modelling, TDLAS, gas dispersion simulation}, abstract = {Remote gas sensors like those based on the Tunable Diode Laser Absorption Spectroscopy (TDLAS) enable mobile robots to scan huge areas for gas concentrations in reasonable time and are therefore well suited for tasks such as gas emission surveillance and environmental monitoring. A further advantage of remote sensors is that the gas distribution is not disturbed by the sensing platform itself if the measurements are carried out from a sufficient distance, which is particularly interesting when a rotary-wing platform is used. Since there is no possibility to obtain ground truth measurements of gas distributions, simulations are used to develop and evaluate suitable olfaction algorithms. For this purpose several models of in-situ gas sensors have been developed, but models of remote gas sensors are missing. In this paper we present two novel 3D ray-tracer-based TDLAS sensor models. While the first model simplifies the laser beam as a line, the second model takes the conical shape of the beam into account. Using a simulated gas plume, we compare the line model with the cone model in terms of accuracy and computational cost and show that the results generated by the cone model can differ significantly from those of the line model. }, ISBN = {978-1-5386-8327-9}, ISBN = {978-1-5386-8328-6}, year = {2019} } @inproceedings{Adolfsson1391182, author = {Adolfsson, Daniel and Lowry, Stephanie and Magnusson, Martin and Lilienthal, Achim J. and Andreasson, Henrik}, booktitle = {2019 European Conference on Mobile Robots (ECMR) : }, institution = {Örebro University, School of Science and Technology}, title = {A Submap per Perspective : Selecting Subsets for SuPer Mapping that Afford Superior Localization Quality}, DOI = {10.1109/ECMR.2019.8870941}, abstract = {This paper targets high-precision robot localization. We address a general problem for voxel-based map representations that the expressiveness of the map is fundamentally limited by the resolution since integration of measurements taken from different perspectives introduces imprecisions, and thus reduces localization accuracy.We propose SuPer maps that contain one Submap per Perspective representing a particular view of the environment. For localization, a robot then selects the submap that best explains the environment from its perspective. We propose SuPer mapping as an offline refinement step between initial SLAM and deploying autonomous robots for navigation. We evaluate the proposed method on simulated and real-world data that represent an important use case of an industrial scenario with high accuracy requirements in an repetitive environment. Our results demonstrate a significantly improved localization accuracy, up to 46% better compared to localization in global maps, and up to 25% better compared to alternative submapping approaches. }, ISBN = {978-1-7281-3605-9}, year = {2019} } @article{Neumann1347768, author = {Neumann, Patrick P. and H{\"u}llmann, Dino and Krentel, Daniel and Kluge, Martin and Dzierliński, Marcin and Lilienthal, Achim J. and Bartholmai, Matthias}, institution = {Örebro University, School of Science and Technology}, institution = {Department 8 Non-destructive testing, Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Department 8 Non-destructive testing, Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Department 8 Non-destructive testing, Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Department 8 Non-destructive testing, Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Dział Urządzeń Ciśnieniowych, Urząd Dozoru Technicznego (UDT), Poland}, institution = {Department 8 Non-destructive testing, Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, journal = {European Journal of Remote Sensing}, note = {Funding Agencies:German Federal Ministry for Economic Affairs and Energy (BMWi) within the ZIM program  KF2201091HM4BAM }, number = {Sup. 3}, pages = {2--16}, title = {Aerial-based gas tomography : from single beams to complex gas distributions}, volume = {52}, DOI = {10.1080/22797254.2019.1640078}, keywords = {Aerial robot olfaction, mobile robot olfaction, gas tomography, TDLAS, plume}, abstract = {In this paper, we present and validate the concept of an autonomous aerial robot to reconstruct tomographic 2D slices of gas plumes in outdoor environments. Our platform, the so-called Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS), combines a lightweight Tunable Diode Laser Absorption Spectroscopy (TDLAS) gas sensor with a 3-axis aerial stabilization gimbal for aiming at a versatile octocopter. While the TDLAS sensor provides integral gas concentration measurements, it does not measure the distance traveled by the laser diode?s beam nor the distribution of gas along the optical path. Thus, we complement the set-up with a laser rangefinder and apply principles of Computed Tomography (CT) to create a model of the spatial gas distribution from a set of integral concentration measurements. To allow for a fundamental ground truth evaluation of the applied gas tomography algorithm, we set up a unique outdoor test environment based on two 3D ultrasonic anemometers and a distributed array of 10 infrared gas transmitters. We present results showing its performance characteristics and 2D plume reconstruction capabilities under realistic conditions. The proposed system can be deployed in scenarios that cannot be addressed by currently available robots and thus constitutes a significant step forward for the field of Mobile Robot Olfaction (MRO). }, year = {2019} } @article{Wiedemann1284133, author = {Wiedemann, Thomas and Lilienthal, Achim J. and Shutin, Dmitriy}, institution = {Örebro University, School of Science and Technology}, institution = {German Aerospace Center, Oberpfaffenhofen, Germany}, institution = {German Aerospace Center, Oberpfaffenhofen, Germany}, journal = {Sensors}, note = {Funding Agencies:European Commission  645101 Valles Marineris Explorer initiative of DLR (German Aerospace Center) Space Administration }, number = {3}, eid = {520}, title = {Analysis of Model Mismatch Effects for a Model-based Gas Source Localization Strategy Incorporating Advection Knowledge}, volume = {19}, DOI = {10.3390/s19030520}, keywords = {Robotic exploration, gas source localization, mobile robot olfaction, sparse Bayesian learning, multi-agent system, advection-diffusion model}, abstract = {In disaster scenarios, where toxic material is leaking, gas source localization is a common but also dangerous task. To reduce threats for human operators, we propose an intelligent sampling strategy that enables a multi-robot system to autonomously localize unknown gas sources based on gas concentration measurements. This paper discusses a probabilistic, model-based approach for incorporating physical process knowledge into the sampling strategy. We model the spatial and temporal dynamics of the gas dispersion with a partial differential equation that accounts for diffusion and advection effects. We consider the exact number of sources as unknown, but assume that gas sources are sparsely distributed. To incorporate the sparsity assumption we make use of sparse Bayesian learning techniques. Probabilistic modeling can account for possible model mismatch effects that otherwise can undermine the performance of deterministic methods. In the paper we evaluate the proposed gas source localization strategy in simulations using synthetic data. Compared to real-world experiments, a simulated environment provides us with ground truth data and reproducibility necessary to get a deeper insight into the proposed strategy. The investigation shows that (i) the probabilistic model can compensate imperfect modeling; (ii) the sparsity assumption significantly accelerates the source localization; and (iii) a-priori advection knowledge is of advantage for source localization, however, it is only required to have a certain level of accuracy. These findings will help in the future to parameterize the proposed algorithm in real world applications. }, year = {2019} } @article{Lindner1382292, author = {Lindner, Helen Y and Hill, Wendy and Hermansson, Liselotte and Lilienthal, Achim J.}, institution = {Örebro University, School of Health Sciences}, institution = {Örebro University, School of Science and Technology}, institution = {Institute of Biomedical Engineering, UNB, Fredericton, Canada}, institution = {University Health Care Research Centre, Faculty of Medicine and Health, Örebro University, Örebro, Sweden; Dept. of Prosthetics and Orthotics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden}, journal = {Prosthetics and Orthotics International}, number = {1 suppl. 1}, pages = {52--52}, title = {Cognitive load and compensatory movement in learning to use a multi-function hand}, volume = {43}, keywords = {Eye tracking, upper limb prosthetics, cognitive load, compensatory movement}, abstract = {BACKGROUND: Recent technology provides increased dexterity in multi-function hands with the potential to reduce compensatory body movements. However, it is challenging to learn how to operate a hand that has up to 36 grips. While the cognitive load required to use these hands is unknown, it is clear that if the cognitive load is too high, the user may stop using the multi-functional hand or may not take full advantage of its advanced features. AIM: The aim of this project was to compare cognitive load and compensatory movement in using a multi-function hand versus a conventional myo hand. METHOD: An experienced prosthesis user was assessed using his conventional myo hand and an unfamiliar iLimb Ultra hand, with two-site control and the same wrist for both prostheses. He was trained to use power grip, lateral grip and pinch grip and then completed the SHAP test while wearing the Tobii Pro 2 eye-tracking glasses. Pupil diameter (normal range: 2-4mm during normal light) was used to indicate the amount of cognitive load.[1] The number of eye fixations on the prosthesis indicate the need of visual feedback during operation. Dartfish motion capture was used to track the maximum angles for shoulder abduction and elbow flexion. RESULTS: Larger pupils were found in the use of Ilimb ultra (2.6-5.6mm) than in the use of conventional myo hand (2.4-3.5mm) during the SHAP abstract light tests. The pupils dilated most often during changing grips, e.g. switching to pinch grip for the tripod task (from 2.7 to 5.6mm). After training of using power grip and pinch grip repeatedly, the maximum pupil diameter decreased from 5.6 to 3.3mm. The number of eye fixations on the I-limb ultra (295 fixations) were also higher than on the conventional myo-hand (139 fixations). Smaller shoulder abduction and elbow flexion were observed in the use of I-limb ultra (16.6°, 36.1°) than in the use of conventional myo hand (57°, 52.7°). DISCUSSION AND CONCLUSION: Although it is cognitively demanding to learn to use a multi-function hand, it is possible to decrease this demand with adequate prosthetic training. Our results suggest that using a multi-function hand enables reduction of body compensatory movement, however at the cost of a higher cognitive load. Further research with more prosthesis users and other multi-function hands is needed to confirm the study findings. REFERENCES [1] van der Wel P, van Steenbergen H. Psychon Bull Rev 2018; 25(6):2005-15. ACKNOWLEDGEMENTS: This project was supported financially by Norrbacka-Eugenia Foundation, Promobilia Foundation and Örebro University. }, URL = {https://doi.org/10.1177/0309364619883197}, year = {2019} } @inproceedings{Lilienthal1391180, author = {Lilienthal, Achim J. and Schindler, Maike}, booktitle = {43rd Annual Meeting of the International Group for the Psychology of Mathematics Education : }, institution = {Örebro University, School of Science and Technology}, institution = {Department of Special Education, University of Cologne, Germany}, pages = {62--62}, title = {Current Trends in Eye Tracking Research in Mathematics Education: A PME Literature Review : A PME Survey}, volume = {4}, keywords = {Eye Tracking, Mathematics Education Research, Survey, PME}, abstract = {Eye tracking (ET) is a research method that receives growing interest in mathematics education research (MER). This paper aims to give a literature overview, specifically focusing on the evolution of interest in this technology, ET equipment, and analysis methods used in mathematics education. To capture the current state, we focus on papers published in the proceedings of PME, one of the primary conferences dedicated to MER, of the last ten years. We identify trends in interest, methodology, and methods of analysis that are used in the community, and discuss possible future developments. }, URL = {https://arxiv.org/abs/1904.12581}, year = {2019} } @inproceedings{Schindler1390984, author = {Schindler, Maike and Schaffernicht, Erik and Lilienthal, Achim J.}, booktitle = {Proceedings of the 43rd Conference of the International Group for the Psychology of Mathematics Education : }, institution = {Örebro University, School of Science and Technology}, institution = {University of Cologne, Cologne, Germany}, pages = {281--288}, publisher = {PME}, title = {Differences in Quantity Recognition Between Students with and without Mathematical Difficulties Analyzed Through Eye : Analysis Through Eye-Tracking and AI}, volume = {3}, abstract = {Difficulties in mathematics learning are an important topic in practice and research. In particular, researchers and practitioners need to identify students’ needs for support to teach and help them adequately. However, empirical research about group differences of students with and without mathematical difficulties (MD) is still scarce. Previous research suggests that students with MD may differ in their quantity recognition strategies in structured whole number representations from students without MD. This study uses eye-tracking (ET), combined with Artificial Intelligence (AI), in particular pattern recognition methods, to analyze group differences in gaze patterns in quantity recognition of N=164 fifth grade students. }, year = {2019} } @article{Schindler1306375, author = {Schindler, Maike and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Faculty of Human Sciences, University of Cologne, Cologne, Germany}, journal = {Educational Studies in Mathematics}, number = {1}, pages = {123--139}, title = {Domain-specific interpretation of eye tracking data : towards a refined use of the eye-mind hypothesis for the field of geometry}, volume = {101}, DOI = {10.1007/s10649-019-9878-z}, keywords = {Eye tracking, Eye movements, Eye-mind hypothesis, Geometry}, abstract = {Eye tracking is getting increasingly popular in mathematics education research. Studies predominantly rely on the so-called eye-mind hypothesis (EMH), which posits that what persons fixate on closely relates to what they process. Given that the EMH was developed in reading research, we see the risk that implicit assumptions are tacitly adopted in mathematics even though they may not apply in this domain. This article investigates to what extent the EMH applies in mathematics - geometry in particular - and aims to lift the discussion of what inferences can be validly made from eye-tracking data. We use a case study to investigate the need for a refinement of the use of the EMH. In a stimulated recall interview, a student described his original thoughts perusing a gaze-overlaid video recorded when he was working on a geometry problem. Our findings contribute to better a understanding of when and how the EMH applies in the subdomain of geometry. In particular, we identify patterns of eye movements that provide valuable information on students' geometry problem solving: certain patterns where the eye fixates on what the student is processing and others where the EMH does not hold. Identifying such patterns may contribute to an interpretation theory for students' eye movements in geometry - exemplifying a domain-specific theory that may reduce the inherent ambiguity and uncertainty that eye tracking data analysis has. }, year = {2019} } @inproceedings{Hullmann1391191, author = {H{\"u}llmann, Dino and Neumann, Patrick and Scheuschner, Nils and Bartholmai, Matthias and Lilienthal, Achim J.}, booktitle = {2019 IEEE SENSORS : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM) Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM) Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM) Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM) Berlin, Germany}, publisher = {IEEE}, title = {Experimental Validation of the Cone-Shaped Remote Gas Sensor Model}, series = {Proceedings of IEEE Sensors}, DOI = {10.1109/SENSORS43011.2019.8956613}, keywords = {Remote gas sensor model, TDLAS, gas dispersion simulation}, abstract = {Remote gas sensors mounted on mobile robots enable the mapping of gas distributions in large or hardly accessible areas. A challenging task, however, is the generation of three-dimensional distribution maps from these gas measurements. Suitable reconstruction algorithms can be adapted, for instance, from the field of computed tomography (CT), but both their performance and strategies for selecting optimal measuring poses must be evaluated. For this purpose simulations are used, since, in contrast to field tests, they allow repeatable conditions. Although several simulation tools exist, they lack realistic models of remote gas sensors. Recently, we introduced a model for a Tunable Diode Laser Absorption Spectroscopy (TDLAS) gas sensor taking into account the conical shape of its laser beam. However, the novel model has not yet been validated with experiments. In this paper, we compare our model with a real sensor device and show that the assumptions made hold. }, ISBN = {978-1-7281-1634-1}, year = {2019} } @article{Xing1368091, author = {Xing, Yuxin and Vincent, Timothy A. and Fan, Han and Schaffernicht, Erik and Hernandez Bennetts, Victor and Lilienthal, Achim J. and Cole, Marina and Gardner, Julian W.}, institution = {Örebro University, School of Science and Technology}, institution = {School of Engineering, University of Warwick, Coventry, UK}, institution = {School of Engineering, University of Warwick, Coventry, UK}, institution = {School of Engineering, University of Warwick, Coventry, UK}, institution = {School of Engineering, University of Warwick, Coventry, UK}, journal = {IEEE Sensors Journal}, number = {24}, pages = {12418--12431}, title = {FireNose on Mobile Robot in Harsh Environments}, volume = {19}, DOI = {10.1109/JSEN.2019.2939039}, keywords = {FireNose, mobile robot, MOX sensor, gas map, harsh environments}, abstract = {In this work we present a novel multi-sensor unit, a.k.a. FireNose, to detect and discriminate both known and unknown gases in uncontrolled conditions to aid firefighters under harsh conditions. The unit includes three metal oxide (MOX) gas sensors with CMOS micro heaters, a plasmonic enhanced non-dispersive infrared (NDIR) sensor optimized for the detection of CO2, a commercial temperature humidity sensor, and a flow sensor. We developed custom film coatings for the MOX sensors (SnO2, WO3 and NiO) which greatly improved the gas sensitivity, response time and lifetime of the miniature devices. Our proposed system exhibits promising performance for gas sensing in harsh environments, in terms of power consumption (∼ 35 mW at 350°C per MOX sensor), response time (<10 s), robustness and physical size. The sensing unit was evaluated with plumes of gases in both, a laboratory setup on a gas testing rig and on-board a mobile robot operating indoors. These high sensitivity, high-bandwidth sensors, together with online unsupervised gas discrimination algorithms, are able to detect and generate their spatial distribution maps accordingly. In the robotic experiments, the resulting gas distribution maps corresponded well to the actual location of the sources. Therefore, we verified its ability to differentiate gases and generate gas maps in real-world experiments. }, year = {2019} } @incollection{Palm1391193, author = {Palm, Rainer and Chadalavada, Ravi Teja and Lilienthal, Achim}, booktitle = {Computational Intelligence : International Joint Conference, IJCCI2016 Porto, Portugal, November 9–11,2016 Revised Selected Papers}, institution = {Örebro University, School of Science and Technology}, pages = {149--177}, title = {Fuzzy Modeling, Control and Prediction in Human-Robot Systems}, series = {Studies in Computational Intelligence}, number = {792}, DOI = {10.1007/978-3-319-99283-9}, keywords = {Fuzzy control, Fuzzy modeling, Prediction, Human-robot interaction, Human intentions, Obstacle avoidance, Velocity obstacles}, abstract = {A safe and synchronized interaction between human agents and robots in shared areas requires both long distance prediction of their motions and an appropriate control policy for short distance reaction. In this connection recognition of mutual intentions in the prediction phase is crucial to improve the performance of short distance control.We suggest an approach for short distance control inwhich the expected human movements relative to the robot are being summarized in a so-called “compass dial” from which fuzzy control rules for the robot’s reactions are derived. To predict possible collisions between robot and human at the earliest possible time, the travel times to predicted human-robot intersections are calculated and fed into a hybrid controller for collision avoidance. By applying the method of velocity obstacles, the relation between a change in robot’s motion direction and its velocity during an interaction is optimized and a combination with fuzzy expert rules is used for a safe obstacle avoidance. For a prediction of human intentions to move to certain goals pedestrian tracks are modeled by fuzzy clustering, and trajectories of human and robot agents are extrapolated to avoid collisions at intersections. Examples with both simulated and real data show the applicability of the presented methods and the high performance of the results. }, ISBN = {978-3-319-99282-2}, ISBN = {978-3-319-99283-9}, year = {2019} } @inproceedings{Hullmann1360133, author = {H{\"u}llmann, Dino and Neumann, Patrick P. and Lilienthal, Achim J.}, booktitle = {36th Danubia Adria Symposium on Advances in Experimental Mechanics : Extended Abstracts}, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, pages = {49--50}, publisher = {Danubia-Adria Symposium on Advances in Experimental Mechanics}, title = {Gas Dispersion Fluid Mechanics Simulation for Large Outdoor Environments}, keywords = {Gas dispersion simulation, CFD, gas tomography}, abstract = {The development of algorithms for mapping gas distributions and localising gas sources is a challenging task, because gas dispersion is a highly dynamic process and it is impossible to capture ground truth data. Fluid-mechanical simulations are a suitable way to support the development of these algorithms. Several tools for gas dispersion simulation have been developed, but they are not suitable for simulations of large outdoor environments. In this paper, we present a concept of how an existing simulator can be extended to handle both indoor and large outdoor scenarios. }, URL = {https://das2019.zcu.cz/DAS2019_extended_abstracts.pdf}, ISBN = {978-80-261-0876-4}, year = {2019} } @inproceedings{Palm1391173, author = {Palm, Rainer and Lilienthal, Achim J.}, booktitle = {2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) : }, institution = {Örebro University, School of Science and Technology}, pages = {1--6}, eid = {8858796}, title = {Gaussian Noise and the Intersection Problem in Human-Robot Systems : Analytical and Fuzzy Approach}, DOI = {10.1109/FUZZ-IEEE.2019.8858796}, keywords = {Humn Robot Interaction, Human Motion Prediction, Collision Avoidance}, abstract = {In this paper the intersection problem in humanrobot systems with respect to noisy information is discussed. The interaction between humans and mobile robots in shared areas requires a high level of safety especially at the intersections of trajectories. We discuss the intersection problem with respect to noisy information on the basis of an analytic geometrical model and its TS fuzzy version. The transmission of a 2-dimensional Gaussian noise signal, in particular information on human and robot orientations, through a non-linear static system and its fuzzy version, will be described. We discuss the problem: Given the parameters of the input distributions, find the parameters of the output distributions. }, ISBN = {978-1-5386-1729-8}, year = {2019} } @inproceedings{Chadalavada1391172, author = {Chadalavada, Ravi Teja and Andreasson, Henrik and Schindler, Maike and Lilienthal, Achim J.}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {Faculty of Human Sciences, University of Cologne, Germany, Cologne, Gemany}, title = {Implicit intention transference using eye-tracking glasses for improved safety in human-robot interaction}, keywords = {Human-robot interaction, intention communication, eye tracking, spatial augmented reality, electrodermal activity, stress, cognitive load.}, abstract = {Eye gaze can convey information about intentions beyond what can beinferred from the trajectory and head pose of a person. We propose eye-trackingglasses as safety equipment in industrial environments shared by humans androbots. In this work, an implicit intention transference system was developed and implemented. Robot was given access to human eye gaze data, and it responds tothe eye gaze data through spatial augmented reality projections on the sharedfloor space in real-time and the robot could also adapt its path. This allows proactivesafety approaches in HRI for example by attempting to get the human'sattention when they are in the vicinity of a moving robot. A study was conductedwith workers at an industrial warehouse. The time taken to understand the behaviorof the system was recorded. Electrodermal activity and pupil diameter wererecorded to measure the increase in stress and cognitive load while interactingwith an autonomous system, using these measurements as a proxy to quantifytrust in autonomous systems. }, year = {2019} } @article{Wiedemann1339325, author = {Wiedemann, Thomas and Shutin, Dmitriy and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {German Aerospace Center, Oberpfaffenhofen, Germany}, institution = {German Aerospace Center, Oberpfaffenhofen, Germany}, journal = {Robotics and Autonomous Systems}, pages = {66--79}, title = {Model-based gas source localization strategy for a cooperative multi-robot system-A probabilistic approach and experimental validation incorporating physical knowledge and model uncertainties}, volume = {118}, DOI = {10.1016/j.robot.2019.03.014}, keywords = {Robotic exploration, Gas source localization, Multi-agent-system, Partial differential equation, Mobile robot olfaction, Sparse Bayesian learning, Factor graph, Message passing}, abstract = {Sampling gas distributions by robotic platforms in order to find gas sources is an appealing approach to alleviate threats for a human operator. Different sampling strategies for robotic gas exploration exist. In this paper we investigate the benefit that could be obtained by incorporating physical knowledge about the gas dispersion. By exploring a gas diffusion process using a multi-robot system. The physical behavior of the diffusion process is modeled using a Partial Differential Equation (PDE) which is integrated into the exploration strategy. It is assumed that the diffusion process is driven by only a few spatial sources at unknown locations with unknown intensity. The objective of the exploration strategy is to guide the robots to informative measurement locations and by means of concentration measurements estimate the source parameters, in particular, their number, locations and magnitudes. To this end we propose a probabilistic approach towards PDE identification under sparsity constraints using factor graphs and a message passing algorithm. Moreover, message passing schemes permit efficient distributed implementation of the algorithm, which makes it suitable for a multi-robot system. We designed an experimental setup that allows us to evaluate the performance of the exploration strategy in hardware-in-the-loop experiments as well as in experiments with real ethanol gas under laboratory conditions. The results indicate that the proposed exploration approach accelerates the identification of the source parameters and outperforms systematic sampling. (C) 2019 Elsevier B.V. All rights reserved. }, year = {2019} } @article{HernandezBennetts1297002, author = {Hernandez Bennetts, Victor and Kamarudin, Kamarulzaman and Wiedemann, Thomas and Kucner, Tomasz Piotr and Somisetty, Sai Lokesh and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Center of Excellence for Advanced Sensor Technology, School of Mechatronics Engineering, Universiti Malaysia Perlis, Arau Perlis, Malaysia}, institution = {Institute of Communications and Navigation, German Aerospace Center, Oberpfaffenhofen, Germany}, institution = {Department of Mechatronics, Sastra University, Thanjavur, India}, journal = {Sensors}, number = {5}, eid = {E1119}, title = {Multi-Domain Airflow Modeling and Ventilation Characterization Using Mobile Robots, Stationary Sensors and Machine Learning}, volume = {19}, DOI = {10.3390/s19051119}, keywords = {Airflow modeling, environmental monitoring, machine learning, mobile robotics, static sensor networks, ventilation}, abstract = {Ventilation systems are critically important components of many public buildings and workspaces. Proper ventilation is often crucial for preventing accidents, such as explosions in mines and avoiding health issues, for example, through long-term exposure to harmful respirable matter. Validation and maintenance of ventilation systems is thus of key interest for plant operators and authorities. However, methods for ventilation characterization, which allow us to monitor whether the ventilation system in place works as desired, hardly exist. This article addresses the critical challenge of ventilation characterization-measuring and modelling air flow at micro-scales-that is, creating a high-resolution model of wind speed and direction from airflow measurements. Models of the near-surface micro-scale flow fields are not only useful for ventilation characterization, but they also provide critical information for planning energy-efficient paths for aerial robots and many applications in mobile robot olfaction. In this article we propose a heterogeneous measurement system composed of static, continuously sampling sensing nodes, complemented by localized measurements, collected during occasional sensing missions with a mobile robot. We introduce a novel, data-driven, multi-domain airflow modelling algorithm that estimates (1) fields of posterior distributions over wind direction and speed ("ventilation maps", spatial domain); (2) sets of ventilation calendars that capture the evolution of important airflow characteristics at measurement positions (temporal domain); and (3) a frequency domain analysis that can reveal periodic changes of airflow in the environment. The ventilation map and the ventilation calendars make use of an improved estimation pipeline that incorporates a wind sensor model and a transition model to better filter out sporadic, noisy airflow changes. These sudden changes may originate from turbulence or irregular activity in the surveyed environment and can, therefore, disturb modelling of the relevant airflow patterns. We tested the proposed multi-domain airflow modelling approach with simulated data and with experiments in a semi-controlled environment and present results that verify the accuracy of our approach and its sensitivity to different turbulence levels and other disturbances. Finally, we deployed the proposed system in two different real-world industrial environments (foundry halls) with different ventilation regimes for three weeks during full operation. Since airflow ground truth cannot be obtained, we present a qualitative discussion of the generated airflow models with plant operators, who concluded that the computed models accurately depicted the expected airflow patterns and are useful to understand how pollutants spread in the work environment. This analysis may then provide the basis for decisions about corrective actions to avoid long-term exposure of workers to harmful respirable matter. }, year = {2019} } @inproceedings{Hoang1374210, author = {Hoang, Dinh-Cuong and Stoyanov, Todor and Lilienthal, Achim J.}, booktitle = {2019 European Conference on Mobile Robots, ECMR 2019 : Proceedings}, institution = {Örebro University, School of Science and Technology}, eid = {152970}, title = {Object-RPE : Dense 3D Reconstruction and Pose Estimation with Convolutional Neural Networks for Warehouse Robots}, DOI = {10.1109/ECMR.2019.8870927}, abstract = {We present a system for accurate 3D instance-aware semantic reconstruction and 6D pose estimation, using an RGB-D camera. Our framework couples convolutional neural networks (CNNs) and a state-of-the-art dense Simultaneous Localisation and Mapping (SLAM) system, ElasticFusion, to achieve both high-quality semantic reconstruction as well as robust 6D pose estimation for relevant objects. The method presented in this paper extends a high-quality instance-aware semantic 3D Mapping system from previous work [1] by adding a 6D object pose estimator. While the main trend in CNN-based 6D pose estimation has been to infer object's position and orientation from single views of the scene, our approach explores performing pose estimation from multiple viewpoints, under the conjecture that combining multiple predictions can improve the robustness of an object detection system. The resulting system is capable of producing high-quality object-aware semantic reconstructions of room-sized environments, as well as accurately detecting objects and their 6D poses. The developed method has been verified through experimental validation on the YCB-Video dataset and a newly collected warehouse object dataset. Experimental results confirmed that the proposed system achieves improvements over state-of-the-art methods in terms of surface reconstruction and object pose prediction. Our code and video are available at https://sites.google.com/view/object-rpe. }, ISBN = {978-1-7281-3605-9}, year = {2019} } @article{Schindler1391196, author = {Schindler, Maike and Bader, Eveline and Lilienthal, Achim J. and Schindler, Florian and Schabmann, Alfred}, institution = {Örebro University, School of Science and Technology}, institution = {Department of Special Education, University of Cologne, Cologne, Germany}, institution = {Department of Special Education, University of Cologne, Cologne, Germany}, institution = {TU Dortmund University, Dortmund, Germany}, institution = {Department of Special Education, University of Cologne, Cologne, Germany}, journal = {Learning Disabilities: A Contemporary Journal}, number = {1}, pages = {5--28}, publisher = {Learning Disabilities Worldwide}, title = {Quantity Recognition in Structured Whole Number Representations of Students with Mathematical Difficulties : An Eye-Tracking Study}, volume = {17}, keywords = {Mathematical Difficulties, Structured Whole Number Representations, Quantity Recognition, Abacus, Dot-Field, Eye Tracking}, abstract = {Quantity recognition in whole number representations is a fundamental skill children need to acquire in their mathematical development. Despite the observed correlation to mathematics achievement, however, the abil-ity to recognize quantities in structured whole number representations has not been studied extensively. In this article, we investigate how stu-dents with mathematical difficulties (MD) differ from typically develop-ing (TD) students in quantity recognition in structured whole number representations. We use eye tracking (ET), which can help to identify stu-dents’ quantity recognition strategies. In contrast to methods that include collecting verbal answers and reports, ET avoids an additional verbal-ization step, which may be affected by poor language skills and by low meta-cognitive abilities or memory issues when monitoring, recalling,and explaining one’s thoughts. We present an ET study with 20 students of which ten were found to have MD in initial tests (using qualitative and quantitative diagnostics). We used ET glasses, which allow seeing the students’ view of the scene with an augmented visualization of the gaze point projected onto the scene. The obtained gaze-overlaid videos also include audio data and records of students’ answers and utterances. In our study, we did not find significant differences between the error rates of MD and TD students. Response times, however, were longer for students with MD. The analysis of the ET data brought students’ quantity recogni-tion strategies to light, some of which were not found in previous research. Our analyses revealed differences in the use of these quantity recognition strategies between MD and TD students. Our study illustrates the power of ET for investigating students’ quantity recognition strategies and the potential of ET to support MD students. }, year = {2019} } @inproceedings{Fan1360454, author = {Fan, Han and Hernandez Bennetts, Victor and Schaffernicht, Erik and Lilienthal, Achim J.}, booktitle = {18th ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) : }, institution = {Örebro University, School of Science and Technology}, eid = {151773}, title = {Semi-supervised Gas Detection Using an Ensemble of One-class Classifiers}, DOI = {10.1109/ISOEN.2019.8823148}, keywords = {Metal oxide semiconductor sensor, electronic nose, gas detection, gas sensing, open sampling systems}, abstract = {Detecting chemical compounds using electronic noses is important in many gas sensing related applications. Existing gas detection methods typically use prior knowledge of the target analytes. However, in some scenarios, the analytes to be detected are not fully known in advance, and preparing a dedicated model is not possible. To address this issue, we propose a gas detection approach using an ensemble of one-class classifiers. The proposed approach is initialized by learning a Mahalanobis-based and a Gaussian based model using clean air only. During the sampling process, the presence of chemicals is detected by the initialized system, which allows to learn a one-class nearest neighbourhood model without supervision. From then on the gas detection considers the predictions of the three one-class models. The proposed approach is validated with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an open environment. }, year = {2019} } @article{Burgues1284132, author = {Burgu{\’e;}s, Javier and Hernandez Bennetts, Victor and Lilienthal, Achim J. and Marco, Santiago}, institution = {Örebro University, School of Science and Technology}, institution = {Institute for Bioengineering of Catalonia (IBEC),The Barcelona Institute of Science and Technology, Baldiri Reixac, Barcelona, Spain; Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Barcelona, Spain}, institution = {Institute for Bioengineering of Catalonia (IBEC),The Barcelona Institute of Science and Technology, Baldiri Reixac, Barcelona, Spain; Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Barcelona, Spain}, journal = {Sensors}, note = {Funding Agency:Spanish MINECO  BES-2015-071698  TEC2014-59229-R}, number = {3}, eid = {478}, title = {Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping}, volume = {19}, DOI = {10.3390/s19030478}, keywords = {Robotics, signal processing, electronics, gas source localization, gas distribution mapping; gas sensors, drone, UAV, MOX sensor, quadcopter}, abstract = {This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up to two in situ metal oxide semiconductor (MOX) gas sensors. Due to its small form-factor, the SNAV is not a hazard for humans, enabling its use in public areas or inside buildings. It can autonomously carry out gas sensing missions of hazardous environments inaccessible to terrestrial robots and bigger drones, for example searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion. The first contribution of this work is assessing the impact of the nano-propellers on the MOX sensor signals at different distances to a gas source. A second contribution is adapting the ‘bout’ detection algorithm, proposed by Schmuker et al. (2016) to extract specific features from the derivative of the MOX sensor response, for real-time operation. The third and main contribution is the experimental validation of the SNAV for gas source localization (GSL) and mapping in a large indoor environment (160 m2) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. Two GSL strategies are compared, one based on the instantaneous gas sensor response and the other one based on the bout frequency. From the measurements collected (in motion) along a predefined sweeping path we built (in less than 3 min) a 3D map of the gas distribution and identified the most likely source location. Using the bout frequency yielded on average a higher localization accuracy than using the instantaneous gas sensor response (1.38 m versus 2.05 m error), however accurate tuning of an additional parameter (the noise threshold) is required in the former case. The main conclusion of this paper is that a nano-drone has the potential to perform gas sensing tasks in complex environments. }, year = {2019} } @article{Mielle1342185, author = {Mielle, Malcolm and Magnusson, Martin and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Robotics}, note = {Funding Agency:EU  ICT-26-2016 732737  ICT-23-2014 645101}, number = {2}, eid = {40}, publisher = {MDPI}, title = {The Auto-Complete Graph : Merging and Mutual Correction of Sensor and Prior Maps for SLAM}, volume = {8}, DOI = {10.3390/robotics8020040}, keywords = {SLAM, prior map, emergency map, layout map, graph-based SLAM, navigation, search and rescue}, abstract = {Simultaneous Localization And Mapping (SLAM) usually assumes the robot starts without knowledge of the environment. While prior information, such as emergency maps or layout maps, is often available, integration is not trivial since such maps are often out of date and have uncertainty in local scale. Integration of prior map information is further complicated by sensor noise, drift in the measurements, and incorrect scan registrations in the sensor map. We present the Auto-Complete Graph (ACG), a graph-based SLAM method merging elements of sensor and prior maps into one consistent representation. After optimizing the ACG, the sensor map's errors are corrected thanks to the prior map, while the sensor map corrects the local scale inaccuracies in the prior map. We provide three datasets with associated prior maps: two recorded in campus environments, and one from a fireman training facility. Our method handled up to 40% of noise in odometry, was robust to varying levels of details between the prior and the sensor map, and could correct local scale errors of the prior. In field tests with ACG, users indicated points of interest directly on the prior before exploration. We did not record failures in reaching them. }, year = {2019} } @inproceedings{Vintr1391186, author = {Vintr, Tomas and Molina, Sergi and Senanayake, Ransalu and Broughton, George and Yan, Zhi and Ulrich, Jiri and Kucner, Tomasz P. and Swaminathan, Chittaranjan Srinivas and Majer, Filip and Stachova, Maria and Lilienthal, Achim J. and Krajnik, Tomas}, booktitle = {2019 European Conference on Mobile Robots (ECMR) : }, institution = {Örebro University, School of Science and Technology}, institution = {Artificial Intelligence Center, Czech Technical University}, institution = {Lincoln Centre for Autonomous Systems (L-CAS), University of Lincoln}, institution = {Stanford University}, institution = {Artificial Intelligence Center, Czech Technical University}, institution = {Distributed Artificial Intelligence and Knowledge Laboratory (CIAD), University of Technology of Belfort-Montbeliard (UTBM), France}, institution = {Artificial Intelligence Center, Czech Technical University}, institution = {Artificial Intelligence Center, Czech Technical University}, institution = {University of Matej Bel, Banska Bystrica, Slovakia}, institution = {Artificial Intelligence Center, Czech Technical University}, note = {Funding Agencies:CSF project  17-27006Y STRoLLCTU IGA grant  SGS16/235/OHK3/3T/13 FR-8J18FR018PHC Barrande programme  40682ZH (3L4AV)CZ grant  CZ.02.1.01/0.0/0.0/16 019/0000765}, eid = {8870909}, title = {Time-varying Pedestrian Flow Models for Service Robots}, DOI = {10.1109/ECMR.2019.8870909}, keywords = {Long-Term Operation, Flow Models, Spatio-Temporal Models, Human Motion Prediction}, abstract = {We present a human-centric spatio-temporal model for service robots operating in densely populated environments for long time periods. The method integrates observations of pedestrians performed by a mobile robot at different locations and times into a memory efficient model, that represents the spatial layout of natural pedestrian flows and how they change over time. To represent temporal variations of the observed flows, our method does not model the time in a linear fashion, but by several dimensions wrapped into themselves. This representation of time can capture long-term (i.e. days to weeks) periodic patterns of peoples’ routines and habits. Knowledge of these patterns allows making long-term predictions of future human presence and walking directions, which can support mobile robot navigation in human-populated environments. Using datasets gathered by a robot for several weeks, we compare the model to state-of-the-art methods for pedestrian flow modelling. }, ISBN = {978-1-7281-3605-9}, year = {2019} } @article{Fan1287969, author = {Fan, Han and Hernandez Bennetts, Victor and Schaffernicht, Erik and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Sensors}, note = {Funding Agency:European Commission  645101}, number = {3}, eid = {E685}, title = {Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose}, volume = {19}, DOI = {10.3390/s19030685}, keywords = {Emergency response, gas discrimination, gas distribution mapping, mobile robotics olfaction, search and rescue robot, unsupervised learning}, abstract = {Emergency personnel, such as firefighters, bomb technicians, and urban search and rescue specialists, can be exposed to a variety of extreme hazards during the response to natural and human-made disasters. In many of these scenarios, a risk factor is the presence of hazardous airborne chemicals. The recent and rapid advances in robotics and sensor technologies allow emergency responders to deal with such hazards from relatively safe distances. Mobile robots with gas-sensing capabilities allow to convey useful information such as the possible source positions of different chemicals in the emergency area. However, common gas sampling procedures for laboratory use are not applicable due to the complexity of the environment and the need for fast deployment and analysis. In addition, conventional gas identification approaches, based on supervised learning, cannot handle situations when the number and identities of the present chemicals are unknown. For the purpose of emergency response, all the information concluded from the gas detection events during the robot exploration should be delivered in real time. To address these challenges, we developed an online gas-sensing system using an electronic nose. Our system can automatically perform unsupervised learning and update the discrimination model as the robot is exploring a given environment. The online gas discrimination results are further integrated with geometrical information to derive a multi-compound gas spatial distribution map. The proposed system is deployed on a robot built to operate in harsh environments for supporting fire brigades, and is validated in several different real-world experiments of discriminating and mapping multiple chemical compounds in an indoor open environment. Our results show that the proposed system achieves high accuracy in gas discrimination in an online, unsupervised, and computationally efficient manner. The subsequently created gas distribution maps accurately indicate the presence of different chemicals in the environment, which is of practical significance for emergency response. }, year = {2019} } @inproceedings{Palm1391189, author = {Palm, Rainer and Lilienthal, Achim J.}, booktitle = {Proceedings of the 11th International Joint Conference on Computational Intelligence : Volume 1 (FCTA)}, institution = {Örebro University, School of Science and Technology}, note = {Funding Agency:AIR-project, Action and Intention Recognition in Human Interaction with Autonomous Systems}, pages = {296--305}, title = {Uncertainty and Fuzzy Modeling in Human-Robot Navigation}, DOI = {10.5220/0008344902960305}, keywords = {Human-robot Interaction, Navigation, Fuzzy Modeling, Gaussian Noise}, abstract = {The interaction between humans and mobile robots in shared areas requires a high level of safety especially at the crossings of the trajectories of humans and robots. We discuss the intersection calculation and its fuzzy version in the context of human-robot navigation with respect to noise information. Based on known parameters of the Gaussian input distributions at the orientations of human and robot the parameters of the output distributions at the intersection are to be found by analytical and fuzzy calculation. Furthermore the inverse task is discussed where the parameters of the output distributions are given and the parameters of the input distributions are searched. For larger standard deviations of the orientation signals we suggest mixed Gaussian models as approximation of nonlinear distributions. }, ISBN = {978-989-758-384-1}, year = {2019} } @article{Mielle1342184, author = {Mielle, Malcolm and Magnusson, Martin and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Robotics}, note = {Funding Agency:EU  ICT-26-2016 732737}, number = {2}, eid = {43}, title = {URSIM : Unique Regions for Sketch Map Interpretation and Matching}, volume = {8}, DOI = {10.3390/robotics8020043}, keywords = {Map matching, sketch, human-robot interaction, interface, graph matching, segmentation}, abstract = {We present a method for matching sketch maps to a corresponding metric map, with the aim of later using the sketch as an intuitive interface for human-robot interactions. While sketch maps are not metrically accurate and many details, which are deemed unnecessary, are omitted, they represent the topology of the environment well and are typically accurate at key locations. Thus, for sketch map interpretation and matching, one cannot only rely on metric information. Our matching method first finds the most distinguishable, or unique, regions of two maps. The topology of the maps, the positions of the unique regions, and the size of all regions are used to build region descriptors. Finally, a sequential graph matching algorithm uses the region descriptors to find correspondences between regions of the sketch and metric maps. Our method obtained higher accuracy than both a state-of-the-art matching method for inaccurate map matching, and our previous work on the subject. The state of the art was unable to match sketch maps while our method performed only 10% worse than a human expert. }, year = {2019} } @inproceedings{Fan1284105, author = {Fan, Hongqi and Lu, Dawei and Kucner, Tomasz Piotr and Magnusson, Martin and Lilienthal, Achim}, booktitle = {Proceedings of 21st International Conference on Information Fusion (FUSION) : }, institution = {Örebro University, School of Science and Technology}, institution = {National University of Defense Technology, Changsa, P. R. China}, institution = {National University of Defense Technology, Changsa, P. R. China}, pages = {2400--2406}, title = {2D Spatial Keystone Transform for Sub-Pixel Motion Extraction from Noisy Occupancy Grid Map}, DOI = {10.23919/ICIF.2018.8455274}, keywords = {robotics, occupancy grid map, motion extraction, keystone transform, 2DS-KST, sub-pixel}, abstract = {In this paper, we propose a novel sub-pixel motion extraction method, called as Two Dimensional Spatial Keystone Transform (2DS-KST), for the motion detection and estimation from successive noisy Occupancy Grid Maps (OGMs). It extends the KST in radar imaging or motion compensation to 2D real spatial case, based on multiple hypotheses about possible directions of moving obstacles. Simulation results show that 2DS-KST has a good performance on the extraction of sub-pixel motions in very noisy environment, especially for those slowly moving obstacles. }, ISBN = {978-0-9964527-6-2}, ISBN = {978-1-5386-4330-3}, year = {2018} } @article{Burgues1284131, author = {Burgu{\’e;}s, Javier and Hernandez Bennetts, Victor and Lilienthal, Achim and Marco, Santiago}, institution = {Örebro University, School of Science and Technology}, institution = {Department of Electronic and Biomedical Engineering, Universitat de Barcelona, Barcelona, Spain; Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain}, institution = {Department of Electronic and Biomedical Engineering, Universitat de Barcelona, Barcelona, Spain; Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain}, journal = {Proceedings}, number = {13}, eid = {911}, title = {3D Gas Distribution with and without Artificial Airflow : An Experimental Study with a Grid of Metal Oxide Semiconductor Gas Sensors}, volume = {2}, DOI = {10.3390/proceedings2130911}, keywords = {MOX, metal oxide, flow visualization, gas sensors, gas distribution mapping, sensor grid, 3D, gas source localization, indoor}, abstract = {Gas distribution modelling can provide potentially life-saving information when assessing the hazards of gaseous emissions and for localization of explosives, toxic or flammable chemicals. In this work, we deployed a three-dimensional (3D) grid of metal oxide semiconductor (MOX) gas sensors deployed in an office room, which allows for novel insights about the complex patterns of indoor gas dispersal. 12 independent experiments were carried out to better understand dispersion patters of a single gas source placed at different locations of the room, including variations in height, release rate and air flow profiles. This dataset is denser and richer than what is currently available, i.e., 2D datasets in wind tunnels. We make it publicly available to enable the community to develop, validate, and compare new approaches related to gas sensing in complex environments. }, year = {2018} } @article{Fan1167983, author = {Fan, Han and Hernandez Bennetts, Victor and Schaffernicht, Erik and Lilienthal, Achim}, institution = {Örebro University, School of Science and Technology}, journal = {Sensors and actuators. B, Chemical}, pages = {183--203}, title = {A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments}, volume = {259}, DOI = {10.1016/j.snb.2017.10.063}, keywords = {Gas discrimination, environmental monitoring, metal oxide sensors, cluster analysis, unsupervised learning}, abstract = {Gas discrimination in open and uncontrolled environments based on smart low-cost electro-chemical sensor arrays (e-noses) is of great interest in several applications, such as exploration of hazardous areas, environmental monitoring, and industrial surveillance. Gas discrimination for e-noses is usually based on supervised pattern recognition techniques. However, the difficulty and high cost of obtaining extensive and representative labeled training data limits the applicability of supervised learning. Thus, to deal with the lack of information regarding target substances and unknown interferents, unsupervised gas discrimination is an advantageous solution. In this work, we present a cluster-based approach that can infer the number of different chemical compounds, and provide a probabilistic representation of the class labels for the acquired measurements in a given environment. Our approach is validated with the samples collected in indoor and outdoor environments using a mobile robot equipped with an array of commercial metal oxide sensors. Additional validation is carried out using a multi-compound data set collected with stationary sensor arrays inside a wind tunnel under various airflow conditions. The results show that accurate class separation can be achieved with a low sensitivity to the selection of the only free parameter, namely the neighborhood size, which is used for density estimation in the clustering process. }, year = {2018} } @article{Fan1172125, author = {Fan, Hongqi and Kucner, Tomasz Piotr and Magnusson, Martin and Li, Tiancheng and Lilienthal, Achim}, institution = {Örebro University, School of Science and Technology}, institution = {National Laboratory of Science and Technology on Automatic Target Recognition, National University of Defense Technology, Changsha, China}, institution = {School of Sciences, University of Salamanca, Salamanca, Spain}, journal = {IEEE transactions on intelligent transportation systems (Print)}, note = {Funding Agencies:EU Project SPENCER  600877 Marie Sklodowska-Curie Individual Fellowship  709267 National Twelfth Five-Year Plan for Science and Technology Support of China  2014BAK12B03 }, number = {9}, pages = {2977--2993}, title = {A Dual PHD Filter for Effective Occupancy Filtering in a Highly Dynamic Environment}, volume = {19}, DOI = {10.1109/TITS.2017.2770152}, keywords = {Mobile robot, occupancy filtering, PHD filter, BOF, particle filter, random finite set}, abstract = {Environment monitoring remains a major challenge for mobile robots, especially in densely cluttered or highly populated dynamic environments, where uncertainties originated from environment and sensor significantly challenge the robot's perception. This paper proposes an effective occupancy filtering method called the dual probability hypothesis density (DPHD) filter, which models uncertain phenomena, such as births, deaths, occlusions, false alarms, and miss detections, by using random finite sets. The key insight of our method lies in the connection of the idea of dynamic occupancy with the concepts of the phase space density in gas kinetic and the PHD in multiple target tracking. By modeling the environment as a mixture of static and dynamic parts, the DPHD filter separates the dynamic part from the static one with a unified filtering process, but has a higher computational efficiency than existing Bayesian Occupancy Filters (BOFs). Moreover, an adaptive newborn function and a detection model considering occlusions are proposed to improve the filtering efficiency further. Finally, a hybrid particle implementation of the DPHD filter is proposed, which uses a box particle filter with constant discrete states and an ordinary particle filter with a time-varying number of particles in a continuous state space to process the static part and the dynamic part, respectively. This filter has a linear complexity with respect to the number of grid cells occupied by dynamic obstacles. Real-world experiments on data collected by a lidar at a busy roundabout demonstrate that our approach can handle monitoring of a highly dynamic environment in real time. }, year = {2018} } @inproceedings{Mielle1237531, author = {Mielle, Malcolm and Magnusson, Martin and Lilienthal, Achim J.}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, pages = {4993--4999}, title = {A method to segment maps from different modalities using free space layout MAORIS : map of ripples segmentation}, DOI = {10.1109/ICRA.2018.8461128}, keywords = {map segmentation, free space, layout}, abstract = {How to divide floor plans or navigation maps into semantic representations, such as rooms and corridors, is an important research question in fields such as human-robot interaction, place categorization, or semantic mapping. While most works focus on segmenting robot built maps, those are not the only types of map a robot, or its user, can use. We present a method for segmenting maps from different modalities, focusing on robot built maps and hand-drawn sketch maps, and show better results than state of the art for both types. Our method segments the map by doing a convolution between the distance image of the map and a circular kernel, and grouping pixels of the same value. Segmentation is done by detecting ripple-like patterns where pixel values vary quickly, and merging neighboring regions with similar values. We identify a flaw in the segmentation evaluation metric used in recent works and propose a metric based on Matthews correlation coefficient (MCC). We compare our results to ground-truth segmentations of maps from a publicly available dataset, on which we obtain a better MCC than the state of the art with 0.98 compared to 0.65 for a recent Voronoi-based segmentation method and 0.70 for the DuDe segmentation method. We also provide a dataset of sketches of an indoor environment, with two possible sets of ground truth segmentations, on which our method obtains an MCC of 0.56 against 0.28 for the Voronoi-based segmentation method and 0.30 for DuDe. }, year = {2018} } @inproceedings{Canelhas1232362, author = {Canelhas, Daniel Ricão and Stoyanov, Todor and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), : }, institution = {Örebro University, School of Science and Technology}, institution = {Univrses AB, Strängnäs, Sweden}, pages = {6337--6343}, title = {A Survey of Voxel Interpolation Methods and an Evaluation of Their Impact on Volumetric Map-Based Visual Odometry}, keywords = {Voxels, Compression, Interpolation, TSDF, Visual Odometry}, abstract = {Voxel volumes are simple to implement and lend themselves to many of the tools and algorithms available for 2D images. However, the additional dimension of voxels may be costly to manage in memory when mapping large spaces at high resolutions. While lowering the resolution and using interpolation is common work-around, in the literature we often find that authors either use trilinear interpolation or nearest neighbors and rarely any of the intermediate options. This paper presents a survey of geometric interpolation methods for voxel-based map representations. In particular we study the truncated signed distance field (TSDF) and the impact of using fewer than 8 samples to perform interpolation within a depth-camera pose tracking and mapping scenario. We find that lowering the number of samples fetched to perform the interpolation results in performance similar to the commonly used trilinear interpolation method, but leads to higher framerates. We also report that lower bit-depth generally leads to performance degradation, though not as much as may be expected, with voxels containing as few as 3 bits sometimes resulting in adequate estimation of camera trajectories. }, year = {2018} } @inproceedings{Chadalavada1270176, author = {Chadalavada, Ravi Teja and Andreasson, Henrik and Schindler, Maike and Palm, Rainer and Lilienthal, Achim}, booktitle = {Advances in Manufacturing Technology XXXII : Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden}, institution = {Örebro University, School of Science and Technology}, pages = {253--258}, title = {Accessing your navigation plans! Human-Robot Intention Transfer using Eye-Tracking Glasses}, series = {Advances in Transdisciplinary Engineering}, number = {8}, DOI = {10.3233/978-1-61499-902-7-253}, keywords = {Human-Robot Interaction (HRI), Eye-tracking, Eye-Tracking Glasses, Navigation Intent, Implicit Intention Transference, Obstacle avoidance.}, abstract = {Robots in human co-habited environments need human-aware task and motion planning, ideally responding to people’s motion intentions as soon as they can be inferred from human cues. Eye gaze can convey information about intentions beyond trajectory and head pose of a person. Hence, we propose eye-tracking glasses as safety equipment in industrial environments shared by humans and robots. This paper investigates the possibility of human-to-robot implicit intention transference solely from eye gaze data.  We present experiments in which humans wearing eye-tracking glasses encountered a small forklift truck under various conditions. We evaluate how the observed eye gaze patterns of the participants related to their navigation decisions. Our analysis shows that people primarily gazed on that side of the robot they ultimately decided to pass by. We discuss implications of these results and relate to a control approach that uses human eye gaze for early obstacle avoidance. }, ISBN = {978-1-61499-901-0}, ISBN = {978-1-61499-902-7}, year = {2018} } @inproceedings{Swaminathan1262737, author = {Swaminathan, Chittaranjan Srinivas and Kucner, Tomasz Piotr and Magnusson, Martin and Palmieri, Luigi and Lilienthal, Achim}, booktitle = {2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, institution = {Robert Bosch, GmbH Corporate Research, Germany}, pages = {7403--7409}, title = {Down the CLiFF : Flow-Aware Trajectory Planning under Motion Pattern Uncertainty}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2018.8593905}, keywords = {Trajectory, Robots, Planning, Cost function, Uncertainty, Veichle dynamics, Aerospace electronics}, abstract = {In this paper we address the problem of flow-aware trajectory planning in dynamic environments considering flow model uncertainty. Flow-aware planning aims to plan trajectories that adhere to existing flow motion patterns in the environment, with the goal to make robots more efficient, less intrusive and safer. We use a statistical model called CLiFF-map that can map flow patterns for both continuous media and discrete objects. We propose novel cost and biasing functions for an RRT* planning algorithm, which exploits all the information available in the CLiFF-map model, including uncertainties due to flow variability or partial observability. Qualitatively, a benefit of our approach is that it can also be tuned to yield trajectories with different qualities such as exploratory or cautious, depending on application requirements. Quantitatively, we demonstrate that our approach produces more flow-compliant trajectories, compared to two baselines. }, ISBN = {978-1-5386-8094-0}, ISBN = {978-1-5386-8095-7}, year = {2018} } @inproceedings{Lindner1284093, author = {Lindner, Helen Y and Lilienthal, Achim and Karlsson, Gunilla and Lundqvist, Lars-Olov}, booktitle = { : }, institution = {Örebro University, School of Health Sciences}, institution = {Örebro University, School of Science and Technology}, institution = {Adult rehabilitation centre, Region Örebro County, Örebro, Sweden}, institution = {University Health Care Research Centre}, title = {Eye gaze technology to gain access to cognitive processes in individuals with profound intellectual and physical disabilities (PIPD)}, keywords = {Eye-tracking, profound intellectual and physical disabilities}, abstract = {Objective: Individuals with profound intellectual and physical disabilities (PIPD) often cannot speak for themselves and do things for themselves. Their level of cognitive abilities is unclear. Eye gaze technology has the potential to gain access to cognitive processes and eventually enable communication among these individuals. Method: Six individuals with PIPD were given multiple sessions of eye gaze training (9-36 sessions) between February 17 to October 18. They used a screen eye-tracker (Tobii pc eye-mini) to control the objects/icons on the screen. An eye-gaze training program with different levels of activities was used to teach cause and effect, give appropriate response, explore the whole screen, target specific objects, choosing objects AND turn taking. }, year = {2018} } @inproceedings{Schindler1284102, author = {Schindler, Maike and Lilienthal, Achim}, booktitle = {Proceedings of Annual Meeting of the International Group for the Psychology of Mathematics Education (PME-42) : }, institution = {Örebro University, School of Science and Technology}, institution = {University of Cologne, Department of Special Education}, pages = {115--122}, publisher = {PME}, title = {Eye-Tracking For Studying Mathematical Difficulties : Also In Inclusive Settings}, volume = {4}, keywords = {Eye-tracking, Mathematical Difficulties}, abstract = {Eye-Tracking (ET) is a promising tool for mathematics education research. Interest is fue­led by recent theoretical and technical developments, and the potential to identify strategies students use in mathematical tasks. This makes ET in­teresting for studying students with mathematical difficulties (MD), also with a view on inclusive settings. We present a systematic analysis of the opportunities ET may hold for understanding strategies of students with MD. Based on an empirical study with 20 fifth graders (10 with MD), we illustrate that and why ET offers opportunities especially for students with MD and describe main advantages. We also identify limitations of think aloud protocols, using ET as validation method, and present characteristics of students’ strategies in tasks on quantity recognition in structured whole number representations. }, year = {2018} } @inproceedings{Palm1234103, author = {Palm, Rainer and Lilienthal, Achim}, booktitle = {WCCI 2018 : 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)}, institution = {Örebro University, School of Science and Technology}, pages = {827--834}, title = {Fuzzy logic and control in Human-Robot Systems : geometrical and kinematic considerations}, keywords = {Human-robot interaction, fuzzy control, obstacle avoidance, eye tracking}, abstract = {The interaction between humans and mobile robots in shared areas requires adequate control for both humans and robots.The online path planning of the robot depending on the estimated or intended movement of the person is crucial for the obstacle avoidance and close cooperation between them. The velocity obstacles method and its fuzzification optimizes the relationship between the velocities of a robot and a human agent during the interaction. In order to find the estimated intersection between robot and human in the case of positions/orientations disturbed by noise, analytical and fuzzified versions are presented. The orientation of a person is estimated by eye tracking, with the help of which the intersection area is calculated. Eye tracking leads to clusters of fixations that are condensed into cluster centers by fuzzy-time clustering to detect the intention and attention of humans. }, ISBN = {978-1-5090-6020-7}, year = {2018} } @inproceedings{Neumann1279608, author = {Neumann, Patrick P. and H{\"u}llmann, Dino and Krentel, Daniel and Kluge, Martin and Kohlhoff, Harald and Lilienthal, Achim}, booktitle = {Proceedings of the IEEE Sensors 2018 : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, note = {Funding Agency:German Federal Ministry for Economic Affairs and Energy (BMWi) within the ZIM program  KF2201091HM4}, title = {Gas Tomography Up In The Air!}, series = {Proceedings of IEEE Sensors}, DOI = {10.1109/ICSENS.2018.8630293}, keywords = {Aerial robot, gas tomography, plume, TDLAS}, abstract = {In this paper, we present an autonomous aerial robot to reconstruct tomographic 2D slices of gas plumes in outdoor environments. Our platform, the so-called Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS) combines a lightweight Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor with a 3-axis aerial stabilization gimbal for aiming on a versatile octocopter. The TDLAS sensor provides integral gas concentration measurements but no information regarding the distance traveled by the laser diode's beam or the distribution of the gas along the optical path. We complemented the set-up with a laser rangefinder and apply principles of Computed Tomography (CT) to create a model of the spatial gas distribution from these integral concentration measurements. To allow for a rudimentary ground truth evaluation of the applied gas tomography algorithm, we set up a unique outdoor test environment based on two 3D ultrasonic anemometers and a distributed array of 10 infrared gas transmitters. We present first results showing the 2D plume reconstruction capabilities of the system under realistic conditions. }, ISBN = {978-1-5386-4707-3}, year = {2018} } @inproceedings{Rudenko1284106, author = {Rudenko, Andrey and Palmieri, Luigi and Lilienthal, Achim and Arras, Kai O.}, booktitle = {2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, institution = {Bosch Corporate Research, Stuttgart, Germany}, institution = {Bosch Corporate Research, Stuttgart, Germany}, institution = {Bosch Corporate Research, Stuttgart, Germany}, pages = {3358--3364}, title = {Human Motion Prediction under Social Grouping Constraints}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2018.8594258}, keywords = {Human motion prediction, human robot interaction, social forces, human-aware planning}, abstract = {Accurate long-term prediction of human motion inpopulated spaces is an important but difficult task for mobile robots and intelligent vehicles. What makes this task challenging is that human motion is influenced by a large variety offactors including the person’s intention, the presence, attributes, actions, social relations and social norms of other surrounding agents, and the geometry and semantics of the environment. In this paper, we consider the problem of computing human motion predictions that account for such factors. We formulate the task as an MDP planning problem with stochastic policies and propose a weighted random walk algorithm in which each agent is locally influenced by social forces from other nearby agents. The novelty of this paper is that we incorporate social grouping information into the prediction process reflecting the soft formation constraints that groups typically impose to their members’ motion. We show that our method makes more accurate predictions than three state-of-the-art methods in terms of probabilistic and geometrical performance metrics. }, ISBN = {978-1-5386-8094-0}, ISBN = {978-1-5386-8095-7}, year = {2018} } @article{Almqvist1163065, author = {Almqvist, H{\aa}kan and Magnusson, Martin and Kucner, Tomasz Piotr and Lilienthal, Achim}, institution = {Örebro University, School of Science and Technology}, journal = {Journal of Field Robotics}, number = {5}, pages = {662--677}, title = {Learning to detect misaligned point clouds}, volume = {35}, DOI = {10.1002/rob.21768}, keywords = {perception, mapping, position estimation}, abstract = {Matching and merging overlapping point clouds is a common procedure in many applications, including mobile robotics, three-dimensional mapping, and object visualization. However, fully automatic point-cloud matching, without manual verification, is still not possible because no matching algorithms exist today that can provide any certain methods for detecting misaligned point clouds. In this article, we make a comparative evaluation of geometric consistency methods for classifying aligned and nonaligned point-cloud pairs. We also propose a method that combines the results of the evaluated methods to further improve the classification of the point clouds. We compare a range of methods on two data sets from different environments related to mobile robotics and mapping. The results show that methods based on a Normal Distributions Transform representation of the point clouds perform best under the circumstances presented herein. }, year = {2018} } @article{Hullmann1279642, author = {H{\"u}llmann, Dino and Paul, Niels and Kohlhoff, Harald and Neumann, Patrick P. and Lilienthal, Achim}, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, journal = {Materials Today: Proceedings}, number = {13}, pages = {26703--26708}, title = {Measuring rotor speed for wind vector estimation on multirotor aircraft}, volume = {5}, DOI = {10.1016/j.matpr.2018.08.139}, keywords = {Rotor speed, tachometer, UAV, wind vector estimation}, abstract = {For several applications involving multirotor aircraft, it is crucial to know both the direction and speed of the ambient wind. In this paper, an approach to wind vector estimation based on an equilibrium of the principal forces acting on the aircraft is shown. As the thrust force generated by the rotors depends on their rotational speed, a sensor to measure this quantity is required. Two concepts for such a sensor are presented: One is based on tapping the signal carrying the speed setpoint for the motor controllers, the other one uses phototransistors placed underneath the rotor blades. While some complications were encountered with the first approach, the second yields accurate measurement data. This is shown by an experiment comparing the proposed speed sensor to a commercial non-contact tachometer. }, year = {2018} } @inproceedings{Schindler1284092, author = {Schindler, Maike and Lilienthal, Achim}, booktitle = {Dialogue between ontology and epistemology : New perspectives on theory and methodology in research on learning and education}, institution = {Örebro University, School of Science and Technology}, institution = {University of Cologne, Department of Special Education}, title = {Method and Theory in Their Interplay : Using Eye-Tracking for Investigating Mathematical Learning}, keywords = {Eye tracking, mathematics education research}, abstract = {In this presentation, we discuss the interplay between theory and one particular method of data collection: eye-tracking. Eye-tracking promises various opportunities for research, in particular for studying students’ attention, strategies, and even collaboration in so-called dual eye-tracking (DUET), and has gained increased interest as a research method. Still, researchers acknowledge that eye-tracking data interpretation is difficult and ambiguous and often needs to be complemented with other sources. In this talk, we discuss two studies in which we aimed for a triangulation of eye-tracking with other research methods. In both studies, ontological and epistemological questions are intertwined. }, year = {2018} } @inproceedings{Schindler1284095, author = {Schindler, Maike and Schindler, Florian and Lilienthal, Achim and Bader, Eveline}, booktitle = {Beiträge zum Mathematikunterricht 2018 : }, institution = {Örebro University, School of Science and Technology}, institution = {University of Cologne, Department of Special Education, Cologne, Germany}, institution = {Dortmund University, Dortmund, Germany}, institution = {University of Cologne, Cologne, Germany}, pages = {1591--1594}, title = {Vorgehensweisen bei der Anzahlerfassung am 100er Feld und 100er Rahmen : Eine Eye-Tracking Studie bei Kindern mit und ohne Rechenschwierigkeiten sowie sonderp{\"a}dagogischem Unterst{\"u}tzungsbedarf}, keywords = {Eye-tracking, quantity recognition, mathematical difficulties}, abstract = {Arbeitsmittel werden im Mathematikunterricht zum Aufbau von Zahl- und Operationsvorstellungen genutzt. Gerade für Kinder mit Schwierigkeiten im strukturierten Erfassen von Anzahlen undunzureichenden Zahl- und Operationsvorstellungen ist die Nutzung von Darstellungen zentral. Wie gehenjedoch Kinder mit Rechenschwierigkeiten bei der Anzahlerfassung in unterschiedlichen Darstellungenvor und inwiefern erfolgt ein Transfer zwischen strukturell ähnlichen Darstellungen? Die vorgestellteStudie untersucht Vorgehensweisen bei der Anzahlerfassung am 100er Feld und 100er Rahmen bei 20Kindern (davon 11 mit Rechenschwierigkeiten und z.T. sonderpädagogischem Unterstützungsbedarf) zu Beginn der fünften Klasse. Eye-Tracking ermöglicht dabei neue Erkenntnisse gerade bei Kindern, die Schwierigkeiten haben, ihre Vorgehensweisen zu beschreiben. Die Ergebnisse liefern Einblicke in mathematische Kompetenzen und Schwierigkeiten der Kinder sowie die Unterschiede in der Nutzung derbeiden Darstellungen. }, year = {2018} } @inproceedings{Wiedemann1139725, author = {Wiedemann, Thomas and Shutin, Dmitri and Hernandez Bennetts, Victor and Schaffernicht, Erik and Lilienthal, Achim}, booktitle = {2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017) : Proceedings}, institution = {Örebro University, School of Science and Technology}, institution = {Institute of Communications and Navigation, German Aerospace Center (DLR), Wessling, Germany}, institution = {Institute of Communications and Navigation, German Aerospace Center (DLR), Wessling, Germany}, pages = {122--124}, title = {Bayesian Gas Source Localization and Exploration with a Multi-Robot System Using Partial Differential Equation Based Modeling}, DOI = {10.1109/ISOEN.2017.7968884}, abstract = {Here we report on active water sampling devices forunderwater chemical sensing robots. Crayfish generate jetlikewater currents during food search by waving theflagella of their maxillipeds. The jets generated toward theirsides induce an inflow from the surroundings to the jets.Odor sample collection from the surroundings to theirolfactory organs is promoted by the generated inflow.Devices that model the jet discharge of crayfish have beendeveloped to investigate the effectiveness of the activechemical sampling. Experimental results are presented toconfirm that water samples are drawn to the chemicalsensors from the surroundings more rapidly by using theaxisymmetric flow field generated by the jet discharge thanby centrosymmetric flow field generated by simple watersuction. Results are also presented to show that there is atradeoff between the angular range of chemical samplecollection and the sample collection time. }, ISBN = {978-1-5090-2393-6}, ISBN = {978-1-5090-2392-9}, year = {2017} } @inproceedings{Neumann1179677, author = {Neumann, Patrick P. and Kohlhoff, Harald and H{\"u}llmann, Dino and Lilienthal, Achim and Kluge, Martin}, booktitle = {2017 IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, pages = {3910--3916}, title = {Bringing Mobile Robot Olfaction to the Next Dimension - UAV-based Remote Sensing of Gas Clouds and Source Localization}, series = {IEEE International Conference on Robotics and Automation}, DOI = {10.1109/ICRA.2017.7989450}, abstract = {This paper introduces a novel robotic platform for aerial remote gas sensing. Spectroscopic measurement methods for remote sensing of selected gases lend themselves for use on mini-copters, which offer a number of advantages for inspection and surveillance. No direct contact with the target gas is needed and thus the influence of the aerial platform on the measured gas plume can be kept to a minimum. This allows to overcome one of the major issues with gas-sensitive mini-copters. On the other hand, remote gas sensors, most prominently Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensors have been too bulky given the payload and energy restrictions of mini-copters. Here, we introduce and present the Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS), which combines a novel lightweight TDLAS sensor with a 3-axis aerial stabilization gimbal for aiming on a versatile hexacopter. The proposed system can be deployed in scenarios that cannot be addressed by currently available robots and thus constitutes a significant step forward for the field of Mobile Robot Olfaction (MRO). It enables tomographic reconstruction of gas plumes and a localization of gas sources. We also present first results showing the gas sensing and aiming capabilities under realistic conditions. }, ISBN = {978-1-5090-4633-1}, ISBN = {978-1-5090-4634-8}, year = {2017} } @article{Canelhas1175909, author = {Canelhas, Daniel R. and Schaffernicht, Erik and Stoyanov, Todor and Lilienthal, Achim and Davison, Andrew J.}, institution = {Örebro University, School of Science and Technology}, institution = {Department of Computing, Imperial College London, London, United Kingdom}, journal = {Robotics}, note = {Funding Agencies:European Commission  FP7-ICT-270350 H-ICT  732737 }, number = {3}, eid = {15}, publisher = {MDPI AG}, title = {Compressed Voxel-Based Mapping Using Unsupervised Learning}, volume = {6}, DOI = {10.3390/robotics6030015}, keywords = {3D mapping, TSDF, compression, dictionary learning, auto-encoder, denoising}, abstract = {In order to deal with the scaling problem of volumetric map representations, we propose spatially local methods for high-ratio compression of 3D maps, represented as truncated signed distance fields. We show that these compressed maps can be used as meaningful descriptors for selective decompression in scenarios relevant to robotic applications. As compression methods, we compare using PCA-derived low-dimensional bases to nonlinear auto-encoder networks. Selecting two application-oriented performance metrics, we evaluate the impact of different compression rates on reconstruction fidelity as well as to the task of map-aided ego-motion estimation. It is demonstrated that lossily reconstructed distance fields used as cost functions for ego-motion estimation can outperform the original maps in challenging scenarios from standard RGB-D (color plus depth) data sets due to the rejection of high-frequency noise content. }, year = {2017} } @inproceedings{Lilienthal1179666, author = {Lilienthal, Achim and Schindler, Maike}, booktitle = {Proceedings the 41th Conference of the International Group for the Psychology of Mathematics Education : }, institution = {Örebro University, School of Science and Technology}, institution = {University of Cologne, Cologne, Germany}, pages = {233--233}, publisher = {PME}, title = {Conducting Dual Portable Eye-Tracking in Mathematical Creativity Research}, volume = {1}, abstract = {Eye-tracking opens a window to the focus of attention of persons and promises to allow studying, e.g., creative processes “in vivo” (Nüssli, 2011). Most eye-tracking studies in mathematics education research focus on single students. However, following a Vygotskyan notion of learning and development where the individual and the social are dialectically interrelated, eye-tracking studies of collaborating persons appear beneficial for understanding students’ learning in their social facet. Dual eye-tracking, where two persons’ eye-movements are recorded and related to a joint coordinate-system, has hardly been used in mathematics education research. Especially dual portable eye-tracking (DPET) with goggles has hardly been explored due to its technical challenges compared to screen-based eye-tracking.In our interdisciplinary research project between mathematics education and computer science, we conduct DPET for studying collective mathematical creativity (Levenson, 2011) in a process perspective. DPET offers certain advantages, including to carry out paper and pen tasks in rather natural settings. Our research interests are: conducting DPET (technical), investigating opportunities and limitations of DPET for studying students’ collective creativity (methodological), and studying students’ collective creative problem solving (empirical).We carried out experiments with two pairs of university students wearing Pupil Pro eye tracking goggles. The students were given 45 min to solve a geometry problem in as many ways as possible. For our analysis, we first programmed MATLAB code to synchronize data from both participants’ goggles; resulting in a video displaying both students’ eye-movements projected on the task sheet, the sound recorded by the goggles, and additional information, e.g. pupil dilation. With these videos we expect to get insights into how students’ attentions meet, if students’ eye-movements follow one another, or verbal inputs, etc. We expect insights into promotive aspects in students’ collaboration: e.g., if pointing on the figure or intensive verbal communication promote students’ joint attention (cf. Nüssli, 2011). Finally, we think that the expected insights can contribute to existing research on collective mathematical creativity, especially to the question of how to enhance students’ creative collaboration. }, ISBN = {978-138-71-3608-7}, year = {2017} } @article{Kucner1070541, author = {Kucner, Tomasz Piotr and Magnusson, Martin and Schaffernicht, Erik and Hernandez Bennetts, Victor Manuel and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {IEEE Robotics and Automation Letters}, note = {Funding Agencies:EU project SPENCER  ICT-2011-600877 H2020-ICT project SmokeBot  645101 H2020-ICT project ILIAD  732737 }, number = {2}, pages = {1093--1100}, title = {Enabling Flow Awareness for Mobile Robots in Partially Observable Environments}, volume = {2}, DOI = {10.1109/LRA.2017.2660060}, keywords = {Field robots; mapping; social human-robot interaction}, abstract = {Understanding the environment is a key requirement for any autonomous robot operation. There is extensive research on mapping geometric structure and perceiving objects. However, the environment is also defined by the movement patterns in it. Information about human motion patterns can, e.g., lead to safer and socially more acceptable robot trajectories. Airflow pattern information allow to plan energy efficient paths for flying robots and improve gas distribution mapping. However, modelling the motion of objects (e.g., people) and flow of continuous media (e.g., air) is a challenging task. We present a probabilistic approach for general flow mapping, which can readily handle both of these examples. Moreover, we present and compare two data imputation methods allowing to build dense maps from sparsely distributed measurements. The methods are evaluated using two different data sets: one with pedestrian data and one with wind measurements. Our results show that it is possible to accurately represent multimodal, turbulent flow using a set of Gaussian Mixture Models, and also to reconstruct a dense representation based on sparsely distributed locations. }, year = {2017} } @inproceedings{Vuka1139675, author = {Vuka, Mikel and Schaffernicht, Erik and Schmuker, Michael and Hernandez Bennetts, Victor and Amigoni, Francesco and Lilienthal, Achim J}, booktitle = {2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017) : Proceedings}, institution = {Örebro University, School of Science and Technology}, institution = {Dipartitmento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy}, institution = {School of Computer Science, College Lane, University of Hertfordshire, Hatfield, United Kingdom}, institution = {Dipartitmento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy}, pages = {164--166}, title = {Exploration and Localization of a Gas Source with MOX Gas Sensorson a Mobile Robot : A Gaussian Regression Bout Amplitude Approach}, DOI = {10.1109/ISOEN.2017.7968898}, abstract = {Mobile robot olfaction systems combine gas sensorswith mobility provided by robots. They relief humansof dull, dirty and dangerous tasks in applications such assearch & rescue or environmental monitoring. We address gassource localization and especially the problem of minimizingexploration time of the robot, which is a key issue due toenergy constraints. We propose an active search approach forrobots equipped with MOX gas sensors and an anemometer,given an occupancy map. Events of rapid change in the MOXsensor signal (“bouts”) are used to estimate the distance to agas source. The wind direction guides a Gaussian regression,which interpolates distance estimates. The contributions of thispaper are two-fold. First, we extend previous work on gassource distance estimation with MOX sensors and propose amodification to cope better with turbulent conditions. Second,we introduce a novel active search gas source localizationalgorithm and validate it in a real-world environment. }, year = {2017} } @inproceedings{Schindler1179672, author = {Schindler, Maike and Lilienthal, Achim}, booktitle = {Proceedings of the 41st Conference of the International Group for the Psychology of Mathematics Education : }, institution = {Örebro University, School of Science and Technology}, institution = {University of Cologne, Cologne, Germany}, pages = {153--160}, publisher = {PME}, title = {Eye-Tracking and its Domain-Specific Interpretation : A Stimulated Recall Study on Eye Movements in Geometrical Tasks}, volume = {4}, abstract = {Eye-tracking offers various possibilities for mathematics education. Yet, even in suitably visually presented tasks, interpretation of eye-tracking data is non-trivial. A key reason is that the interpretation of eye-tracking data is context-sensitive. To reduce ambiguity and uncertainty, we studied the interpretation of eye movements in a specific domain: geometrical mathematical creativity tasks. We present results from a qualitative empirical study in which we analyzed a Stimulated Recall Interview where a student watched the eye-tracking overlaid video of his work on a task. Our results hint at how eye movements can be interpreted and show limitations and opportunities of eye tracking in the domain of mathematical geometry tasks and beyond. }, ISBN = {978-138-71-3613-1}, year = {2017} } @inproceedings{Schindler1179675, author = {Schindler, Maike and Lilienthal, Achim}, booktitle = {The 10th Mathematical Creativity and Giftedness International Conference : Proceedings}, institution = {Örebro University, School of Science and Technology}, institution = {University of Cologne, Cologne, Germany}, pages = {45--50}, publisher = {Department of Education, University of Cyprus}, title = {Eye-Tracking As A Tool For Investigating Mathematical Creativity}, keywords = {Mathematical Creativity, Eye-Tracking, Eye Movements, MSTs, geometry, proof}, abstract = {Mathematical creativity as a key ability in our increasingly automated and interconnected, high-technology based society and economy is increasingly in the focus of mathematics education research. The recent scientific discussion in this domain is shifting from a product view, on written solutions and drawings, to a process view, which aims to investigate the different stages of how students come up with creative ideas. The latter is, however, a challenge. In this theoretical-methodological paper, we present and discuss the opportunities that eye-tracking offers for studying creativity in a process view. We discuss in which way eye-tracking allows to obtain novel answers to the questions of how original ideas come up, how they evolve and what leads to the so-called Eureka!-moment. We focus on video-based eye tracking approaches, discuss pros and cons of screen-based and mobile eye tracking, and illustrate methods of data analysis and their benefits for research on mathematical creativity. }, ISBN = {978-9963-700-99-8}, year = {2017} } @article{Monroy1179652, author = {Monroy, Javier and Hernandez Bennetts, Victor and Fan, Han and Lilienthal, Achim and Gonzalez-Jimenez, Javier}, institution = {Örebro University, School of Science and Technology}, institution = {Machine Perception and Intelligent Robotics group (MAPIR), Instituto de Investigación Biomedica de Malaga (IBIMA), Universidad de Malaga, Malaga, Spain}, institution = {Machine Perception and Intelligent Robotics group (MAPIR), Instituto de Investigación Biomedica de Malaga (IBIMA), Universidad de Malaga, Malaga, Spain}, journal = {Sensors}, note = {Funding Agencies:Spanish GovermentAndalucia Goverment}, number = {7}, pages = {1479--1494}, publisher = {MPDI AG}, title = {GADEN : A 3D Gas Dispersion Simulator for Mobile Robot Olfaction in Realistic Environments}, volume = {17}, DOI = {10.3390/s17071479}, keywords = {Gas dispersal, robotics olfaction, gas sensing, mobile robotics, Robot Operating System (ROS)}, abstract = {This work presents a simulation framework developed under the widely used Robot Operating System (ROS) to enable the validation of robotics systems and gas sensing algorithms under realistic environments. The framework is rooted in the principles of computational fluid dynamics and filament dispersion theory, modeling wind flow and gas dispersion in 3D real-world scenarios (i.e., accounting for walls, furniture, etc.). Moreover, it integrates the simulation of different environmental sensors, such as metal oxide gas sensors, photo ionization detectors, or anemometers. We illustrate the potential and applicability of the proposed tool by presenting a simulation case in a complex and realistic office-like environment where gas leaks of different chemicals occur simultaneously. Furthermore, we accomplish quantitative and qualitative validation by comparing our simulated results against real-world data recorded inside a wind tunnel where methane was released under different wind flow profiles. Based on these results, we conclude that our simulation framework can provide a good approximation to real world measurements when advective airflows are present in the environment. }, year = {2017} } @inproceedings{Fan1138648, author = {Fan, Han and Arain, Muhammad Asif and Hernandez Bennetts, Victor and Schaffernicht, Erik and Lilienthal, Achim J.}, booktitle = {2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings : }, institution = {Örebro University, School of Science and Technology}, eid = {17013581}, title = {Improving Gas Dispersal Simulation For Mobile Robot Olfaction : Using Robot-Created Occupancy Maps And Remote Gas Sensors In The Simulation Loop}, DOI = {10.1109/ISOEN.2017.7968874}, abstract = {Mobile robot platforms equipped with olfaction systems have been used in many gas sensing applications. However, in-field validation of mobile robot olfaction systems is time consuming, expensive, cumbersome and lacks repeatability. In order to address these issues, simulation tools are used. However, the available mobile robot olfaction simulations lack models for remote gas sensors, and the possibility to import geometrical representations of actual real-world environments in a convenient way. In this paper, we describe extensions to an open-source CFD-based filament gas dispersal simulator. These improvements arrow to use robot-created occupancy maps and offer remote sensing capabilities in the simulation loop. We demonstrate the novel features in an example application: we created a 3D map a complex indoor environment, and performed a gas emission monitoring task with a Tunable Diode Laser Absorption Spectroscopy based remote gas sensor in a simulated version of the environment. }, ISBN = {978-1-5090-2392-9}, ISBN = {978-1-5090-2393-6}, year = {2017} } @inproceedings{Arain1139140, author = {Arain, Muhammad Asif and Fan, Han and Hernandez Bennetts, Victor and Schaffernicht, Erik and Lilienthal, Achim J.}, booktitle = {2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings : }, institution = {Örebro University, School of Science and Technology}, eid = {7968895}, title = {Improving Gas Tomography With Mobile Robots : An Evaluation of Sensing Geometries in Complex Environments}, DOI = {10.1109/ISOEN.2017.7968895}, abstract = {An accurate model of gas emissions is of high importance in several real-world applications related to monitoring and surveillance. Gas tomography is a non-intrusive optical method to estimate the spatial distribution of gas concentrations using remote sensors. The choice of sensing geometry, which is the arrangement of sensing positions to perform gas tomography, directly affects the reconstruction quality of the obtained gas distribution maps. In this paper, we present an investigation of criteria that allow to determine suitable sensing geometries for gas tomography. We consider an actuated remote gas sensor installed on a mobile robot, and evaluated a large number of sensing configurations. Experiments in complex settings were conducted using a state-of-the-art CFD-based filament gas dispersal simulator. Our quantitative comparison yields preferred sensing geometries for sensor planning, which allows to better reconstruct gas distributions. }, ISBN = {978-1-5090-2392-9}, ISBN = {978-1-5090-2393-6}, year = {2017} } @inproceedings{Andreasson1159885, author = {Andreasson, Henrik and Adolfsson, Daniel and Stoyanov, Todor and Magnusson, Martin and Lilienthal, Achim}, booktitle = {2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, pages = {1389--1395}, title = {Incorporating Ego-motion Uncertainty Estimates in Range Data Registration}, series = {Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2017.8202318}, abstract = {Local scan registration approaches commonlyonly utilize ego-motion estimates (e.g. odometry) as aninitial pose guess in an iterative alignment procedure. Thispaper describes a new method to incorporate ego-motionestimates, including uncertainty, into the objective function of aregistration algorithm. The proposed approach is particularlysuited for feature-poor and self-similar environments,which typically present challenges to current state of theart registration algorithms. Experimental evaluation showssignificant improvements in accuracy when using data acquiredby Automatic Guided Vehicles (AGVs) in industrial productionand warehouse environments. }, ISBN = {978-1-5386-2682-5}, ISBN = {978-1-5386-2683-2}, year = {2017} } @inproceedings{Palmieri1070556, author = {Palmieri, Luigi and Kucner, Tomasz and Magnusson, Martin and Lilienthal, Achim J. and Arras, Kai}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA 2017) : }, institution = {Örebro University, School of Science and Technology}, institution = {Computer Science Department, University of Freiburg, Freiburg im Breisgau, Germany}, institution = {Bosch Corporate Research, Stuttgart, Germany}, pages = {6176--6181}, eid = {7989731}, title = {Kinodynamic Motion Planning on Gaussian Mixture Fields}, DOI = {10.1109/ICRA.2017.7989731}, abstract = {We present a mobile robot motion planning ap-proach under kinodynamic constraints that exploits learnedperception priors in the form of continuous Gaussian mixturefields. Our Gaussian mixture fields are statistical multi-modalmotion models of discrete objects or continuous media in theenvironment that encode e.g. the dynamics of air or pedestrianflows. We approach this task using a recently proposed circularlinear flow field map based on semi-wrapped GMMs whosemixture components guide sampling and rewiring in an RRT*algorithm using a steer function for non-holonomic mobilerobots. In our experiments with three alternative baselines,we show that this combination allows the planner to veryefficiently generate high-quality solutions in terms of pathsmoothness, path length as well as natural yet minimum controleffort motions through multi-modal representations of Gaussianmixture fields. }, year = {2017} } @inproceedings{Palm1179669, author = {Palm, Rainer and Lilienthal, Achim}, booktitle = {2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) : }, institution = {Örebro University, School of Science and Technology}, eid = {8015396}, title = {Long distance prediction and short distance control in Human-Robot Systems}, DOI = {10.1109/FUZZ-IEEE.2017.8015396}, abstract = {The study of the interaction between autonomous robots and human agents in common working areas is an emerging field of research. Main points thereby are human safety, system stability, performance and optimality of the whole interaction process. Two approaches to deal with human-robot interaction can be distinguished: Long distance prediction which requires the recognition of intentions of other agents, and short distance control which deals with actions and reactions between agents and mutual reactive control of their motions and behaviors. In this context obstacle avoidance plays a prominent role. In this paper long distance prediction is represented by the identification of human intentions to use specific lanes by using fuzzy time clustering of pedestrian tracks. Another issue is the extrapolation of parts of both human and robot trajectories in the presence of scattered/uncertain measurements to guarantee a collision-free robot motion. Short distance control is represented by obstacle avoidance between agents using the method of velocity obstacles and both analytical and fuzzy control methods. }, ISBN = {978-1-5090-6034-4}, ISBN = {978-1-5090-6035-1}, ISBN = {978-1-5090-6033-7}, year = {2017} } @inproceedings{Xing1176939, author = {Xing, Yuxin and Vincent, Timothy A. and Cole, Marina and Gardner, Julian W. and Fan, Han and Hernandez Bennetts, Victor and Schaffernicht, Erik and Lilienthal, Achim}, booktitle = {IEEE SENSORS 2017 : Conference Proceedings}, institution = {Örebro University, School of Science and Technology}, institution = {School of Engineering, University of Warwick, Coventry, UK}, institution = {School of Engineering, University of Warwick, Coventry, UK}, institution = {School of Engineering, University of Warwick, Coventry, UK}, institution = {School of Engineering, University of Warwick, Coventry, UK}, pages = {1691--1693}, title = {Mobile robot multi-sensor unit for unsupervised gas discrimination in uncontrolled environments}, series = {Proceedings of IEEE Sensors}, DOI = {10.1109/ICSENS.2017.8234440}, keywords = {Gas sensor, mobile robot, MOX, open sampling system, gas discrimination}, abstract = {In this work we present a novel multi-sensor unit to detect and discriminate unknown gases in uncontrolled environments. The unit includes three metal oxide (MOX) sensors with CMOS micro heaters, a plasmonic enhanced non-dispersive infra-red (NDIR) sensor, a commercial temperature humidity sensor, and a flow sensor. The proposed sensing unit was evaluated with plumes of gases (propanol, ethanol and acetone) in both, a laboratory setup on a gas testing bench and on-board a mobile robot operating in an indoor workshop. It offers significantly improved performance compared to commercial systems, in terms of power consumption, response time and physical size. We verified the ability to discriminate gases in an unsupervised manner, with data collected on the robot and high accuracy was obtained in the classification of propanol versus acetone (96%), and ethanol versus acetone (90%). }, ISBN = {978-1-5090-1012-7}, ISBN = {978-1-5090-1013-4}, year = {2017} } @inproceedings{Schaffernicht1170470, author = {Schaffernicht, Erik and Hernandez Bennetts, Victor and Lilienthal, Achim}, booktitle = {2017 IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, pages = {2659--2665}, title = {Mobile robots for learning spatio-temporal interpolation models in sensor networks - The Echo State map approach : The Echo State map approach}, DOI = {10.1109/ICRA.2017.7989310}, keywords = {Gaussian processes, learning (artificial intelligence), mobile robots, neurocontrollers, wireless sensor networks, Gaussian process estimator, echo state map approach, gas concentration, mobile robots, particulate matter measurement, sensor networks, spatio-temporal interpolation model learning, temperature concentration, Foundries, Interpolation, Mobile robots, Robot sensing systems, Wireless sensor networks}, abstract = {Sensor networks have limited capabilities to model complex phenomena occuring between sensing nodes. Mobile robots can be used to close this gap and learn local interpolation models. In this paper, we utilize Echo State Networks in order to learn the calibration and interpolation model between sensor nodes using measurements collected by a mobile robot. The use of Echo State Networks allows to deal with temporal dependencies implicitly, while the spatial mapping with a Gaussian Process estimator exploits the fact that Echo State Networks learn linear combinations of complex temporal dynamics. The resulting Echo State Map elegantly combines spatial and temporal cues into a single representation. We showcase the method in the exposure modeling task of building dust distribution maps for foundries, a challenge which is of great interest to occupational health researchers. Results from simulated data and real world experiments highlight the potential of Echo State Maps. While we focus on particulate matter measurements, the method can be applied for any other environmental variables like temperature or gas concentration. }, year = {2017} } @inproceedings{Hullmann1279636, author = {H{\"u}llmann, Dino and Paul, Niels and Neumann, Patrick P. and Lilienthal, Achim}, booktitle = {34th Danubia-Adria Symposium on Advances in Experimental Mechanics : Book of proceedings}, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, pages = {75--77}, publisher = {EUT Edizioni Università di Trieste}, title = {Motor Speed Transfer Function for Wind Vector Estimation on Multirotor Aircraft}, keywords = {Anemometer, Multicopter, UAV, PWM, Wind}, abstract = {A set of equations is derived to estimate the 3D wind vector with a multirotor aircraft using the aircraft itself as a flying anemometer. Since the thrust component is required to compute the wind vector, the PWM signal controlling the motors of the aircraft is measured and a transfer function describing the relation between the PWM signal and the rotational speed of the motors is derived. }, URL = {https://www.openstarts.units.it/handle/10077/14834}, ISBN = {978-88-8303-863-1}, year = {2017} } @article{HernandezBennetts1078590, author = {Hernandez Bennetts, Victor and Kucner, Tomasz Piotr and Schaffernicht, Erik and Neumann, Patrick P. and Fan, Han and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany}, journal = {IEEE Robotics and Automation Letters}, note = {Funding Agency:H2020-ICT project SmokeBot  645101}, number = {2}, pages = {1117--1123}, title = {Probabilistic Air Flow Modelling Using Turbulent and Laminar Characteristics for Ground and Aerial Robots}, volume = {2}, DOI = {10.1109/LRA.2017.2661803}, keywords = {Aerial systems: perception and autonomy, environment monitoring and management, field robots, mapping}, abstract = {For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling<?brk?> algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback&ndash;Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts. }, year = {2017} } @inproceedings{Wiedemann1179662, author = {Wiedemann, Thomas and Manss, Christoph and Shutin, Dmitriy and Lilienthal, Achim and Karolj, Valentina and Viseras, Alberto}, booktitle = {2017 European Conference on Mobile Robots (ECMR) : }, institution = {Örebro University, School of Science and Technology}, institution = {Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Germany}, institution = {Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Germany}, institution = {Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Germany}, institution = {Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Germany}, institution = {Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Germany}, note = {Funding Agency:H2020-ICT by the European Commission  645101}, eid = {8098707}, title = {Probabilistic modeling of gas diffusion with partial differential equations for multi-robot exploration and gas source localization}, DOI = {10.1109/ECMR.2017.8098707}, keywords = {multi-agent exploration, gas source localization, mobile robot olfaction partial differential equation, factor graph, sparse Bayesian learning, message passing}, abstract = {Employing automated robots for sampling gas distributions and for localizing gas sources is beneficial since it avoids hazards for a human operator. This paper addresses the problem of exploring a gas diffusion process using a multi-agent system consisting of several mobile sensing robots. The diffusion process is modeled using a partial differential equation (PDE). It is assumed that the diffusion process is driven by only a few spatial sources at unknown locations with unknown intensity. The goal of the multi-robot exploration is thus to identify source parameters, in particular, their number, locations and magnitudes. Therefore, this paper develops a probabilistic approach towards PDE identification under sparsity constraint using factor graphs and a message passing algorithm. Moreover, the message passing schemes permits efficient distributed implementation of the algorithm. This brings significant advantages with respect to scalability, computational complexity and robustness of the proposed exploration algorithm. Based on the derived probabilistic model, an exploration strategy to guide the mobile agents in real time to more informative sampling locations is proposed. Hardware- in-the-loop experiments with real mobile robots show that the proposed exploration approach accelerates the identification of the source parameters and outperforms systematic sampling. }, ISBN = {978-1-5386-1096-1}, ISBN = {978-1-5386-1097-8}, year = {2017} } @inproceedings{Magnusson1151027, author = {Magnusson, Martin and Kucner, Tomasz Piotr and Gholami Shahbandi, Saeed and Andreasson, Henrik and Lilienthal, Achim}, booktitle = {2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, institution = {IS lab, Halmstad University, Halmstad, Sweden}, note = {Iliad Project: http://iliad-project.eu}, pages = {620--625}, title = {Semi-Supervised 3D Place Categorisation by Descriptor Clustering}, series = {Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2017.8202216}, abstract = {Place categorisation; i. e., learning to group perception data into categories based on appearance; typically uses supervised learning and either visual or 2D range data. This paper shows place categorisation from 3D data without any training phase. We show that, by leveraging the NDT histogram descriptor to compactly encode 3D point cloud appearance, in combination with standard clustering techniques, it is possible to classify public indoor data sets with accuracy comparable to, and sometimes better than, previous supervised training methods. We also demonstrate the effectiveness of this approach to outdoor data, with an added benefit of being able to hierarchically categorise places into sub-categories based on a user-selected threshold. This technique relieves users of providing relevant training data, and only requires them to adjust the sensitivity to the number of place categories, and provide a semantic label to each category after the process is completed. }, ISBN = {978-1-5386-2682-5}, ISBN = {978-1-5386-2683-2}, year = {2017} } @inproceedings{Mielle1155435, author = {Mielle, Malcolm and Magnusson, Martin and Andreasson, Henrik and Lilienthal, Achim J.}, booktitle = {2017 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR) : }, institution = {Örebro University, School of Science and Technology}, note = {Funding Agency:EU  ICT-23-2014645101}, pages = {35--40}, eid = {8088137}, title = {SLAM auto-complete : completing a robot map using an emergency map}, DOI = {10.1109/SSRR.2017.8088137}, keywords = {SLAM, robotics, graph, graph SLAM, emergency map, rescue, exploration, auto complete, SLAM, robotics, graph, graph SLAM, plan de secours, sauvetage, exploration, auto complete}, abstract = {In search and rescue missions, time is an important factor; fast navigation and quickly acquiring situation awareness might be matters of life and death. Hence, the use of robots in such scenarios has been restricted by the time needed to explore and build a map. One way to speed up exploration and mapping is to reason about unknown parts of the environment using prior information. While previous research on using external priors for robot mapping mainly focused on accurate maps or aerial images, such data are not always possible to get, especially indoor. We focus on emergency maps as priors for robot mapping since they are easy to get and already extensively used by firemen in rescue missions. However, those maps can be outdated, information might be missing, and the scales of rooms are typically not consistent. We have developed a formulation of graph-based SLAM that incorporates information from an emergency map. The graph-SLAM is optimized using a combination of robust kernels, fusing the emergency map and the robot map into one map, even when faced with scale inaccuracies and inexact start poses. We typically have more than 50% of wrong correspondences in the settings studied in this paper, and the method we propose correctly handles them. Experiments in an office environment show that we can handle up to 70% of wrong correspondences and still get the expected result. The robot can navigate and explore while taking into account places it has not yet seen. We demonstrate this in a test scenario and also show that the emergency map is enhanced by adding information not represented such as closed doors or new walls. }, ISBN = {978-1-5386-3923-8}, ISBN = {978-1-5386-3924-5}, year = {2017} } @article{Asadi1159790, author = {Asadi, Sahar and Fan, Han and Hernandez Bennetts, Victor and Lilienthal, Achim}, institution = {Örebro University, School of Science and Technology}, journal = {Robotics and Autonomous Systems}, note = {Funding Agency:EC  FP7-224318-DIADEM}, pages = {157--170}, title = {Time-dependent gas distribution modelling}, volume = {96}, DOI = {10.1016/j.robot.2017.05.012}, keywords = {Mobile robot olfaction, Statistical gas distribution modelling, Temporal sub-sampling, Time-dependent gas distribution modelling}, abstract = {Artificial olfaction can help to address pressing environmental problems due to unwanted gas emissions. Sensor networks and mobile robots equipped with gas sensors can be used for e.g. air pollution monitoring. Key in this context is the ability to derive truthful models of gas distribution from a set of sparse measurements. Most statistical gas distribution modelling methods assume that gas dispersion is a time constant random process. While this assumption approximately holds in some situations, it is necessary to model variations over time in order to enable applications of gas distribution modelling in a wider range of realistic scenarios. Time-invariant approaches cannot model well evolving gas plumes, for example, or major changes in gas dispersion due to a sudden change of the environmental conditions. This paper presents two approaches to gas distribution modelling, which introduce a time-dependency and a relation to a time-scale in generating the gas distribution model either by sub-sampling or by introducing a recency weight that relates measurement and prediction time. We evaluated these approaches in experiments performed in two real environments as well as on several simulated experiments. As expected, the comparison of different sub-sampling strategies revealed that more recent measurements are more informative to derive an estimate of the current gas distribution as long as a sufficient spatial coverage is given. Next, we compared a time-dependent gas distribution modelling approach (TD Kernel DM+V), which includes a recency weight, to the state-of-the-art gas distribution modelling approach (Kernel DM+V), which does not consider sampling times. The results indicate a consistent improvement in the prediction of unseen measurements, particularly in dynamic scenarios. Furthermore, this paper discusses the impact of meta-parameters in model selection and compares the performance of time-dependent GDM in different plume conditions. Finally, we investigated how to set the target time for which the model is created. The results indicate that TD Kernel DM+V performs best when the target time is set to the maximum sampling time in the test set. }, year = {2017} } @inproceedings{Mielle1151040, author = {Mielle, Malcolm and Magnusson, Martin and Andreasson, Henrik and Lilienthal, Achim}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, title = {Using emergency maps to add not yet explored places into SLAM}, keywords = {Search and Rescue Robots, SLAM, Mapping}, abstract = {While using robots in search and rescue missions would help ensure the safety of first responders, a key issue is the time needed by the robot to operate. Even though SLAM is faster and faster, it might still be too slow to enable the use of robots in critical situations. One way to speed up operation time is to use prior information. We aim at integrating emergency-maps into SLAM to complete the SLAM map with information about not yet explored part of the environment. By integrating prior information, we can speed up exploration time or provide valuable prior information for navigation, for example, in case of sensor blackout/failure. However, while extensively used by firemen in their operations, emergency maps are not easy to integrate in SLAM since they are often not up to date or with non consistent scales. The main challenge we are tackling is in dealing with the imperfect scale of the rough emergency maps and integrate it with the online SLAM map in addition to challenges due to incorrect matches between these two types of map. We developed a formulation of graph-based SLAM incorporating information from an emergency map into SLAM, and propose a novel optimization process adapted to this formulation. We extract corners from the emergency map and the SLAM map, in between which we find correspondences using a distance measure. We then build a graph representation associating information from the emergency map and the SLAM map. Corners in the emergency map, corners in the robot map, and robot poses are added as nodes in the graph, while odometry, corner observations, walls in the emergency map, and corner associations are added as edges. To conserve the topology of the emergency map, but correct its possible errors in scale, edges representing the emergency map's walls are given a covariance so that they are easy to extend or shrink but hard to rotate. Correspondences between corners represent a zero transformation for the optimization to match them as close as possible. The graph optimization is done by using a combination robust kernels. We first use the Huber kernel, to converge toward a good solution, followed by Dynamic Covariance Scaling, to handle the remaining errors. We demonstrate our system in an office environment. We run the SLAM online during the exploration. Using the map enhanced by information from the emergency map, the robot was able to plan the shortest path toward a place it has not yet explored. This capability can be a real asset in complex buildings where exploration can take up a long time. It can also reduce exploration time by avoiding exploration of dead-ends, or search of specific places since the robot knows where it is in the emergency map. }, year = {2017} } @inproceedings{Krug1064907, author = {Krug, Robert and Lilienthal, Achim J. and Kragic, Danica and Bekiroglu, Yasemin}, booktitle = {2016 IEEE International Conference on Robotics and Automation, ICRA 2016 : }, institution = {Örebro University, School of Science and Technology}, institution = {Centre for Autonomous Systems, Computer Vision and Active Perception Lab, CSC, KTH Stockholm, Stockholm, Sweden}, institution = {School of Mechanical Engineering, University of Birmingham, Birmingham, United Kingdom}, pages = {165--171}, publisher = {IEEE}, title = {Analytic Grasp Success Prediction with Tactile Feedback}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ICRA.2016.7487130}, abstract = {Predicting grasp success is useful for avoiding failures in many robotic applications. Based on reasoning in wrench space, we address the question of how well analytic grasp success prediction works if tactile feedback is incorporated. Tactile information can alleviate contact placement uncertainties and facilitates contact modeling. We introduce a wrench-based classifier and evaluate it on a large set of real grasps. The key finding of this work is that exploiting tactile information allows wrench-based reasoning to perform on a level with existing methods based on learning or simulation. Different from these methods, the suggested approach has no need for training data, requires little modeling effort and is computationally efficient. Furthermore, our method affords task generalization by considering the capabilities of the grasping device and expected disturbance forces/moments in a physically meaningful way. }, ISBN = {978-1-4673-8026-3}, year = {2016} } @article{Rituerto931985, author = {Rituerto, Alejandro and Andreasson, Henrik and Murillo, Ana C. and Lilienthal, Achim and Jesus Guerrero, Jose}, institution = {Örebro University, School of Science and Technology}, institution = {Instituto de Investigación en Ingeniería de Aragón, Deptartmento de Informática e Ingeniería de Sistemas, University of Zaragoza, Zaragoza, Spain}, institution = {Instituto de Investigación en Ingeniería de Aragón, Deptartmento de Informática e Ingeniería de Sistemas, University of Zaragoza, Zaragoza, Spain}, institution = {Instituto de Investigación en Ingeniería de Aragón, Deptartmento de Informática e Ingeniería de Sistemas, University of Zaragoza, Zaragoza, Spain}, journal = {Sensors}, note = {Funding Agencies:Spanish Government European Union DPI2015-65962-R}, number = {4}, eid = {493}, publisher = {MDPI AG}, title = {Building an Enhanced Vocabulary of the Robot Environment with a Ceiling Pointing Camera}, volume = {16}, DOI = {10.3390/s16040493}, keywords = {visual vocabulary, computer vision, bag of words, robotics, place recognition, environment description}, abstract = {Mobile robots are of great help for automatic monitoring tasks in different environments. One of the first tasks that needs to be addressed when creating these kinds of robotic systems is modeling the robot environment. This work proposes a pipeline to build an enhanced visual model of a robot environment indoors. Vision based recognition approaches frequently use quantized feature spaces, commonly known as Bag of Words (BoW) or vocabulary representations. A drawback using standard BoW approaches is that semantic information is not considered as a criteria to create the visual words. To solve this challenging task, this paper studies how to leverage the standard vocabulary construction process to obtain a more meaningful visual vocabulary of the robot work environment using image sequences. We take advantage of spatio-temporal constraints and prior knowledge about the position of the camera. The key contribution of our work is the definition of a new pipeline to create a model of the environment. This pipeline incorporates (1) tracking information to the process of vocabulary construction and (2) geometric cues to the appearance descriptors. Motivated by long term robotic applications, such as the aforementioned monitoring tasks, we focus on a configuration where the robot camera points to the ceiling, which captures more stable regions of the environment. The experimental validation shows how our vocabulary models the environment in more detail than standard vocabulary approaches, without loss of recognition performance. We show different robotic tasks that could benefit of the use of our visual vocabulary approach, such as place recognition or object discovery. For this validation, we use our publicly available data-set. }, year = {2016} } @inproceedings{Schindler1070809, author = {Schindler, Maike and Lilienthal, Achim and Chadalavada, Ravi and {\"O}gren, Magnus}, booktitle = {Proceedings of the 40th Conference of the International Group for the Psychology of Mathematics Education (PME) : }, institution = {Örebro University, School of Science and Technology}, title = {Creativity in the eye of the student : Refining investigations of mathematical creativity using eye-tracking goggles}, abstract = {Mathematical creativity is increasingly important for improved innovation and problem-solving. In this paper, we address the question of how to best investigate mathematical creativity and critically discuss dichotomous creativity scoring schemes. In order to gain deeper insights into creative problem-solving processes, we suggest the use of mobile, unobtrusive eye-trackers for evaluating students’ creativity in the context of Multiple Solution Tasks (MSTs). We present first results with inexpensive eye-tracking goggles that reveal the added value of evaluating students’ eye movements when investigating mathematical creativity—compared to an analysis of written/drawn solutions as well as compared to an analysis of simple videos. }, year = {2016} } @inproceedings{Chadalavada1070994, author = {Chadalavada, Ravi Teja and Andreasson, Henrik and Krug, Robert and Lilienthal, Achim}, booktitle = {Proceedings of RSS Workshop "Social Trust in Autonomous Robots 2016" : }, institution = {Örebro University, School of Science and Technology}, title = {Empirical evaluation of human trust in an expressive mobile robot}, keywords = {Human robot interaction, hri, mobile robot, trust, evaluation}, abstract = {A mobile robot communicating its intentions using Spatial Augmented Reality (SAR) on the shared floor space makes humans feel safer and more comfortable around the robot. Our previous work [1] and several other works established this fact. We built upon that work by adding an adaptable information and control to the SAR module. An empirical study about how a mobile robot builds trust in humans by communicating its intentions was conducted. A novel way of evaluating that trust is presented and experimentally shown that adaption in SAR module lead to natural interaction and the new evaluation system helped us discover that the comfort levels between human-robot interactions approached those of human-human interactions. }, year = {2016} } @article{Canelhas1044256, author = {Canelhas, Daniel R. and Stoyanov, Todor and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {IEEE Robotics and Automation Letters}, number = {2}, pages = {1148--1155}, title = {From Feature Detection in Truncated Signed Distance Fields to Sparse Stable Scene Graphs}, volume = {1}, DOI = {10.1109/LRA.2016.2523555}, keywords = {Mapping, recognition}, abstract = {With the increased availability of GPUs and multicore CPUs, volumetric map representations are an increasingly viable option for robotic applications. A particularly important representation is the truncated signed distance field (TSDF) that is at the core of recent advances in dense 3D mapping. However, there is relatively little literature exploring the characteristics of 3D feature detection in volumetric representations. In this paper we evaluate the performance of features extracted directly from a 3D TSDF representation. We compare the repeatability of Integral invariant features, specifically designed for volumetric images, to the 3D extensions of Harris and Shi & Tomasi corners. We also study the impact of different methods for obtaining gradients for their computation. We motivate our study with an example application for building sparse stable scene graphs, and present an efficient GPU-parallel algorithm to obtain the graphs, made possible by the combination of TSDF and 3D feature points. Our findings show that while the 3D extensions of 2D corner-detection perform as expected, integral invariants have shortcomings when applied to discrete TSDFs. We conclude with a discussion of the cause for these points of failure that sheds light on possible mitigation strategies. }, year = {2016} } @inproceedings{Neumann950089, author = {Neumann, Patrick P. and Hernandez Bennetts, Victor and Lilienthal, Achim J. and Bartholmai, Matthias}, booktitle = {Intelligent Autonomous Systems 13 : }, institution = {Örebro University, School of Science and Technology}, institution = {BAM Federal Institute for Materials Research and Testing, Berlin, Germany}, institution = {BAM Federal Institute for Materials Research and Testing, Berlin, Germany}, pages = {1533--1548}, title = {From Insects to Micro Air Vehicles : A Comparison of Reactive Plume Tracking Strategies}, series = {Advances in Intelligent Systems and Computing}, number = {302}, DOI = {10.1007/978-3-319-08338-4_110}, keywords = {Autonomous micro UAV, Mobile robot olfaction, Gas source localization, Reactive plume tracking, Biologically inspired robots}, abstract = {Insect behavior is a common source of inspiration for roboticists and computer scientists when designing gas-sensitive mobile robots. More specifically, tracking airborne odor plumes, and localization of distant gas sources are abilities that suit practical applications such as leak localization and emission monitoring. Gas sensing with mobile robots has been mostly addressed with ground-based platforms and under simplified conditions and thus, there exist a significant gap between the outstanding insect abilities and state-of-the-art robotics systems. As a step toward practical applications, we evaluated the performance of three biologically inspired plume tracking algorithms. The evaluation is carried out not only with computer simulations, but also with real-world experiments in which, a quadrocopter-based micro Unmanned Aerial Vehicle autonomously follows a methane trail toward the emitting source. Compared to ground robots, micro UAVs bring several advantages such as their superior steering capabilities and fewer mobility restrictions in complex terrains. The experimental evaluation shows that, under certain environmental conditions, insect like behavior in gas-sensitive UAVs is feasible in real-world environments. }, ISBN = {978-3-319-08338-4}, ISBN = {978-3-319-08337-7}, year = {2016} } @inproceedings{Palm1051090, author = {Palm, Rainer and Chadalavada, Ravi and Lilienthal, Achim}, booktitle = {Proceedings of the 8th International Joint Conference on Computational Intelligence (IJCCI 2016) : }, institution = {Örebro University, School of Science and Technology}, note = {Funding Agency:AIR-project, Action and Intention Recognition in Human Interaction with Autonomous Systems}, pages = {67--74}, title = {Fuzzy Modeling and Control for Intention Recognition in Human-Robot Systems}, volume = {2}, DOI = {10.5220/0006015400670074}, keywords = {Fuzzy control, Fuzzy modeling, Human-Robot interaction, human intentions}, abstract = {The recognition of human intentions from trajectories in the framework of human-robot interaction is a challenging field of research. In this paper some control problems of the human-robot interaction and their intentions to compete or cooperate in shared work spaces are addressed and the time schedule of the information flow is discussed. The expected human movements relative to the robot are summarized in a so-called "compass dial" from which fuzzy control rules for the robot's reactions are derived. To avoid collisions between robot and human very early the computation of collision times at predicted human-robot intersections is discussed and a switching controller for collision avoidance is proposed. In the context of the recognition of human intentions to move to certain goals, pedestrian tracks are modeled by fuzzy clustering, lanes preferred by human agents are identified, and the identification of degrees of membership of a pedestrian track to specific lanes are discussed. Computations based on simulated and experimental data show the applicability of the methods presented. }, ISBN = {978-989-758-201-1}, year = {2016} } @inproceedings{Mosberger1057245, author = {Mosberger, Rafael and Schaffernicht, Erik and Andreasson, Henrik and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, pages = {4131--4136}, title = {Inferring human body posture information from reflective patterns of protective work garments}, DOI = {10.1109/IROS.2016.7759608}, keywords = {Computer Vision, Human Detection, Reflective Clothing, Image Segmentation, Active Illumination, Infrared Vision}, abstract = {We address the problem of extracting human body posture labels, upper body orientation and the spatial location of individual body parts from near-infrared (NIR) images depicting patterns of retro-reflective markers. The analyzed patterns originate from the observation of humans equipped with protective high-visibility garments that represent common safety equipment in the industrial sector. Exploiting the shape of the observed reflectors we adopt shape matching based on the chamfer distance and infer one of seven discrete body posture labels as well as the approximate upper body orientation with respect to the camera. We then proceed to analyze the NIR images on a pixel scale and estimate a figure-ground segmentation together with human body part labels using classification of densely extracted local image patches. Our results indicate a body posture classification accuracy of 80% and figure-ground segmentations with 87% accuracy. }, ISBN = {978-1-5090-3762-9}, year = {2016} } @inproceedings{Palm1051086, author = {Palm, Rainer and Bouguerra, Abdelbaki and Abdullah, Muhammad and Lilienthal, Achim}, booktitle = {SMC 2016 : 2016 IEEE International Conference on Systems, Man, and Cybernetics}, institution = {Örebro University, School of Science and Technology}, institution = {The university of Faisalabad, Faisalabad, Pakistan}, pages = {4489--4494}, title = {Navigation in Human-Robot and Robot-Robot Interaction using Optimization Methods}, keywords = {Human-robot interaction, human intentions, collision avoidance, robot navigation, artificial force fields, market-based optimization}, abstract = {Human-robot interaction and robot-robot interaction and cooperation in shared spatial areas is a challenging field of research regarding safety, stability and performance. In this paper the collision avoidance between human and robot by extrapolation of human intentions and a suitable optimization of tracking velocities is discussed. Furthermore for robot-robot interactions in a shared area traffic rules and artificial force potential fields and their optimization by market-based approach are applied for obstacle avoidance. For testing and verification, the navigation strategy is implemented and tested in simulation of more realistic vehicles. Extensive simulation experiments are performed to examine the improvement of the traditional potential field (PF) method by the MBO strategy. }, ISBN = {978-1-5090-1897-0}, year = {2016} } @article{Stoyanov1044254, author = {Stoyanov, Todor and Vaskevicius, Narunas and Mueller, Christian Atanas and Fromm, Tobias and Krug, Robert and Tincani, Vinicio and Mojtahedzadeh, Rasoul and Kunaschk, Stefan and Ernits, R. Mortensen and Canelhas, Daniel R. and Bonilla, Manuell and Schwertfeger, Soeren and Bonini, Marco and Halfar, Harry and Pathak, Kaustubh and Rohde, Moritz and Fantoni, Gualtiero and Bicchi, Antonio and Birk, Andreas and Lilienthal, Achim J. and Echelmeyer, Wolfgang}, institution = {Örebro University, School of Science and Technology}, institution = {Jacobs University Bremen, Bremen, Germany}, institution = {Jacobs University Bremen, Bremen, Germany}, institution = {Jacobs University Bremen, Bremen, Germany}, institution = {University of Pisa, Pisa, Italy}, institution = {Bremer Institut für Produktion und Logistik (BIBA), Bremen, Germany}, institution = {Bremer Institut für Produktion und Logistik (BIBA), Bremen, Germany}, institution = {University of Pisa, Pisa, Italy}, institution = {ShanghaiTech University, Shanghai, China}, institution = {Reutlingen University, Reutlingen, Germany}, institution = {Reutlingen University, Reutlingen, Germany}, institution = {Jacobs University Bremen, Bremen, Germany}, institution = {Bremer Institut für Produktion und Logistik (BIBA), Bremen, Germany}, institution = {University of Pisa, Pisa, Italy}, institution = {Università di Pisa & Istituto Italiano di Tecnologia, Genova, Italy}, institution = {Jacobs University, Bremen, Germany}, institution = {Reutlingen University, Reutlingen, Germany}, journal = {IEEE robotics & automation magazine}, note = {Funding Agency:EU FP7 project ROBLOG ICT-270350}, number = {4}, pages = {94--106}, title = {No More Heavy Lifting : Robotic Solutions to the Container-Unloading Problem}, volume = {23}, DOI = {10.1109/MRA.2016.2535098}, year = {2016} } @inproceedings{Palm1051078, author = {Palm, Rainer and Chadalavada, Ravi and Lilienthal, Achim}, booktitle = {2016 9th International Conference on Human System Interactions, HSI 2016 : Proceedings}, institution = {Örebro University, School of Science and Technology}, note = {Funding Agency:AIR-project Action and Intention Recognition in Human Interaction with Autonomous Systems}, pages = {229--235}, title = {Recognition of Human-Robot Motion Intentions by Trajectory Observation}, series = {Conference on Human System Interaction}, DOI = {10.1109/HSI.2016.7529636}, keywords = {Human robot interaction, human intentions, obstacle avoidance, fuzzy rules}, abstract = {The intention of humans and autonomous robots to interact in shared spatial areas is a challenging field of research regarding human safety, system stability and performance of the system's behavior. In this paper the intention recognition between human and robot from the control point of view are addressed and the time schedule of the exchanged signals is discussed. After a description of the kinematic and geometric relations between human and robot a so-called 'compass dial' with the relative velocities is presented from which suitable fuzzy control rules are derived. The computation of the collision times at intersections and possible avoidance strategies are further discussed. Computations based on simulated and experimental data show the applicability of the methods presented. }, ISBN = {9781509017294}, year = {2016} } @inproceedings{Bunz1071024, author = {Bunz, Elsa and Chadalavada, Ravi Teja and Andreasson, Henrik and Krug, Robert and Schindler, Maike and Lilienthal, Achim}, booktitle = {Proceedings of RO-MAN 2016 Workshop : Workshop on Communicating Intentions in Human-Robot Interaction}, institution = {Örebro University, School of Science and Technology}, institution = {Örebro University, Örebro, Sweden}, title = {Spatial Augmented Reality and Eye Tracking for Evaluating Human Robot Interaction}, abstract = {Freely moving autonomous mobile robots may leadto anxiety when operating in workspaces shared with humans.Previous works have given evidence that communicating in-tentions using Spatial Augmented Reality (SAR) in the sharedworkspace will make humans more comfortable in the vicinity ofrobots. In this work, we conducted experiments with the robotprojecting various patterns in order to convey its movementintentions during encounters with humans. In these experiments,the trajectories of both humans and robot were recorded witha laser scanner. Human test subjects were also equipped withan eye tracker. We analyzed the eye gaze patterns and thelaser scan tracking data in order to understand how the robot’sintention communication affects the human movement behavior.Furthermore, we used retrospective recall interviews to aid inidentifying the reasons that lead to behavior changes. }, year = {2016} } @inproceedings{Triebel950081, author = {Triebel, Rudolph and Arras, Kai and Alami, Rachid and Beyer, Lucas and Breuers, Stefan and Chatila, Raja and Chetouani, Mohamed and Cremers, Daniel and Evers, Vanessa and Fiore, Michelangelo and Hung, Hayley and Ramirez, Omar A. Islas and Joosse, Michiel and Khambhaita, Harmish and Kucner, Tomasz and Leibe, Bastian and Lilienthal, Achim J. and Linder, Timm and Lohse, Manja and Magnusson, Martin and Okal, Billy and Palmieri, Luigi and Rafi, Umer and van Rooij, Marieke and Zhang, Lu}, booktitle = {Field and Service Robotics : Results of the 10th International Conference}, institution = {Örebro University, School of Science and Technology}, institution = {Department of Computer Science, Technische Universität München, Munich, Germany}, institution = {Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany}, institution = {Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), Toulouse, France}, institution = {Rheinisch-Westfälische Technische Hochschule, Aachen, Germany}, institution = {Rheinisch-Westfälische Technische Hochschule, Aachen, Germany}, institution = {Institute for Intelligent Systems and Robotics (ISIR-CNRS), Paris, France}, institution = {Institute for Intelligent Systems and Robotics (ISIR-CNRS), Paris, France}, institution = {Department of Computer Science, Technische Universität München, Munich, Germany}, institution = {University of Twente, Enschede, Netherlands}, institution = {Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), Toulouse, France}, institution = {Delft University of Technology, Delft, Netherlands}, institution = {Institute for Intelligent Systems and Robotics (ISIR-CNRS), Paris, France}, institution = {University of Twente, Enschede, Netherlands}, institution = {Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), Toulouse, France}, institution = {Rheinisch-Westfälische Technische Hochschule, Aachen, Germany}, institution = {Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany}, institution = {University of Twente, Enschede, Netherlands}, institution = {Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany}, institution = {Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany}, institution = {Rheinisch-Westfälische Technische Hochschule, Aachen, Germany}, institution = {University of Amsterdam, Amsterdam, Netherlands}, institution = {University of Twente, Enschede, Netherlands; Delft University of Technology, Delft, Netherlands}, pages = {607--622}, title = {SPENCER : A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports}, series = {Springer Tracts in Advanced Robotics}, number = {113}, DOI = {10.1007/978-3-319-27702-8_40}, abstract = {We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in real-time for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors. }, ISBN = {978-3-319-27702-8}, ISBN = {978-3-319-27700-4}, year = {2016} } @inproceedings{Kucner1070733, author = {Kucner, Tomasz and Magnusson, Martin and Schaffernicht, Erik and Hernandez Bennetts, Victor and Lilienthal, Achim}, booktitle = {Robotics : Science and Systems Conference (RSS 2016)}, institution = {Örebro University, School of Science and Technology}, title = {Tell me about dynamics! : Mapping velocity fields from sparse samples with Semi-Wrapped Gaussian Mixture Models}, abstract = {Autonomous mobile robots often require informa-tion about the environment beyond merely the shape of thework-space. In this work we present a probabilistic method formappingdynamics, in the sense of learning and representingstatistics about the flow of discrete objects (e.g., vehicles, people)as well as continuous media (e.g., air flow). We also demonstratethe capabilities of the proposed method with two use cases. Onerelates to motion planning in populated environments, whereinformation about the flow of people can help robots to followsocial norms and to learn implicit traffic rules by observingthe movements of other agents. The second use case relates toMobile Robot Olfaction (MRO), where information about windflow is crucial for most tasks, including e.g. gas detection, gasdistribution mapping and gas source localisation. We representthe underlying velocity field as a set of Semi-Wrapped GaussianMixture Models (SWGMM) representing the learnt local PDF ofvelocities. To estimate the parameters of the PDF we employ aformulation of Expectation Maximisation (EM) algorithm specificfor SWGMM. We also describe a data augmentation methodwhich allows to build a dense dynamic map based on a sparseset of measurements. In case only a small set of observations isavailable we employ a hierarchical sampling method to generatevirtual observations from existing mixtures. }, year = {2016} } @article{Krug1044259, author = {Krug, Robert and Stoyanov, Todor and Tincani, Vinicio and Andreasson, Henrik and Mosberger, Rafael and Fantoni, Gualtiero and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {University of Pisa, Pisa, Italy}, institution = { University of Pisa, Pisa, Italy}, journal = {IEEE Robotics and Automation Letters}, number = {1}, pages = {546--553}, title = {The Next Step in Robot Commissioning : Autonomous Picking and Palletizing}, volume = {1}, DOI = {10.1109/LRA.2016.2519944}, keywords = {Logistics, grasping, autonomous vehicle navigation, robot safety, mobile manipulation}, abstract = {So far, autonomous order picking (commissioning) systems have not been able to meet the stringent demands regarding speed, safety, and accuracy of real-world warehouse automation, resulting in reliance on human workers. In this letter, we target the next step in autonomous robot commissioning: automatizing the currently manual order picking procedure. To this end, we investigate the use case of autonomous picking and palletizing with a dedicated research platform and discuss lessons learned during testing in simplified warehouse settings. The main theoretical contribution is a novel grasp representation scheme which allows for redundancy in the gripper pose placement. This redundancy is exploited by a local, prioritized kinematic controller which generates reactive manipulator motions on-the-fly. We validated our grasping approach by means of a large set of experiments, which yielded an average grasp acquisition time of 23.5 s at a success rate of 94.7%. Our system is able to autonomously carry out simple order picking tasks in a humansafe manner, and as such serves as an initial step toward future commercial-scale in-house logistics automation solutions. }, year = {2016} } @inproceedings{Arain938083, author = {Arain, Muhammad Asif and Schaffernicht, Erik and Hernandez Bennetts, Victor and Lilienthal, Achim J.}, booktitle = {2016 IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, pages = {4275--4281}, title = {The Right Direction to Smell : Efficient Sensor Planning Strategies for Robot Assisted Gas Tomography}, DOI = {10.1109/ICRA.2016.7487624}, keywords = {Sensor planning, robot exploration, sensing geometry, robot assisted gas tomography, mobile robot olfaction, coverage planning, surveillance robots}, abstract = {Creating an accurate model of gas emissions is an important task in monitoring and surveillance applications. A promising solution for a range of real-world applications are gas-sensitive mobile robots with spectroscopy-based remote sensors that are used to create a tomographic reconstruction of the gas distribution. The quality of these reconstructions depends crucially on the chosen sensing geometry. In this paper we address the problem of sensor planning by investigating sensing geometries that minimize reconstruction errors, and then formulate an optimization algorithm that chooses sensing configurations accordingly. The algorithm decouples sensor planning for single high concentration regions (hotspots) and subsequently fuses the individual solutions to a global solution consisting of sensing poses and the shortest path between them. The proposed algorithm compares favorably to a template matching technique in a simple simulation and in a real-world experiment. In the latter, we also compare the proposed sensor planning strategy to the sensing strategy of a human expert and find indications that the quality of the reconstructed map is higher with the proposed algorithm. }, year = {2016} } @inproceedings{HernandezBennetts1070802, author = {Hernandez Bennetts, Victor and Schaffernicht, Erik and Lilienthal, Achim J. and Fan, Han and Kucner, Tomasz Piotr and Andersson, Lena and Johansson, Anders}, booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, institution = {Department of Occupational and Environmental Medicine, Örebro University Hospital, Örebro, Sweden}, institution = {Department of Occupational and Environmental Medicine, Örebro University Hospital, Örebro, Sweden}, pages = {131--136}, eid = {7759045}, title = {Towards occupational health improvement in foundries through dense dust and pollution monitoring using a complementary approach with mobile and stationary sensing nodes}, DOI = {10.1109/IROS.2016.7759045}, keywords = {Occupational Health; Mobile Robot Olfaction; Pollution Monitoring}, abstract = {In industrial environments, such as metallurgic facilities, human operators are exposed to harsh conditions where ambient air is often polluted with quartz, dust, lead debris and toxic fumes. Constant exposure to respirable particles can cause irreversible health damages and thus it is of high interest for occupational health experts to monitor the air quality on a regular basis. However, current monitoring procedures are carried out sparsely, with data collected in single day campaigns limited to few measurement locations. In this paper we explore the use and present first experimental results of a novel heterogeneous approach that uses a mobile robot and a network of low cost sensing nodes. The proposed system aims to address the spatial and temporal limitations of current monitoring techniques. The mobile robot, along with standard localization and mapping algorithms, allows to produce short term, spatially dense representations of the environment where dust, gas, ambient temperature and airflow information can be modelled. The sensing nodes on the other hand, can collect temporally dense (and usually spatially sparse) information during long periods of time, allowing in this way to register for example, daily variations in the pollution levels. Using data collected with the proposed system in an steel foundry, we show that a heterogeneous approach provides dense spatio-temporal information that can be used to improve the working conditions in industrial facilities. }, ISBN = {9781509037629}, year = {2016} } @inproceedings{Siddiqui945980, author = {Siddiqui, J. Rafid and Andreasson, Henrik and Driankov, Dimiter and Lilienthal, Achim J.}, booktitle = {2016 IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, pages = {5766--5773}, eid = {7487800}, title = {Towards visual mapping in industrial environments : a heterogeneous task-specific and saliency driven approach}, series = {IEEE International Conference on Robotics and Automation}, DOI = {10.1109/ICRA.2016.7487800}, keywords = {Image color analysis, Object detection, Robot sensing systems, Service robots, Training, Visualization}, abstract = {The highly percipient nature of human mind in avoiding sensory overload is a crucial factor which gives human vision an advantage over machine vision, the latter has otherwise powerful computational resources at its disposal given today’s technology. This stresses the need to focus on methods which extract a concise representation of the environment inorder to approach a complex problem such as visual mapping. This article is an attempt of creating a mapping system, which proposes an architecture that combines task-specific and saliency driven approaches. The proposed method is implemented on a warehouse robot. The proposed solution provide a priority framework which enables an industrial robot to build a concise visual representation of the environment. The method is evaluated on data collected by a RGBD sensor mounted on a fork-lift robot and shows promise for addressing visual mapping problems in industrial environments. }, ISBN = {978-146738026-3}, year = {2016} } @inproceedings{Fan1057307, author = {Fan, Han and Hernandez Bennetts, Victor and Schaffernicht, Erik and Lilienthal, Achim J.}, booktitle = {2016 IEEE SENSORS : }, institution = {Örebro University, School of Science and Technology}, note = {Funding Agency:ICT by the European Commission  645101}, title = {Unsupervised gas discrimination in uncontrolled environments by exploiting density peaks}, series = {Proceedings of IEEE Sensors}, DOI = {10.1109/ICSENS.2016.7808903}, keywords = {gas discrimination, Open Sampling Systems, metal oxide sensors, unsupervised learning}, abstract = {Gas discrimination with Open Sampling Systems based on low-cost electro-chemical sensor arrays is of great interest in several applications, such as exploration of hazardous areas and environmental monitoring. Due to the lack of labeled training data or the high costs of obtaining them, as well as the presence of unknown interferents in the target environments, supervised learning is often not applicable and thus, unsupervised learning is an interesting alternative. In this work, we present a cluster analysis approach that can infer the number of different chemical compounds and label the measurements in a given uncontrolled environment without relying on previously acquired training data. Our approach is validated with data collected in indoor and outdoor environments by a mobile robot equipped with an array of metal oxide sensors. The results show that high classification accuracy can be achieved with a rather low sensitivity to the selection of the only functional parameter of our proposed algorithm.  }, ISBN = {978-1-4799-8287-5}, year = {2016} } @incollection{Ishida1070680, author = {Ishida, Hiroshi and Lilienthal, Achim J. and Matsukura, Haruka and Hernandez Bennetts, Victor and Schaffernicht, Erik}, booktitle = {Essentials of Machine Olfaction and Taste : }, institution = {Örebro University, School of Science and Technology}, institution = {Tokyo University of Agriculture and Technology, Tokyo, Japan}, institution = {Tokyo University of Agriculture and Technology, Tokyo, Japan}, pages = {219--246}, title = {Using Chemical Sensors as 'Noses' for Mobile Robots}, abstract = {Gas sensors detect the presence of gaseous chemical compounds in air. They are often used in the form of gas alarms for detecting dangerous or hazardous gases. However, a limited number of stationary gas alarms may not be always sufficient to cover a large industrial facility. Human workers having a portable gas detector in their hand needs to be sent to thoroughly check gas leaks in the areas not covered by stationary gas alarms. However, making repetitive measurements with a gas detector at a number of different locations is laborious. Moreover, the places where the gas concentration level needs to be checked are often potentially dangerous for human workers. If a portable gas detector is mounted on a mobile robot, the task of patrolling in an industrial facility for checking a gas leak can be automated. Robots are good at doing repetitive tasks, and can be sent into harsh environments. }, ISBN = {9781118768488}, ISBN = {9781118768518}, year = {2016} } @inproceedings{Mielle1054805, author = {Mielle, Malcolm and Magnusson, Martin and Lilienthal, Achim J.}, booktitle = {2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) : }, institution = {Örebro University, School of Science and Technology}, pages = {252--257}, title = {Using sketch-maps for robot navigation : interpretation and matching}, DOI = {10.1109/SSRR.2016.7784307}, keywords = {sketch, sketch-map, human robot interface, HRI, graph matching}, abstract = {We present a study on sketch-map interpretationand sketch to robot map matching, where maps have nonuniform scale, different shapes or can be incomplete. For humans, sketch-maps are an intuitive way to communicate navigation information, which makes it interesting to use sketch-maps forhuman robot interaction; e.g., in emergency scenarios. To interpret the sketch-map, we propose to use a Voronoi diagram that is obtained from the distance image on which a thinning parameter is used to remove spurious branches. The diagram is extracted as a graph and an efficient error-tolerant graph matching algorithm is used to find correspondences, while keeping time and memory complexity low. A comparison against common algorithms for graph extraction shows that our method leads to twice as many good matches. For simple maps, our method gives 95% good matches even for heavily distorted sketches, and for a more complex real-world map, up to 58%. This paper is a first step toward using unconstrained sketch-maps in robot navigation. }, ISBN = {978-1-5090-4349-1}, year = {2016} } @inproceedings{Mojtahedzadeh900560, author = {Mojtahedzadeh, Rasoul and Lilienthal, Achim J.}, booktitle = {2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, pages = {2897--2903}, title = {A principle of minimum translation search approach for object pose refinement}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2015.7353776}, keywords = {Pose estimation, robot vision, search problems, A-star search, PROMTS, cluttered environments, depth-limited search, detected poses, geometrically consistent objects configuration, inaccurate noisy poses, interpenetration-free configuration, minimum translation search, minimum translation search approach, object pose estimation approaches, object pose refinement, overlapping objects, pose estimation accuracy, rigid body assumption, shipping containers, Containers, Search problems, Shape, Solid modeling, Three-dimensional displays, Uncertainty}, abstract = {The state-of-the-art object pose estimation approaches represent the set of detected poses together with corresponding uncertainty. The inaccurate noisy poses may result in a configuration of overlapping objects especially in cluttered environments. Under a rigid body assumption the inter-penetrations between pairs of objects are geometrically inconsistent. In this paper, we propose the principle of minimum translation search, PROMTS, to find an inter-penetration-free configuration of the initially detected objects. The target application is to automate the task of unloading shipping containers, where a geometrically consistent configuration of objects is required for high level reasoning and manipulation. We find that the proposed approach to resolve geometrical inconsistencies improves the overall pose estimation accuracy. We examine the utility of two selected search methods: A-star and Depth-Limited search. The performance of the search algorithms are tested on data sets generated in simulation and from real-world scenarios. The results show overall improvement of the estimated poses and suggest that depth-limited search presents the best overall performance. }, ISBN = {978-1-4799-9994-1}, year = {2015} } @inproceedings{Asadi957520, author = {Asadi, Sahar and Lilienthal, Achim}, booktitle = {2015 European Conference on Mobile Robots (ECMR) : }, institution = {Örebro University, School of Science and Technology}, eid = {7324215}, publisher = {IEEE conference proceedings}, title = {Approaches to Time-Dependent Gas Distribution Modelling}, DOI = {10.1109/ECMR.2015.7324215}, keywords = {Dispersion; Kernel; Pollution measurement; Predictive models; Robot sensing systems; Time measurement; Weight measurement}, abstract = {Mobile robot olfaction solutions for gas distribution modelling offer a number of advantages, among them autonomous monitoring in different environments, mobility to select sampling locations, and ability to cooperate with other systems. However, most data-driven, statistical gas distribution modelling approaches assume that the gas distribution is generated by a time-invariant random process. Such time-invariant approaches cannot model well developing plumes or fundamental changes in the gas distribution. In this paper, we discuss approaches that explicitly consider the measurement time, either by sub-sampling according to a given time-scale or by introducing a recency weight that relates measurement and prediction time. We evaluate the performance of these time-dependent approaches in simulation and in real-world experiments using mobile robots. The results demonstrate that in dynamic scenarios improved gas distribution models can be obtained with time-dependent approaches. }, ISBN = {978-1-4673-9163-4}, year = {2015} } @article{Andreasson807693, author = {Andreasson, Henrik and Bouguerra, Abdelbaki and Cirillo, Marcello and Dimitrov, Dimitar Nikolaev and Driankov, Dimiter and Karlsson, Lars and Lilienthal, Achim J. and Pecora, Federico and Saarinen, Jari Pekka and Sherikov, Aleksander and Stoyanov, Todor}, institution = {Örebro University, School of Science and Technology}, institution = {INRIA - Grenoble, Meylan, France}, institution = {Aalto University, Espo, Finland }, institution = {Centre de recherche Grenoble Rhône-Alpes, Grenoble, France }, journal = {IEEE robotics & automation magazine}, number = {1}, pages = {64--75}, title = {Autonomous transport vehicles : where we are and what is missing}, volume = {22}, DOI = {10.1109/MRA.2014.2381357}, keywords = {Intelligent vehicles; Mobile robots; Resource management; Robot kinematics; Trajectory; Vehicle dynamics}, abstract = {In this article, we address the problem of realizing a complete efficient system for automated management of fleets of autonomous ground vehicles in industrial sites. We elicit from current industrial practice and the scientific state of the art the key challenges related to autonomous transport vehicles in industrial environments and relate them to enabling techniques in perception, task allocation, motion planning, coordination, collision prediction, and control. We propose a modular approach based on least commitment, which integrates all modules through a uniform constraint-based paradigm. We describe an instantiation of this system and present a summary of the results, showing evidence of increased flexibility at the control level to adapt to contingencies. }, year = {2015} } @inproceedings{Khaliq900440, author = {Khaliq, Ali and Pashami, Sepideh and Schaffernicht, Erik and Lilienthal, Achim J. and Hernandez Bennetts, Victor}, booktitle = {Proceedings of the 16th International Symposium on Olfaction and Electronic Noses : }, institution = {Örebro University, School of Science and Technology}, eid = {137}, title = {Bringing Artificial Olfaction and Mobile Robotics Closer Together : An Integrated 3D Gas Dispersion Simulator in ROS}, keywords = {Mobile robot olfaction, gas dispersion simulation, gas sensor simulation, MOX sensors, environmental monitoring}, abstract = {Despite recent achievements, the potential of gas-sensitive mobile robots cannot be realized due to the lack of research on fundamental questions. A key limitation is the difficulty to carry out evaluations against ground truth. To test and compare approaches for gas-sensitive robots a truthful gas dispersion simulator is needed. In this paper we present a unified framework to simulate gas dispersion and to evaluate mobile robotics and gas sensing algorithms using ROS. Gas dispersion is modeled as a set of particles affected by diffusion, turbulence, advection and gravity. Wind information is integrated as time snapshots computed with any fluid dynamics computation tool. In addition, response models for devices such as Metal Oxide (MOX) sensors can be integrated in the framework. }, URL = {https://www.eventing.hu/bazar/ISOEN2015-AbstractBook.pdf}, year = {2015} } @inproceedings{Arain874039, author = {Arain, Muhammad Asif and Cirillo, Marcello and Hernandez Bennetts, Victor and Schaffernicht, Erik and Trincavelli, Marco and Lilienthal, Achim J.}, booktitle = {2015 IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, institution = {Scania AB, Södertälje, Sweden}, pages = {3428--3434}, title = {Efficient Measurement Planning for Remote Gas Sensing with Mobile Robots}, DOI = {10.1109/ICRA.2015.7139673}, keywords = {Sensor planning, mobile robot olfaction, remote gas sensing}, abstract = {The problem of gas detection is relevant to manyreal-world applications, such as leak detection in industrialsettings and surveillance. In this paper we address the problemof gas detection in large areas with a mobile robotic platformequipped with a remote gas sensor. We propose a novelmethod based on convex relaxation for quickly finding anexploration plan that guarantees a complete coverage of theenvironment. Our method proves to be highly efficient in termsof computational requirements and to provide nearly-optimalsolutions. We validate our approach both in simulation andin real environments, thus demonstrating its applicability toreal-world problems. }, ISBN = {978-1-4799-6923-4}, year = {2015} } @inproceedings{Andreasson894653, author = {Andreasson, Henrik and Saarinen, Jari and Cirillo, Marcello and Stoyanov, Todor and Lilienthal, Achim}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA), 2015 : }, institution = {Örebro University, School of Science and Technology}, institution = {SCANIA AB, Södertälje, Sweden}, pages = {662--669}, title = {Fast, continuous state path smoothing to improve navigation accuracy}, series = {Proceedings - IEEE International Conference on Robotics and Automation}, DOI = {10.1109/ICRA.2015.7139250}, abstract = {Autonomous navigation in real-world industrial environments is a challenging task in many respects. One of the key open challenges is fast planning and execution of trajectories to reach arbitrary target positions and orientations with high accuracy and precision, while taking into account non-holonomic vehicle constraints. In recent years, lattice-based motion planners have been successfully used to generate kinematically and kinodynamically feasible motions for non-holonomic vehicles. However, the discretized nature of these algorithms induces discontinuities in both state and control space of the obtained trajectories, resulting in a mismatch between the achieved and the target end pose of the vehicle. As endpose accuracy is critical for the successful loading and unloading of cargo in typical industrial applications, automatically planned paths have not be widely adopted in commercial AGV systems. The main contribution of this paper addresses this shortcoming by introducing a path smoothing approach, which builds on the output of a lattice-based motion planner to generate smooth drivable trajectories for non-holonomic industrial vehicles. In real world tests presented in this paper we demonstrate that the proposed approach is fast enough for online use (it computes trajectories faster than they can be driven) and highly accurate. In 100 repetitions we achieve mean end-point pose errors below 0.01 meters in translation and 0.002 radians in orientation. Even the maximum errors are very small: only 0.02 meters in translation and 0.008 radians in orientation. }, ISBN = {9781479969234}, year = {2015} } @article{Arain807075, author = {Arain, Muhammad Asif and Trincavelli, Marco and Cirillo, Marcello and Schaffernicht, Erik and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Sensors}, number = {3}, pages = {6845--6871}, title = {Global coverage measurement planning strategies for mobile robots equipped with a remote gas sensor}, volume = {15}, DOI = {10.3390/s150306845}, keywords = {Coverage planning, Mobile robot olfaction, Remote gas detection, Sensor planning, Surveillance robots}, abstract = {The problem of gas detection is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. In this paper, we address the problem of gas detection in large areas with a mobile robotic platform equipped with a remote gas sensor. We propose an algorithm that leverages a novel method based on convex relaxation for quickly solving sensor placement problems, and for generating an efficient exploration plan for the robot. To demonstrate the applicability of our method to real-world environments, we performed a large number of experimental trials, both on randomly generated maps and on the map of a real environment. Our approach proves to be highly efficient in terms of computational requirements and to provide nearly-optimal solutions. }, year = {2015} } @inproceedings{Krug842706, author = {Krug, Robert and Stoyanov, Todor and Lilienthal, Achim}, booktitle = {Robotics: Science and Systems Conference : Workshop on Bridging the Gap between Data-driven and Analytical Physics-based Grasping and Manipulation}, institution = {Örebro University, School of Science and Technology}, title = {Grasp Envelopes for Constraint-based Robot Motion Planning and Control}, keywords = {Grasping, Grasp Control, Motion Control}, abstract = {We suggest a grasp represen-tation in form of a set of enveloping spatial constraints. Our representation transforms the grasp synthesisproblem (i. e., the question of where to position the graspingdevice) from finding a suitable discrete manipulator wrist pose to finding a suitable pose manifold. Also the correspondingmotion planning and execution problem is relaxed – insteadof transitioning the wrist to a discrete pose, it is enough tomove it anywhere within the grasp envelope which allows toexploit kinematic redundancy. }, year = {2015} } @inproceedings{HernandezBennetts900844, author = {Hernandez Bennetts, Victor and Lilienthal, Achim J. and Schaffernicht, Erik and Ferrari, Silvia and Albertson, John}, booktitle = {Workshop on Realistic, Rapid and Repeatable Robot Simulation : }, institution = {Örebro University, School of Science and Technology}, institution = {Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca NY, USA}, institution = {School of Civil and Environmental Engineering, Cornell University, Ithaca NY, USA}, title = {Integrated Simulation of Gas Dispersion and Mobile Sensing Systems}, keywords = {Robot simulatior, gas dispersion simulation, metal oxide sensors}, abstract = {Accidental or intentional releases of contaminants into the atmosphere pose risks to human health, the environment, the economy, and national security. In some cases there may be a single release from an unknown source, while in other cases there are fugitive emissions from multiple sources. The need to locate and characterize the sources efficiently - whether it be the urgent need to evacuate or the systematic need to cover broad geographical regions with limited resources - is shared among all cases. Efforts have begun to identify leaks with gas analyzers mounted on Mobile Robot Olfaction (MRO) systems, road vehicles, and networks of fixed sensors, such as may be based in urban environments. To test and compare approaches for gas-sensitive robots a truthful gas dispersion simulator is needed. In this paper, we present a unified framework to simulate gas dispersion and to evaluate mobile robotics and gas sensing technologies using ROS. This framework is also key to developing and testing optimization and planning algorithms for determining sensor placement and sensor motion, as well as for fusing and connecting the sensor measurements to the leak locations. }, year = {2015} } @inproceedings{Mosberger891476, author = {Mosberger, Rafael and Leibe, Bastian and Andreasson, Henrik and Lilienthal, Achim}, booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, institution = {Aachen University, Aachen, Germany}, pages = {697--703}, title = {Multi-band Hough Forests for detecting humans with Reflective Safety Clothing from mobile machinery}, series = {Proceedings - IEEE International Conference on Robotics and Automation}, DOI = {10.1109/ICRA.2015.7139255}, keywords = {Human Detection, Robot Vision, Industrial Safety}, abstract = {We address the problem of human detection from heavy mobile machinery and robotic equipment operating at industrial working sites. Exploiting the fact that workers are typically obliged to wear high-visibility clothing with reflective markers, we propose a new recognition algorithm that specifically incorporates the highly discriminative features of the safety garments in the detection process. Termed Multi-band Hough Forest, our detector fuses the input from active near-infrared (NIR) and RGB color vision to learn a human appearance model that not only allows us to detect and localize industrial workers, but also to estimate their body orientation. We further propose an efficient pipeline for automated generation of training data with high-quality body part annotations that are used in training to increase detector performance. We report a thorough experimental evaluation on challenging image sequences from a real-world production environment, where persons appear in a variety of upright and non-upright body positions. }, ISBN = {978-1-4799-6923-4}, year = {2015} } @inproceedings{Krug808145, author = {Krug, Robert and Stoyanov, Todor and Tincani, Vinicio and Andreasson, Henrik and Mosberger, Rafael and Fantoni, Gualtiero and Bicchi, Antonio and Lilienthal, Achim}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA) - Workshop on Robotic Hands, Grasping, and Manipulation : }, institution = {Örebro University, School of Science and Technology}, institution = {Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy}, institution = {Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy}, institution = {Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy}, title = {On Using Optimization-based Control instead of Path-Planning for Robot Grasp Motion Generation}, keywords = {Grasping, Motion Planning, Control}, year = {2015} } @inproceedings{Magnusson849536, author = {Magnusson, Martin and Kucner, Tomasz and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE) : }, institution = {Örebro University, School of Science and Technology}, pages = {450--455}, publisher = {IEEE conference proceedings}, title = {Quantitative Evaluation of Coarse-To-Fine Loading Strategies for Material Rehandling}, series = {IEEE International Conference on Automation Science and Engineering (CASE)}, DOI = {10.1109/CoASE.2015.7294120}, keywords = {Emerging Topics in Automation, Automation for Machine Tools, Sustainable Production}, abstract = {Autonomous handling of piled materials is an emerging topic in automation science and engineering. A central question for material rehandling tasks (transporting materials that have been assembled in piles) is “where to dig, in order to optimise performance”? In particular, we are interested in the application of autonomous wheel loaders to handle piles of gravel. Still, the methodology proposed in this paper relates to granular materials in other applications too. Although initial work on suggesting strategies for where to dig has been done by a few other groups, there has been a lack of structured evaluation of the usefulness of the proposed strategies. In an attempt to further the field, we present a quantitative evaluation of loading strategies; both coarse ones, aiming to maintain a good pile shape over long-term operation; and refined ones, aiming to detect the locally best attack pose for acquiring a good fill grade in the bucket. Using real-world data from a semi-automated test platform, we present an assessment of how previously proposed pile shape measures can be mapped to the amount of material in the bucket after loading. We also present experimental data for long-term strategies, using simulations based on real-world 3D scan data from a production site. }, ISBN = {978-1-4673-8183-3}, year = {2015} } @inproceedings{Tincani900484, author = {Tincani, Vinicio and Catalano, Manuel and Grioli, Giorgio and Stoyanov, Todor and Krug, Robert and Lilienthal, Achim J. and Fantoni, Gualtiero and Bicchi, Antonio}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {University of Pisa, Pisa, Italy}, institution = {University of Pisa, Pisa, Italy}, institution = {University of Pisa, Pisa, Italy}, institution = {University of Pisa, Pisa, Italy}, institution = {University of Pisa, Pisa, Italy; Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genova, Italy}, pages = {2744--2750}, title = {Sensitive Active Surfaces on the Velvet II Dexterous Gripper}, URL = {https://www.ias.informatik.tu-darmstadt.de/uploads/Workshops/ICRA2015TactileForce/03_icra_ws_tactileforce.pdf}, year = {2015} } @article{Mojtahedzadeh778509, author = {Mojtahedzadeh, Rasoul and Bouguerra, Abdelbaki and Schaffernicht, Erik and Lilienthal, Achim J}, institution = {Örebro University, School of Science and Technology}, journal = {Robotics and Autonomous Systems}, number = {SI}, pages = {99--117}, title = {Support relation analysis and decision making for safe robotic manipulation tasks}, volume = {71}, DOI = {10.1016/j.robot.2014.12.014}, keywords = {Scene analysis, Machine learning, Decision making, World models, Robotic manipulation}, abstract = {In this article, we describe an approach to address the issue of automatically building and using high-level symbolic representations that capture physical interactions between objects in static configurations. Our work targets robotic manipulation systems where objects need to be safely removed from piles that come in random configurations. We assume that a 3D visual perception module exists so that objects in the piles can be completely or partially detected. Depending on the outcome of the perception, we divide the issue into two sub-issues: 1) all objects in the configuration are detected; 2) only a subset of objects are correctly detected. For the first case, we use notions from geometry and static equilibrium in classical mechanics to automatically analyze and extract act and support relations between pairs of objects. For the second case, we use machine learning techniques to estimate the probability of objects supporting each other. Having the support relations extracted, a decision making process is used to identify which object to remove from the configuration so that an expected minimum cost is optimized. The proposed methods have been extensively tested and validated on data sets generated in simulation and from real world configurations for the scenario of unloading goods from shipping containers. }, year = {2015} } @inproceedings{Chadalavada900532, author = {Chadalavada, Ravi Teja and Andreasson, Henrik and Krug, Robert and Lilienthal, Achim}, booktitle = {2015 European Conference on Mobile Robots (ECMR) : }, institution = {Örebro University, School of Science and Technology}, publisher = {IEEE conference proceedings}, title = {That’s on my Mind! : Robot to Human Intention Communication through on-board Projection on Shared Floor Space}, DOI = {10.1109/ECMR.2015.7403771}, keywords = {Human Robot Interaction, Intention Communication, Shared spaces}, abstract = {The upcoming new generation of autonomous vehicles for transporting materials in industrial environments will be more versatile, flexible and efficient than traditional AGVs, which simply follow pre-defined paths. However, freely navigating vehicles can appear unpredictable to human workers and thus cause stress and render joint use of the available space inefficient. Here we address this issue and propose on-board intention projection on the shared floor space for communication from robot to human. We present a research prototype of a robotic fork-lift equipped with a LED projector to visualize internal state information and intents. We describe the projector system and discuss calibration issues. The robot’s ability to communicate its intentions is evaluated in realistic situations where test subjects meet the robotic forklift. The results show that already adding simple information, such as the trajectory and the space to be occupied by the robot in the near future, is able to effectively improve human response to the robot. }, ISBN = {978-1-4673-9163-4}, year = {2015} } @inproceedings{Tincani900487, author = {Tincani, Vinicio and Stoyanov, Todor and Krug, Robert and Catalano, Manuel and Grioli, Giorgio and Lilienthal, Achim J. and Fantoni, Gualtiero and Bicchi, Antonio}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {University of Pisa, Pisa, Italy}, institution = {University of Pisa, Pisa, Italy}, institution = {University of Pisa, Pisa, Italy}, institution = {University of Pisa, Pisa, Italy}, institution = {Istituto Italiano di Tecnologia, Genova, Italy}, title = {The Grasp Acquisition Strategy of the Velvet II}, year = {2015} } @inproceedings{Kucner957495, author = {Kucner, Tomasz Piotr and Magnusson, Martin and Lilienthal, Achim J.}, booktitle = {2015 European Conference on Mobile Robots (ECMR) : }, institution = {Örebro University, School of Science and Technology}, publisher = {IEEE conference proceedings}, title = {Where am I? : An NDT-based prior for MCL}, DOI = {10.1109/ECMR.2015.7324175}, abstract = {One of the key requirements of autonomous mobile robots is a robust and accurate localisation system. Recent advances in the development of Monte Carlo Localisation (MCL) algorithms, especially the Normal Distribution Transform Monte Carlo Localisation (NDT-MCL), provides memory-efficient reliable localisation with industry-grade precision. We propose an approach for building an informed prior for NDT-MCL (in fact for any MCL algorithm) using an initial observation of the environment and its map. Leveraging on the NDT map representation, we build a set of poses using partial observations. After that we construct a Gaussian Mixture Model (GMM) over it. Next we obtain scores for each distribution in GMM. In this way we obtain in an efficient way a prior for NDT-MCL. Our approach provides a more focused then uniform initial distribution, concentrated in states where the robot is more likely to be, by building a Gaussian mixture model over potential poses. We present evaluations and quantitative results using real-world data from an indoor environment. Our experiments show that, compared to a uniform prior, the proposed method significantly increases the number of successful initialisations of NDT-MCL and reduces the time until convergence, at a negligible initial cost for computing the prior. }, ISBN = {978-1-4673-9163-4}, year = {2015} } @article{Mosberger772165, author = {Mosberger, Rafael and Andreasson, Henrik and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Sensors}, number = {10}, pages = {17952--17980}, title = {A customized vision system for tracking humans wearing reflective safety clothing from industrial vehicles and machinery}, volume = {14}, DOI = {10.3390/s141017952}, keywords = {infrared vision, human detection, industrial safety, high-visibility clothing}, abstract = {This article presents a novel approach for vision-based detection and tracking of humans wearing high-visibility clothing with retro-reflective markers. Addressing industrial applications where heavy vehicles operate in the vicinity of humans, we deploy a customized stereo camera setup with active illumination that allows for efficient detection of the reflective patterns created by the worker's safety garments. After segmenting reflective objects from the image background, the interest regions are described with local image feature descriptors and classified in order to discriminate safety garments from other reflective objects in the scene. In a final step, the trajectories of the detected humans are estimated in 3D space relative to the camera. We evaluate our tracking system in two industrial real-world work environments on several challenging video sequences. The experimental results indicate accurate tracking performance and good robustness towards partial occlusions, body pose variation, and a wide range of different illumination conditions. }, year = {2014} } @inproceedings{HernandezBennetts779039, author = {Hernandez Bennetts, Victor and Schaffernicht, Erik and Pomadera Sese, Victor and Lilienthal, Achim J. and Trincavelli, Marco}, booktitle = {Proceedings of the IEEE Sensors Conference 2014 : }, institution = {Örebro University, School of Science and Technology}, institution = {Institute of Bioengineering of Catalonia, Barcelona, Spain}, title = {A Novel Approach for Gas Discrimination in Natural Environments with Open Sampling Systems}, DOI = {10.1109/ICSENS.2014.6985437}, abstract = {This work presents a gas discrimination approachfor Open Sampling Systems (OSS), composed of non-specificmetal oxide sensors only. In an OSS, as used on robots or insensor networks, the sensors are exposed to the dynamics of theenvironment and thus, most of the data corresponds to highlydiluted samples while high concentrations are sparse. In addition,a positive correlation between class separability and concentra-tion level can be observed. The proposed approach computes theclass posteriors by coupling the pairwise probabilities betweenthe compounds to a confidence model based on an estimation ofthe concentration. In this way a rejection posterior, analogous tothe detection limit of the human nose, is learned. Evaluation wasconducted in indoor and outdoor sites, with an OSS equippedrobot, in the presence of two gases. The results show that theproposed approach achieves a high classification performancewith a low sensitivity to the selection of meta parameters. }, year = {2014} } @article{Neumann780080, author = {Neumann, Patrick P. and Schn{\"u}rmacher, Michael and Hernandez Bennetts, Victor and Lilienthal, Achim J. and Bartholmai, Matthias and Schiller, Jochen H.}, institution = {Örebro University, School of Science and Technology}, institution = {BAM Federal Institute for Materials Research and Testing, Berlin, Germany}, institution = {Institute of Computer Science, FU University, Berlin, Germany}, institution = {BAM Federal Institute for Materials Research and Testing, Berlin, Germany}, institution = {Institute of Computer Science, FU University, Berlin, Germany}, journal = {Sensor Letters}, number = {6-7}, pages = {1113--1118}, title = {A Probabilistic Gas Patch Path Prediction Approach for Airborne Gas Source Localization in Non-Uniform Wind Fields}, volume = {12}, DOI = {10.1166/sl.2014.3168}, keywords = {autonomous micro UAV, chemical and wind sensing, gas source localization, particle filter}, abstract = {In this paper, we show that a micro unmanned aerial vehicle (UAV) equipped with commercially available gas sensors can addressenvironmental monitoring and gas source localization (GSL) tasks. To account for the challenges of gas sensing under real-world conditions,we present a probabilistic approach to GSL that is based on a particle filter (PF). Simulation and real-world experiments demonstrate thesuitability of this algorithm for micro UAV platforms. }, year = {2014} } @article{Schaffernicht779893, author = {Schaffernicht, Erik and Trincavelli, Marco and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Sensor Letters}, number = {6-7}, pages = {1142--1146}, title = {Bayesian Spatial Event Distribution Grid Maps for Modeling the Spatial Distribution of Gas Detection Events}, volume = {12}, DOI = {10.1166/sl.2014.3189}, keywords = {BERNOULLI DISTRIBUTION; BETA DISTRIBUTION; GAS DISTRIBUTION MAPPING; STATISTICAL MODELING}, abstract = {In this paper we introduce a novel gas distribution mapping algorithm, Bayesian Spatial Event Distribution (BASED), that, instead of modeling the spatial distribution of a quasi-continuous gas concentration, models the spatial distribution of gas events, for example detection and non-detection of a target gas. The proposed algorithm is based on the Bayesian Inference framework and models the likelihood of events at a certain location with a Bernoulli distribution. In order to avoid overfitting, a Bayesian approach is used with a beta distribution prior for the parameter μ that governs the Bernoulli distribution. In this way, the posterior distribution maintains the same form of the prior, i.e., will be a beta distribution as well, enabling a simple approach for sequential learning. To learn a map composed of beta distributions, we discretize the inspection area into a grid and extrapolate from local measurements using Gaussian kernels. We demonstrate the proposed algorithm for MOX sensors and a photo ionization detector mounted on a mobile robot and show how qualitatively similar maps are obtained from very different gas sensors. }, year = {2014} } @article{HernandezBennetts748117, author = {Hernandez Bennetts, Victor and Schaffernicht, Erik and Pomadera Sese, Victor and Lilienthal, Achim J. and Marco, Santiago and Trincavelli, Marco}, institution = {Örebro University, School of Science and Technology}, institution = {Institute of Bioengineering of Catalonia, Barcelona, Spain}, institution = {Signal and Information Processing for Sensing Systema, Institute for Bioengineering of Catalonia, Barcelona, Spain; Departament d’Electrònica, Universitat de Barcelona, Barcelona, Spain}, journal = {Sensors}, note = {Funding Agencies:Gasbot project 8140Spanish project: "Signal Processing for Ion Mobility Spectrometry: Analysis of Biomedical fluids and detection of toxic chemicals" TEC2011-26143Departament d'Universitats, Recerca i Societat de la Informacio de la Generalitat de Catalunya SGR 1445Comissionat per a Universitats i Recerca del DIUE de la Generalitat de Catalunya and the European Social Fund (ESF)SURDepartment d'Economia i Coneixement}, number = {9}, pages = {17331--17352}, publisher = {MDPI AG}, title = {Combining Non Selective Gas Sensors on a Mobile Robot for Identification and Mapping of Multiple Chemical Compounds}, volume = {14}, DOI = {10.3390/s140917331}, keywords = {environmental monitoring; gas discrimination; gas distribution mapping; service robots; open sampling systems; PID, metal oxide sensors}, abstract = {In this paper, we address the task of gas distribution modeling in scenarios where multiple heterogeneous compounds are present. Gas distribution modeling is particularly useful in emission monitoring applications where spatial representations of the gaseous patches can be used to identify emission hot spots. In realistic environments, the presence of multiple chemicals is expected and therefore, gas discrimination has to be incorporated in the modeling process. The approach presented in this work addresses the task of gas distribution modeling by combining different non selective gas sensors. Gas discrimination is addressed with an open sampling system, composed by an array of metal oxide sensors and a probabilistic algorithm tailored to uncontrolled environments. For each of the identified compounds, the mapping algorithm generates a calibrated gas distribution model using the classification uncertainty and the concentration readings acquired with a photo ionization detector. The meta parameters of the proposed modeling algorithm are automatically learned from the data. The approach was validated with a gas sensitive robot patrolling outdoor and indoor scenarios, where two different chemicals were released simultaneously. The experimental results show that the generated multi compound maps can be used to accurately predict the location of emitting gas sources. }, year = {2014} } @article{Andreasson780236, author = {Andreasson, Henrik and Saarinen, Jari and Cirillo, Marcello and Stoyanov, Todor and Lilienthal, Achim}, institution = {Örebro University, School of Science and Technology}, journal = {Robotics}, number = {4}, pages = {400--416}, publisher = {M D P I AG}, title = {Drive the Drive : From Discrete Motion Plans to Smooth Drivable Trajectories}, volume = {3}, DOI = {10.3390/robotics3040400}, keywords = {Motion planning, motion and path planning, autonomous navigation}, abstract = {Autonomous navigation in real-world industrial environments is a challenging task in many respects. One of the key open challenges is fast planning and execution of trajectories to reach arbitrary target positions and orientations with high accuracy and precision, while taking into account non-holonomic vehicle constraints. In recent years, lattice-based motion planners have been successfully used to generate kinematically and kinodynamically feasible motions for non-holonomic vehicles. However, the discretized nature of these algorithms induces discontinuities in both state and control space of the obtained trajectories, resulting in a mismatch between the achieved and the target end pose of the vehicle. As endpose accuracy is critical for the successful loading and unloading of cargo in typical industrial applications, automatically planned paths have not been widely adopted in commercial AGV systems. The main contribution of this paper is a path smoothing approach, which builds on the output of a lattice-based motion planner to generate smooth drivable trajectories for non-holonomic industrial vehicles. The proposed approach is evaluated in several industrially relevant scenarios and found to be both fast (less than 2 s per vehicle trajectory) and accurate (end-point pose errors below 0.01 m in translation and 0.005 radians in orientation). }, year = {2014} } @inproceedings{Krug780127, author = {Krug, Robert and Stoyanov, Todor and Bonilla, Manuel and Tincani, Vinicio and Vaskevicius, Narunas and Fantoni, Gualtiero and Birk, Andreas and Lilienthal, Achim and Bicchi, Antonio}, booktitle = {Workshop on Autonomous Grasping and Manipulation : An Open Challenge}, institution = {Örebro University, School of Science and Technology}, institution = {Faculty of Engineering, Interdepart. Research Center "Enrico Piaggio", University of Pisa, Pisa, Italy}, institution = {Faculty of Engineering, Interdepart. Research Center "Enrico Piaggio", University of Pisa, Pisa, Italy}, institution = {Faculty of Engineering, Interdepart. Research Center "Enrico Piaggio", University of Pisa, Pisa, Italy}, institution = {Faculty of Engineering, Interdepart. Research Center "Enrico Piaggio", University of Pisa, Pisa, Italy}, institution = {Faculty of Engineering, Interdepart. Research Center "Enrico Piaggio", University of Pisa, Pisa, Italy}, institution = {Faculty of Engineering, Interdepart. Research Center "Enrico Piaggio", University of Pisa, Pisa, Italy}, title = {Improving Grasp Robustness via In-Hand Manipulation with Active Surfaces}, keywords = {Grasping, Grasp Control, Grasp Planning}, year = {2014} } @article{Almqvist704809, author = {Almqvist, H{\aa}kan and Magnusson, Martin and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Journal of Intelligent and Robotic Systems}, number = {1}, pages = {101--128}, title = {Improving Point Cloud Accuracy Obtained from a Moving Platform for Consistent Pile Attack Pose Estimation}, volume = {75}, DOI = {10.1007/s10846-013-9957-9}, keywords = {3D perception, Autoloading, Scanning while moving}, abstract = {We present a perception system for enabling automated loading with waist-articulated wheel loaders. To enable autonomous loading of piled materials, using either above-ground wheel loaders or underground load-haul-dump vehicles, 3D data of the pile shape is needed. However, using common 3D scanners, the scan data is distorted while the wheel loader is moving towards the pile. Existing methods that make use of 3D scan data (for autonomous loading as well as tasks such as mapping, localisation, and object detection) typically assume that each 3D scan is accurate. For autonomous robots moving over rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one 3D scan, in which case the scan data will be distorted. We present a study of auto-loading methods, and how to locate piles in real-world scenarios with nontrivial ground geometry. We have compared how consistently each method performs for live scans acquired in motion, and also how the methods perform with different view points and scan configurations. The system described in this paper uses a novel method for improving the quality of distorted 3D scans made from a vehicle moving over uneven terrain. The proposed method for improving scan quality is capable of increasing the accuracy of point clouds without assuming any specific features of the environment (such as planar walls), without resorting to a “stop-scan-go” approach, and without relying on specialised and expensive hardware. Each new 3D scan is registered to the preceding using the normal-distributions transform (NDT). After each registration, a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. To verify the impact of the quality improvement, we present data that shows how auto-loading methods benefit from the corrected scans. The presented methods are validated on data from an autonomous wheel loader, as well as with simulated data. The proposed scan-correction method increases the accuracy of both the vehicle trajectory and the point cloud. We also show that it increases the reliability of pile-shape measures used to plan an efficient attack pose when performing autonomous loading. }, year = {2014} } @inproceedings{Valencia780074, author = {Valencia, Rafael and Saarinen, Jari and Andreasson, Henrik and Vallv{\’e;}, Joan and Andrade-Cetto, Juan and Lilienthal, Achim J.}, booktitle = {2014 IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, institution = {CSIC-UPC, Barcelona,Spain}, institution = {CSIC-UPC, Barcelona, Spain}, note = {Institut de Robòtica i Informàtica industrial - UPC, Joint Research Center of the Technical University of Catalonia (UPC) and the Spanish Council for Scientific Research (CSIC) focused on robotics research}, pages = {3956--3962}, title = {Localization in highly dynamic environments using dual-timescale NDT-MCL}, series = {Proceedings - IEEE International Conference on Robotics and Automation}, DOI = {10.1109/ICRA.2014.6907433}, keywords = {Localization, Monte Carlo Localization, Intra Logistics, Mapping}, abstract = {Industrial environments are rarely static and oftentheir configuration is continuously changing due to the materialtransfer flow. This is a major challenge for infrastructure freelocalization systems. In this paper we address this challengeby introducing a localization approach that uses a dualtimescaleapproach. The proposed approach - Dual-TimescaleNormal Distributions Transform Monte Carlo Localization (DTNDT-MCL) - is a particle filter based localization method,which simultaneously keeps track of the pose using an aprioriknown static map and a short-term map. The short-termmap is continuously updated and uses Normal DistributionsTransform Occupancy maps to maintain the current state ofthe environment. A key novelty of this approach is that it doesnot have to select an entire timescale map but rather use thebest timescale locally. The approach has real-time performanceand is evaluated using three datasets with increasing levels ofdynamics. We compare our approach against previously proposedNDT-MCL and commonly used SLAM algorithms andshow that DT-NDT-MCL outperforms competing algorithmswith regards to accuracy in all three test cases. }, year = {2014} } @inproceedings{Vaskevicius772382, author = {Vaskevicius, N. and Mueller, C. A. and Bonilla, M. and Tincani, V. and Stoyanov, Todor and Fantoni, G. and Pathak, K. and Lilienthal, Achim J. and Bicchi, A. and Birk, A.}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {Jacobs University, Bremen, Germany}, institution = {Jacobs University, Bremen, Germany}, institution = {University of Pisa, Pisa, Italy}, institution = {University of Pisa, Pisa, Italy}, institution = {University of Pisa, Pisa, Italy}, institution = {Jacobs University, Bremen, Germany}, institution = {University of Pisa, Pisa, Italy}, institution = {Jacobs University, Bremen, Germany}, pages = {1270--1277}, title = {Object recognition and localization for robust grasping with a dexterous gripper in the context of container unloading}, DOI = {10.1109/CoASE.2014.6899490}, keywords = {containers;control engineering computing;dexterous manipulators;goods distribution;grippers;industrial robots;logistics;object recognition;autonomous shipping-container unloading;dexterous gripper;object recognition;perception system;pose estimation errors;table-top scenarios;Educational institutions;Grasping;Grippers;Robot sensing systems;Thumb}, abstract = {The work presented here is embedded in research on an industrial application scenario, namely autonomous shipping-container unloading, which has several challenging constraints: the scene is very cluttered, objects can be much larger than in common table-top scenarios; the perception must be highly robust, while being as fast as possible. These contradicting goals force a compromise between speed and accuracy. In this work, we investigate a state of the art perception system integrated with a dexterous gripper. In particular, we are interested in pose estimation errors from the recognition module and whether these errors can be handled by the abilities of the gripper. }, year = {2014} } @article{HernandezBennetts676766, author = {Hernandez Bennetts, Victor and Trincavelli, Marco and Lilienthal, Achim J. and Schaffernicht, Erik}, institution = {Örebro University, School of Science and Technology}, journal = {Sensor Letters}, number = {6-7}, pages = {1147--1151}, title = {Online parameter selection for gas distribution mapping}, volume = {12}, DOI = {10.1166/sl.2014.3191}, keywords = {BANDWIDTH SELECTION; GAS DISTRIBUTION MAPPING; VIRTUAL LEAVE-ONE-OUT CROSS VALIDATION}, abstract = {The ability to produce truthful maps of the distribution of one or more gases is beneficial for applications ranging from environmental monitoring to mines and industrial plants surveillance. Realistic environments are often too complicated for applying analytical gas plume models or performing reliable CFD simulations, making data-driven statistical gas distribution models the most attractive alternative. However, statistical models for gas distribution modelling, often rely on a set of meta-parameters that need to be learned from the data through Cross Validation (CV) techniques. CV techniques are computationally expensive and therefore need to be computed offline. As a faster alternative, we propose a parameter selection method based on Virtual Leave-One-Out Cross Validation (VLOOCV) that enables online learning of meta-parameters. In particular, we consider the Kernel DM+V, one of the most well studied algorithms for statistical gas distribution mapping, which relies on a meta-parameter, the kernel bandwidth. We validate the proposed VLOOCV method on a set of indoor and outdoor experiments where a mobile robot with a Photo Ionization Detector (PID) was collecting gas measurements. The approximation provided by the proposed VLOOCV method achieves very similar results to plain Cross Validation at a fraction of the computational cost. This is an important step in the development of on-line statistical gas distribution modelling algorithms. }, year = {2014} } @inproceedings{Mojtahedzadeh778503, author = {Mojtahedzadeh, Rasoul and Bouguerra, Abdelbaki and Schaffernicht, Erik and Lilienthal, Achim J.}, booktitle = {Robotics and Automation (ICRA), 2014 IEEE International Conference on : }, institution = {Örebro University, School of Science and Technology}, pages = {5685--5690}, title = {Probabilistic Relational Scene Representation and Decision Making Under Incomplete Information for Robotic Manipulation Tasks}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ICRA.2014.6907695}, keywords = {Containers, Manipulators, Industrial Robots, Object Detection, Support Vector Machines, Decision Making}, abstract = {In this paper, we propose an approach for robotic manipulation systems to autonomously reason about their environments under incomplete information. The target application is to automate the task of unloading the content of shipping containers. Our goal is to capture possible support relations between objects in partially known static configurations. We employ support vector machines (SVM) to estimate the probability of a support relation between pairs of detected objects using features extracted from their geometrical properties and 3D sampled points of the scene. The set of probabilistic support relations is then used for reasoning about optimally selecting an object to be unloaded first. The proposed approach has been extensively tested and verified on data sets generated in simulation and from real world configurations. }, ISBN = {978-1-4799-3685-4}, year = {2014} } @inproceedings{Bennetts1072051, author = {Bennetts, Victor Hernandez and Schaffernicht, Erik and Stoyanov, Todor and Lilienthal, Achim J. and Trincavelli, Marco}, booktitle = {2014 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) : }, institution = {Örebro University, School of Science and Technology}, pages = {6362--6367}, title = {Robot Assisted Gas Tomography - Localizing Methane Leaks in Outdoor Environments}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ICRA.2014.6907798}, abstract = {In this paper we present an inspection robot to produce gas distribution maps and localize gas sources in large outdoor environments. The robot is equipped with a 3D laser range finder and a remote gas sensor that returns integral concentration measurements. We apply principles of tomography to create a spatial gas distribution model from integral gas concentration measurements. The gas distribution algorithm is framed as a convex optimization problem and it models the mean distribution and the fluctuations of gases. This is important since gas dispersion is not an static phenomenon and furthermore, areas of high fluctuation can be correlated with the location of an emitting source. We use a compact surface representation created from the measurements of the 3D laser range finder with a state of the art mapping algorithm to get a very accurate localization and estimation of the path of the laser beams. In addition, a conic model for the beam of the remote gas sensor is introduced. We observe a substantial improvement in the gas source localization capabilities over previous state-of-the-art in our evaluation carried out in an open field environment. }, ISBN = {978-1-4799-3685-4}, year = {2014} } @inproceedings{HernandezBennetts748476, author = {Hernandez Bennetts, Victor and Schaffernicht, Erik and Stoyanov, Todor and Lilienthal, Achim J. and Trincavelli, Marco}, booktitle = {Workshop on Robot Monitoring : }, institution = {Örebro University, School of Science and Technology}, title = {Robot assisted gas tomography : an alternative approach for the detection of fugitive methane emissions}, abstract = {Methane (CH4) based combustibles, such as Natural Gas (NG) and BioGas (BG), are considered bridge fuels towards a decarbonized global energy system. NG emits less CO2 during combustion than other fossil fuels and BG can be produced from organic waste. However, at BG production sites, leaks are common and CH4 can escape through fissures in pipes and insulation layers. While by regulation BG producers shall issue monthly CH4 emission reports, measurements are sparsely collected, only at a few predefined locations. Due to the high global warming potential of CH4, efficient leakage detection systems are critical. We present a robotics approach to localize CH4 leaks. In Robot assisted Gas Tomography (RGT), a mobile robot is equipped with remote gas sensors to create gas distribution maps, which can be used to infer the location of emitting sources. Spectroscopy based remote gas sensors report integral concentrations, which means that the measurements are spatially unresolved, with neither information regarding the gas distribution over the optical path nor the length of the s beam. Thus, RGT fuses different sensing modalities, such as range sensors for robot localization and ray tracing, in order to infer plausible gas distribution models that explain the acquired integral concentration measurements. }, year = {2014} } @article{Pashami758138, author = {Pashami, Sepideh and Lilienthal, Achim J. and Schaffernicht, Erik and Trincavelli, Marco}, institution = {Örebro University, School of Science and Technology}, journal = {Sensor Letters}, number = {6/7}, pages = {1123--1127}, publisher = {American Scientific Publishers}, title = {rTREFEX: Reweighting norms for detecting changes in the response of MOX gas sensors}, volume = {12}, DOI = {10.1166/sl.2014.3170}, keywords = {MOX Sensor, Open Sampling System, Change Point Detection, Reweighted Norm Minimization}, abstract = { The detection of changes in the response of metal oxide (MOX) gas sensors deployed in an open sampling system is a hard problem. It is relevant for applications such as gas leak detection in mines or large-scale pollution monitoring where it is impractical to continuously store or transfer sensor readings and reliable calibration is hard to achieve. Under these circumstances, it is desirable to detect points in the signal where a change indicates a significant event, e.g. the presence of gas or a sudden change of concentration. The key idea behind the proposed change detection approach is that a change in the emission modality of a gas source appears locally as an exponential function in the response of MOX sensors due to their long response and recovery times. The algorithm proposed in this paper, rTREFEX, is an extension of the previously proposed TREFEX algorithm. rTREFEX interprets the sensor response by fitting piecewise exponential functions with different time constants for the response and recovery phase. The number of exponentials, which has to be kept as low as possible, is determined automatically using an iterative approach that solves a sequence of convex optimization problems based on l1-norm. The algorithm is evaluated with an experimental setup where a gas source changes in intensity, compound, and mixture ratio, and the gas source is delivered to the sensors exploiting natural advection and turbulence mechanisms. rTREFEX is compared against the previously proposed TREFEX, which already proved superior to other algorithms. }, year = {2014} } @inproceedings{Krug696464, author = {Krug, Robert and Stoyanov, Todor and Bonilla, Manuel and Tincani, Vinicio and Vaskevicius, Narunas and Fantoni, Gualtiero and Birk, Andreas and Lilienthal, Achim J. and Bicchi, Antonio}, booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, institution = {Interdepart. Research Center “E. Piaggio”, University of Pisa, Pisa, Italy}, institution = {Interdepart. Research Center “E. Piaggio”, University of Pisa, Pisa, Italy}, institution = {Robotics Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany}, institution = {Interdepart. Research Center “E. Piaggio”, University of Pisa, Pisa, Italy}, institution = {Robotics Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany}, institution = {Interdepart. Research Center “E. Piaggio”, University of Pisa, Pisa, Italy}, pages = {3669--3675}, title = {Velvet fingers : grasp planning and execution for an underactuated gripper with active surfaces}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ICRA.2014.6907390}, keywords = {Grasp Planning, Grasp Control, Underactuation}, abstract = {In this work we tackle the problem of planning grasps for an underactuated gripper which enable it to retrieve target objects from a cluttered environment. Furthermore,we investigate how additional manipulation capabilities of the gripping device, provided by active surfaces on the inside of the fingers, can lead to performance improvement in the grasp execution process. To this end, we employ a simple strategy, in which the target object is ‘pulled-in’ towards the palm during grasping which results in firm enveloping grasps. We show the effectiveness of the suggested methods by means of experiments conducted in a real-world scenario. }, ISBN = {978-1-4799-3685-4}, year = {2014} } @article{Saarinen644380, author = {Saarinen, Jari and Andreasson, Henrik and Stoyanov, Todor and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {The international journal of robotics research}, note = {Funding agency:Kunskaps och Kompetensutveckling Stiftelsen project SAUNA 20100315}, number = {14}, pages = {1627--1644}, title = {3D normal distributions transform occupancy maps : an efficient representation for mapping in dynamic environments}, volume = {32}, DOI = {10.1177/0278364913499415}, abstract = {In order to enable long-term operation of autonomous vehicles in industrial environments numerous challenges need to be addressed. A basic requirement for many applications is the creation and maintenance of consistent 3D world models. This article proposes a novel 3D spatial representation for online real-world mapping, building upon two known representations: normal distributions transform (NDT) maps and occupancy grid maps. The proposed normal distributions transform occupancy map (NDT-OM) combines the advantages of both representations; compactness of NDT maps and robustness of occupancy maps. One key contribution in this article is that we formulate an exact recursive updates for NDT-OMs. We show that the recursive update equations provide natural support for multi-resolution maps. Next, we describe a modification of the recursive update equations that allows adaptation in dynamic environments. As a second key contribution we introduce NDT-OMs and formulate the occupancy update equations that allow to build consistent maps in dynamic environments. The update of the occupancy values are based on an efficient probabilistic sensor model that is specially formulated for NDT-OMs. In several experiments with a total of 17 hours of data from a milk factory we demonstrate that NDT-OMs enable real-time performance in large-scale, long-term industrial setups. }, year = {2013} } @inproceedings{Blanco676862, author = {Blanco, Jose Luis and Monroy, Javier G. and Gonzalez-Jimenez, Javier and Lilienthal, Achim J.}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {University of Màlaga, Màlaga, Spain}, institution = {University of Màlaga, Màlaga, Spain}, institution = {University of Màlaga, Màlaga, Spain}, note = {© ACM, YYYY. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is published in 28th ACM Symposium on Applied Computing (SAC 2013), 2013} http://doi.acm.org/10.1145/2480362.2480409"}, pages = {217--222}, title = {A Kalman Filter Based Approach To Probabilistic Gas Distribution Mapping}, DOI = {10.1145/2480362.2480409}, keywords = {Kalman Filter, Gas Distribution Mapping, Mobile Olfaction}, abstract = {Building a model of gas concentrations has important indus-trial and environmental applications, and mobile robots ontheir own or in cooperation with stationary sensors play animportant role in this task. Since an exact analytical de-scription of turbulent flow remains an intractable problem,we propose an approximate approach which not only esti-mates the concentrations but also their variances for eachlocation. Our point of view is that of sequential Bayesianestimation given a lattice of 2D cells treated as hidden vari-ables. We first discuss how a simple Kalman filter pro-vides a solution to the estimation problem. To overcomethe quadratic computational complexity with the mappedarea exhibited by a straighforward application of Kalmanfiltering, we introduce a sparse implementation which runsin constant time. Experimental results for a real robot vali-date the proposed method. }, ISBN = {9781450316569}, year = {2013} } @inproceedings{Neumann644432, author = {Neumann, Patrick and Schn{\"u}rmacher, Michael and Hernandez Bennetts, Victor and Lilienthal, Achim J. and Bartholmai, Matthias and Schiller, Jochen}, booktitle = {Proceedings of the 15th ISOEN : }, institution = {Örebro University, School of Science and Technology}, institution = {BAM Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {BAM Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {BAM Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {BAM Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, title = {A Probabilistic Gas Patch Prediction Approach for Airborne Gas Source Localization in Non-Uniform Wind Fields}, year = {2013} } @inproceedings{Pashami645513, author = {Pashami, Sepideh and Lilienthal, Achim J. and Trincavelli, Marco}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, title = {A trend filtering approach for change point detection in MOX gas sensors}, keywords = {MOX sensor; open sampling system; change point detection; trend filtering}, abstract = {Detecting changes in the response of metal oxide (MOX) gas sensors deployed in an open sampling system is a hard problem. It is relevant for applicationssuch as gas leak detection in coal mines[1],[2] or large scale pollution monitoring [3],[4] where it is unpractical to continuously store or transfer sensor readings and reliable calibration is hard to achieve. Under these circumstances it is desirable to detect points in the signal where a change indicates a significant event, e.g. the presence of gas or a sudden change of concentration. The key idea behind the proposed change detection approach isthat a change in the emission modality of a gas source appears locally as an exponential function in the response of MOX sensors due to their long response and recovery times. The proposed method interprets the sensor responseby fitting piecewise exponential functions with different time constants for the response and recovery phase. The number of exponentials is determined automatically using an approximate method based on the L1-norm. This asymmetric exponential trend filtering problem is formulated as a convex optimization problem, which is particularly advantageous from the computational point of view. The algorithm is evaluated with an experimental setup where a gas source changes in intensity, compound, and mixture ratio, and it is compared against the previously proposed Generalized Likelihood Ratio (GLR) based algorithm [6]. }, year = {2013} } @inproceedings{Mojtahedzadeh698571, author = {Mojtahedzadeh, Rasoul and Stoyanov, Todor and Lilienthal, Achim J.}, booktitle = {Proceedings of the European Conference on Mobile Robots (ECMR) : }, institution = {Örebro University, School of Science and Technology}, pages = {313--318}, title = {Application Based 3D Sensor Evaluation : A Case Study in 3D Object Pose Estimation for Automated Unloading of Containers}, DOI = {10.1109/ECMR.2013.6698860}, abstract = {A fundamental task in the design process of a complex system that requires 3D visual perception is the choice of suitable 3D range sensors. Identifying the utility of 3D range sensors in an industrial application solely based on an evaluation of their distance accuracy and the noise level may lead to an inappropriate selection. To assess the actual effect on the performance of the system as a whole requires a more involved analysis. In this paper, we examine the problem of selecting a set of 3D range sensors when designing autonomous systems for specific industrial applications in a holistic manner. As an instance of this problem we present a case study with an experimental evaluation of the utility of four 3D range sensors for object pose estimation in the process of automation of unloading containers. }, year = {2013} } @inproceedings{Mojtahedzadeh664340, author = {Mojtahedzadeh, Rasoul and Bouguerra, Abdelbaki and Lilienthal, Achim J.}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, pages = {1335--1340}, title = {Automatic relational scene representation for safe robotic manipulation tasks}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2013.6696522}, abstract = {In this paper, we propose a new approach forautomatically building symbolic relational descriptions of staticconfigurations of objects to be manipulated by a robotic system.The main goal of our work is to provide advanced cognitiveabilities for such robotic systems to make them more aware ofthe outcome of their actions. We describe how such symbolicrelations are automatically extracted for configurations ofbox-shaped objects using notions from geometry and staticequilibrium in classical mechanics. We also present extensivesimulation results as well as some real-world experiments aimedat verifying the output of the proposed approach. }, ISBN = {978-1-4673-6358-7}, year = {2013} } @inproceedings{Pomareda646007, author = {Pomareda, Victor and Hernandez Bennetts, Victor and Abdul Khaliq, Ali and Trincavelli, Marco and Lilienthal, Achim J. and Marco, Santiago}, booktitle = {Proceedings of the 15th International Symposium on Olfaction and Electronic Nose (ISOEN 2013) : }, institution = {Örebro University, School of Science and Technology}, institution = {Intelligent Signal Processing, Department of Electronics, University of Barcelona, Barcelona, Spain}, institution = {Intelligent Signal Processing, Department of Electronics, University of Barcelona, Barcelona, Spain}, title = {Chemical source localization in real environments integrating chemical concentrations in a probabilistic plume mapping approach}, keywords = {chemical plume source localization, Bayesian inference, chemical concentration, mobile robots, real environmen ts}, abstract = {Chemical plume source localization algorithms can be classified either as reactive plume tracking or gas distribution mapping approaches. Here, we focus on gas distribution mapping methods where the robot does not need to track the plume to find the source and can be used for other tasks. Probabilistic mapping approaches have been previously applied to real-world data successfully; e.g., in the approach proposed by Pang and Farrell. Instead of the quasi-continuous gas measurement values, this algorithm considers events (detections and non-detections) based on whether the sensor response is above or below a threshold to update recursively a source probability grid map; thus, discarding important information. We developed an extension of this event-based approach, integrating chemical concentrations directly instead of binary information. In this work, both algorithms are compared using real-world data obtained from a photo-ionization detector (PID), a non-selective gas sensor, and an anemometer in real environments. We validate simulation results and demonstrate that the concentration-based approach is more accurate in terms of a higher probability at the ground truth source location, a smaller distance between the probability maximum and the source location, and a more peaked probability distribution, measured in terms of the overall entropy. }, year = {2013} } @article{Stoyanov618586, author = {Stoyanov, Todor and Mojtahedzadeh, Rasoul and Andreasson, Henrik and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Robotics and Autonomous Systems}, number = {10}, pages = {1094--1105}, title = {Comparative evaluation of range sensor accuracy for indoor mobile robotics and automated logistics applications}, volume = {61}, DOI = {10.1016/j.robot.2012.08.011}, abstract = {3D range sensing is an important topic in robotics, as it is a component in vital autonomous subsystems such as for collision avoidance, mapping and perception. The development of affordable, high frame rate and precise 3D range sensors is thus of considerable interest. Recent advances in sensing technology have produced several novel sensors that attempt to meet these requirements. This work is concerned with the development of a holistic method for accuracy evaluation of the measurements produced by such devices. A method for comparison of range sensor output to a set of reference distance measurements, without using a precise ground truth environment model, is proposed. This article presents an extensive evaluation of three novel depth sensors — the Swiss Ranger SR-4000, Fotonic B70 and Microsoft Kinect. Tests are concentrated on the automated logistics scenario of container unloading. Six different setups of box-, cylinder-, and sack-shaped goods inside a mock-up container are used to collect range measurements. Comparisons are performed against hand-crafted ground truth data, as well as against a reference actuated Laser Range Finder (aLRF) system. Additional test cases in an uncontrolled indoor environment are performed in order to evaluate the sensors’ performance in a challenging, realistic application scenario. }, year = {2013} } @article{Stoyanov618700, author = {Stoyanov, Todor and Magnusson, Martin and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Journal of Field Robotics}, number = {2}, pages = {216--236}, title = {Comparative evaluation of the consistency of three-dimensional spatial representations used in autonomous robot navigation}, volume = {30}, DOI = {10.1002/rob.21446}, abstract = {An increasing number of robots for outdoor applications rely on complex three-dimensional (3D) environmental models. In many cases, 3D maps are used for vital tasks, such as path planning and collision detection in challenging semistructured environments. Thus, acquiring accurate three-dimensional maps is an important research topic of high priority for autonomously navigating robots. This article proposes an evaluation method that is designed to compare the consistency with which different representations model the environment. In particular, the article examines several popular (probabilistic) spatial representations that are capable of predicting the occupancy of any point in space, given prior 3D range measurements. This work proposes to reformulate the obtained environmental models as probabilistic binary classifiers, thus allowing for the use of standard evaluation and comparison procedures. To avoid introducing localization errors, this article concentrates on evaluating models constructed from measurements acquired at fixed sensor poses. Using a cross-validation approach, the consistency of different representations, i.e., the likelihood of correctly predicting unseen measurements in the sensor field of view, can be evaluated. Simulated and real-world data sets are used to benchmark the precision of four spatial models—occupancy grid, triangle mesh, and two variations of the three-dimensional normal distributions transform (3D-NDT)—over various environments and sensor noise levels. Overall, the consistency of representation of the 3D-NDT is found to be the highest among the tested models, with a similar performance over varying input data. }, year = {2013} } @inproceedings{Kucner664130, author = {Kucner, Tomasz and Sarinen, Jari and Magnusson, Martin and Lilienthal, Achim J.}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems : }, institution = {Örebro University, School of Science and Technology}, institution = {Aalto university, Helsinki, Finland}, pages = {1196--1201}, title = {Conditional transition maps: learning motion patterns in dynamic environments}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2013.6696502}, keywords = {Mapping, Navigation}, abstract = {In this paper we introduce a method for learning motion patterns in dynamic environments. Representations of dynamic environments have recently received an increasing amount of attention in the research community. Understanding dynamic environments is seen as one of the key challenges in order to enable autonomous navigation in real-world scenarios. However, representing the temporal dimension is a challenge yet to be solved. In this paper we introduce a spatial representation, which encapsulates the statistical dynamic behavior observed in the environment. The proposed Conditional Transition Map (CTMap) is a grid-based representation that associates a probability distribution for an object exiting the cell, given its entry direction. The transition parameters are learned from a temporal signal of occupancy on cells by using a local-neighborhood cross-correlation method. In this paper, we introduce the CTMap, the learning approach and present a proof-of-concept method for estimating future paths of dynamic objects, called Conditional Probability Propagation Tree (CPPTree). The evaluation is done using a real-world data-set collected at a busy roundabout. }, ISBN = {978-1-4673-6357-0}, year = {2013} } @inproceedings{Saarinen644375, author = {Saarinen, Jari and Stoyanov, Todor and Andreasson, Henrik and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, note = {to appear}, pages = {4694--4701}, title = {Fast 3D mapping in highly dynamic environments using normal distributions transform occupancy maps}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2013.6697032}, ISBN = {978-1-4673-6358-7}, year = {2013} } @article{Neumann644489, author = {Neumann, Patrick and Hernandez Bennetts, Victor and Lilienthal, Achim J. and Bartholmai, Matthias and Schiller, Jochen H.}, institution = {Örebro University, School of Science and Technology}, institution = {Federal Institute for Materials Research and Testing (BAM), Berlin, Germany}, institution = {Federal Institute for Materials Research and Testing (BAM), Berlin, Germany}, institution = {Institute of Computer Science, Freie Universität, Berlin, Germany}, journal = {Advanced Robotics}, number = {9}, pages = {725--738}, title = {Gas source localization with a micro-drone using bio-inspired and particle filter-based algorithms}, volume = {27}, DOI = {10.1080/01691864.2013.779052}, keywords = {autonomous micro UAV; chemical and wind sensing; gas source localization; particle filter}, abstract = {Gas source localization (GSL) with mobile robots is a challenging task due to the unpredictable nature of gas dispersion,the limitations of the currents sensing technologies, and the mobility constraints of ground-based robots. This work proposesan integral solution for the GSL task, including source declaration. We present a novel pseudo-gradient-basedplume tracking algorithm and a particle filter-based source declaration approach, and apply it on a gas-sensitivemicro-drone. We compare the performance of the proposed system in simulations and real-world experiments againsttwo commonly used tracking algorithms adapted for aerial exploration missions. }, year = {2013} } @inproceedings{Canelhas644372, author = {Canelhas, Daniel R. and Stoyanov, Todor and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, pages = {3203--3209}, title = {Improved local shape feature stability through dense model tracking}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2013.6696811}, abstract = {In this work we propose a method to effectively remove noise from depth images obtained with a commodity structured light sensor. The proposed approach fuses data into a consistent frame of reference over time, thus utilizing prior depth measurements and viewpoint information in the noise removal process. The effectiveness of the approach is compared to two state of the art, single-frame denoising methods in the context of feature descriptor matching and keypoint detection stability. To make more general statements about the effect of noise removal in these applications, we extend a method for evaluating local image gradient feature descriptors to the domain of 3D shape descriptors. We perform a comparative study of three classes of such descriptors: Normal Aligned Radial Features, Fast Point Feature Histograms and Depth Kernel Descriptors; and evaluate their performance on a real-world industrial application data set. We demonstrate that noise removal enabled by the dense map representation results in major improvements in matching across all classes of descriptors as well as having a substantial positive impact on keypoint detection reliability }, ISBN = {978-1-4673-6358-7}, year = {2013} } @inproceedings{Almqvist644368, author = {Almqvist, H{\aa}kan and Magnusson, Martin and Stoyanov, Todor and Lilienthal, Achim J.}, booktitle = {2013 IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, pages = {733--738}, title = {Improving Point-Cloud Accuracy from a Moving Platform in Field Operations}, DOI = {10.1109/ICRA.2013.6630654}, abstract = {This paper presents a method for improving the quality of distorted 3D point clouds made from a vehicle equipped with a laser scanner moving over uneven terrain. Existing methods that use 3D point-cloud data (for tasks such as mapping, localisation, and object detection) typically assume that each point cloud is accurate. For autonomous robots moving in rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one point cloud, in which case the data will be distorted. The method proposed in this paper is capable of increasing the accuracy of 3D point clouds, without assuming any specific features of the environment (such as planar walls), without resorting to a "stop-scan-go" approach, and without relying on specialised and expensive hardware. Each new point cloud is matched to the previous using normal-distribution-transform (NDT) registration, after which a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. The proposed method increases the accuracy of both the measured platform trajectory and the point cloud. The method is validated on both real-world and simulated data. }, ISBN = {978-1-4673-5641-1}, ISBN = {978-1-4673-5643-5}, year = {2013} } @inproceedings{Lilienthal646005, author = {Lilienthal, Achim J. and Trincavelli, Marco and Schaffernicht, Erik}, booktitle = {Proceedings of the 15th International Symposium on Olfaction and Electronic Nose (ISOEN 2013) : }, institution = {Örebro University, School of Science and Technology}, title = {It's always smelly around here! Modeling the Spatial Distribution of Gas Detection Events with BASED Grid Maps}, keywords = {Gas Distribution Mapping, Bayesian Statistical Modeling, Beta Distribution}, abstract = {In this paper we introduce a novel gas distribution mapping algorithm, Bayesian Spatial Event Distribution (BASED), that, instead of modeling the spatial distribution of the gas concentration, models the spatial distribution of events of detection and non-detection of a target gas. The proposed algorithm is based on the Bayesian inference framework and models the likelihood of events at a certain location with a Bernoulli distribution. In order to avoid overfitting a Bayesian approach is used with a beta distribution prior for the parameter u that governs the Bernoulli distribution. In this way, the posterior distribution maintains the same form of the prior, i.e. will be a beta distribution, enabling a simple approach for sequential learning. To learn a field of beta distributions, we discretize the inspection area into a grid map and extrapolate from local measurements using Gaussian kernels. We demonstrate the proposed algorithm for different sensors mounted on a mobile robot and show how qualitatively similar maps are obtained from very different gas sensors. }, year = {2013} } @article{Neumann644493, author = {Neumann, Patrick and Asadi, Sahar and Hernandez Bennetts, Victor and Lilienthal, Achim J. and Bartholmai, Matthias}, institution = {Örebro University, School of Science and Technology}, institution = { Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = { Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, journal = {Energy Procedia}, pages = {4182--4190}, title = {Monitoring of CCS areas using micro unmanned aerial vehicles (MUAVs)}, volume = {37}, DOI = {10.1016/j.egypro.2013.06.320}, keywords = {gas-sensitive micro-drone; gas distribution mapping; sensor planning; artificial potential field; CCS}, abstract = {Carbon capture & storage (CCS) is one of the most promis ing technologies for greenhouse gas (GHG) management.However, an unsolved issue of CCS is the development of appropriate long-term monitoring systems for leakdetection of the stored CO2. To complement already existing monitoring infrastructure for CO2 storage areas, and toincrease the granularity of gas concentration measurements, a quickly deployab le, mobile measurement device isneeded. In this paper, we present an autonomous gas-sensitive micro-drone, which can be used to monitor GHGemissions, more specifically, CO2. Two different measurement strategies are proposed to address this task. First, theuse of predefined sensing trajectories is evaluated for the task of gas distribution mapping using the micro-drone.Alternatively, we present an adaptive strategy, which suggests sampling points based on an artific ial potential field(APF). The results of real-world experiments demonstrate the feas ibility of using gas-sensitive micro-drones for GHG monitoring missions. Thus, we suggest a multi-layered surveillance system for CO2 storage areas. }, year = {2013} } @inproceedings{Mosberger684470, author = {Mosberger, Rafael and Andreasson, Henrik and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, pages = {638--644}, title = {Multi-human Tracking using High-visibility Clothing for Industrial Safety}, series = {Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on}, DOI = {10.1109/IROS.2013.6696418}, keywords = {Human Detection, Robot Vision, Industrial Safety}, abstract = {We propose and evaluate a system for detecting and tracking multiple humans wearing high-visibility clothing from vehicles operating in industrial work environments. We use a customized stereo camera setup equipped with IR flash and IR filter to detect the reflective material on the worker's garments and estimate their trajectories in 3D space. An evaluation in two distinct industrial environments with different degrees of complexity demonstrates the approach to be robust and accurate for tracking workers in arbitrary body poses, under occlusion, and under a wide range of different illumination settings. }, year = {2013} } @inproceedings{Saarinen644376, author = {Saarinen, Jari and Andreasson, Henrik and Stoyanov, Todor and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, note = {to appear}, pages = {382--389}, title = {Normal distributions transform monte-carlo localization (NDT-MCL)}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2013.6696380}, ISBN = {978-1-4673-6358-7}, year = {2013} } @inproceedings{Stoyanov644379, author = {Stoyanov, Todor and Saarinen, Jari and Andreasson, Henrik and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, note = {to appear}, pages = {4702--4708}, title = {Normal distributions transform occupancy map fusion : simultaneous mapping and tracking in large scale dynamic environments}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2013.6697033}, ISBN = {978-1-4673-6358-7}, year = {2013} } @inproceedings{Saarinen622633, author = {Saarinen, Jari and Andreasson, Henrik and Stoyanov, Todor and Ala-Luhtala, Juha and Lilienthal, Achim J.}, booktitle = {IEEE International Conference on Robotics and Automation : }, institution = {Örebro University, School of Science and Technology}, institution = {Aalto University of Technology, Aalto, Finland}, pages = {2233--2238}, title = {Normal distributions transform occupancy maps : application to large-scale online 3D mapping}, DOI = {10.1109/ICRA.2013.6630878}, abstract = {Autonomous vehicles operating in real-world industrial environments have to overcome numerous challenges, chief among which is the creation and maintenance of consistent 3D world models. This paper proposes to address the challenges of online real-world mapping by building upon previous work on compact spatial representation and formulating a novel 3D mapping approach — the Normal Distributions Transform Occupancy Map (NDT-OM). The presented algorithm enables accurate real-time 3D mapping in large-scale dynamic nvironments employing a recursive update strategy. In addition, the proposed approach can seamlessly provide maps at multiple resolutions allowing for fast utilization in high-level functions such as localization or path planning. Compared to previous approaches that use the NDT representation, the proposed NDT-OM formulates an exact and efficient recursive update formulation and models the full occupancy of the map. }, year = {2013} } @inproceedings{HernandezBennetts644410, author = {Hernandez Bennetts, Victor and Trincavelli, Marco and Lilienthal, Achim J. and Pomadera Sese, Victor and Schaffernicht, Erik}, booktitle = {Proceedings of the ISOEN conference 2013 : }, institution = {Örebro University, School of Science and Technology}, institution = {Institute of Bioengineering of Catalonia, Barcelona, Spain}, title = {Online parameter selection for gas distribution mapping}, year = {2013} } @article{GonzàlezMonroy641025, author = {Gonzàlez Monroy, Javier and Lilienthal, Achim J. and Blanco, Jose Luis and Gonzàlez Jimenez, Javier and Trincavelli, Marco}, institution = {Örebro University, School of Science and Technology}, institution = {University of Málaga, Málaga, Spain}, institution = {University of Almería, Almería, Spain}, institution = {University of Málaga, Málaga, Spain}, journal = {Sensors and actuators. B, Chemical}, note = {Funding agency:Regional Government of Andalucia European Union (FEDER) P08-TEP-4016 }, pages = {298--312}, title = {Probabilistic gas quantification with MOX sensors in open sampling systems : a gaussian process approach}, volume = {188}, DOI = {10.1016/j.snb.2013.06.053}, keywords = {Gas quantification, Open Sampling System, MOX sensors, Gaussian Processes}, abstract = {Gas quantification based on the response of an array of metal oxide (MOX) gas sensors in an Open Sampling System is a complex problem due to the highly dynamic characteristic of turbulent airflow and the slow dynamics of the MOX sensors. However, many gas related applications require to determine the gas concentration the sensors are being exposed to. Due to the chaotic nature that dominates gas dispersal, in most cases it is desirable to provide, together with an estimate of the mean concentration, an estimate of the uncertainty of the prediction. This work presents a probabilistic approach for gas quantification with an array of MOX gas sensors based on Gaussian Processes, estimating for every measurement of the sensors a posterior distribution of the concentration, from which confidence intervals can be obtained. The proposed approach has been tested with an experimental setup where an array of MOX sensors and a Photo Ionization Detector (PID), used to obtain ground truth concentration, are placed downwind with respect to the gas source. Our approach has been implemented and compared with standard gas quantification methods, demonstrating the advantages when estimating gas concentrations. }, year = {2013} } @inproceedings{Canelhas644377, author = {Canelhas, Daniel R. and Stoyanov, Todor and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) : }, institution = {Örebro University, School of Science and Technology}, pages = {3671--3676}, title = {SDF tracker : a parallel algorithm for on-line pose estimation and scene reconstruction from depth images}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2013.6696880}, abstract = {Ego-motion estimation and environment mapping are two recurring problems in the field of robotics. In this work we propose a simple on-line method for tracking the pose of a depth camera in six degrees of freedom and simultaneously maintaining an updated 3D map, represented as a truncated signed distance function. The distance function representation implicitly encodes surfaces in 3D-space and is used directly to define a cost function for accurate registration of new data. The proposed algorithm is highly parallel and achieves good accuracy compared to state of the art methods. It is suitable for reconstructing single household items, workspace environments and small rooms at near real-time rates, making it practical for use on modern CPU hardware }, ISBN = {978-1-4673-6358-7}, year = {2013} } @article{Duckett664999, author = {Duckett, Tom and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Lincoln School of Computer Science, University of Lincoln, Lincoln, United Kingdom}, journal = {Robotics and Autonomous Systems}, number = {10}, pages = {1049--1050}, title = {Special Issue : Selected Papers from the 5th European Conference on Mobile Robots (ECMR 2011)}, volume = {61}, DOI = {10.1016/j.robot.2013.01.005}, year = {2013} } @inproceedings{HernandezBennetts741445, author = {Hernandez Bennetts, Victor Manuel and Lilienthal, Achim J. and Khaliq, Ali Abdul and Pomareda Sese, Victor and Trincavelli, Marco}, booktitle = {2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) : }, institution = {Örebro University, School of Science and Technology}, institution = {Institute of Bioengineering of Catalonia, Barcelona, Spain}, pages = {2335--2340}, title = {Towards Real-World Gas Distribution Mapping and Leak Localization Using a Mobile Robot with 3D and Remote Gas Sensing Capabilities}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ICRA.2013.6630893}, abstract = {Due to its environmental, economical and safety implications, methane leak detection is a crucial task to address in the biogas production industry. In this paper, we introduce Gasbot, a robotic platform that aims to automatize methane emission monitoring in landfills and biogas production sites. The distinctive characteristic of the Gasbot platform is the use of a Tunable Laser Absorption Spectroscopy (TDLAS) sensor. This sensor provides integral concentration measurements over the path of the laser beam. Existing gas distribution mapping algorithms can only handle local measurements obtained from traditional in-situ chemical sensors. In this paper we also describe an algorithm to generate 3D methane concentration maps from integral concentration and depth measurements. The Gasbot platform has been tested in two different scenarios: an underground corridor, where a pipeline leak was simulated and in a decommissioned landfill site, where an artificial methane emission source was introduced. }, year = {2013} } @article{Pashami625614, author = {Pashami, Sepideh and Lilienthal, Achim J. and Schaffernicht, Erik and Trincavelli, Marco}, institution = {Örebro University, School of Science and Technology}, journal = {Sensors}, number = {6}, pages = {7323--7344}, publisher = {MDPI AG}, title = {TREFEX : trend estimation and change detection in the response of mox gas sensors}, volume = {13}, DOI = {10.3390/s130607323}, keywords = {metal oxide sensors, open sampling system, change point detection, trend filtering}, abstract = {Many applications of metal oxide gas sensors can benefit from reliable algorithmsto detect significant changes in the sensor response. Significant changes indicate a changein the emission modality of a distant gas source and occur due to a sudden change ofconcentration or exposure to a different compound. As a consequence of turbulent gastransport and the relatively slow response and recovery times of metal oxide sensors,their response in open sampling configuration exhibits strong fluctuations that interferewith the changes of interest. In this paper we introduce TREFEX, a novel change pointdetection algorithm, especially designed for metal oxide gas sensors in an open samplingsystem. TREFEX models the response of MOX sensors as a piecewise exponentialsignal and considers the junctions between consecutive exponentials as change points. Weformulate non-linear trend filtering and change point detection as a parameter-free convexoptimization problem for single sensors and sensor arrays. We evaluate the performanceof the TREFEX algorithm experimentally for different metal oxide sensors and severalgas emission profiles. A comparison with the previously proposed GLR method shows aclearly superior performance of the TREFEX algorithm both in detection performance andin estimating the change time. }, year = {2013} } @inproceedings{Trincavelli617890, author = {Trincavelli, Marco and Hernandez Bennetts, Victor and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE Sensors Conference, 2012 : }, institution = {Örebro University, School of Science and Technology}, pages = {550--553}, title = {A Least Squares approach for learning gas distribution maps from a set of integral gas concentration measurements obtained with a TDLAS sensor}, series = {IEEE Sensors}, DOI = {10.1109/ICSENS.2012.6411118}, keywords = {Absorption, Gas lasers, Laser beams, Measurement by laser beam, Noise, Noise measurement, Robot sensing systems}, abstract = {Applications related to industrial plant surveillance and environmental monitoring often require the creation of gas distribution maps (GDM). In this paper an approach for creating a gas distribution map using a Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor and a laser range scanner mounted on a pan tilt unit is presented. The TDLAS sensor can remotely sense the target gas, in this case methane, requiring novel GDM algorithms compared to the ones developed for traditional in-situ chemical sensors. The presented setup makes it possible to create a 3D model of the environment and to calculate the path travelled by the TDLAS beam. The knowledge of the beam path is of crucial importance since a TDLAS sensor provides an integral measurement of the gas concentration over that path. An efficient GDM algorithm based on a quadratic programming formulation is proposed. The approach is tested in an indoor scenario where transparent bottles filled with methane are successfully localized. }, ISBN = {978-1-4577-1766-6}, year = {2012} } @article{Neumann524749, author = {Neumann, Patrick P. and Asadi, Sahar and Lilienthal, Achim J. and Bartholmai, Matthias and Schiller, Jochen H.}, institution = {Örebro University, School of Science and Technology}, institution = {BAM Federal Institute for Materials Research and Testing, Berlin, Germany}, institution = {Sensors and Measurement Systems Working Group, BAM Federal Institute for Materials Research and Testing, Berlin, Germany}, institution = {Computer Systems and Telematics Working Group, Institute of Computer Science, Freie Universität, Berlin, Germany}, journal = {IEEE robotics & automation magazine}, note = {Funding Agencies:European Commission FP7 224318BMWi 28/07}, number = {1}, pages = {50--61}, title = {Autonomous gas-sensitive microdrone wind vector estimation and gas distribution mapping}, volume = {19}, DOI = {10.1109/MRA.2012.2184671}, keywords = {Robot sensing systems, Real time systems, Gas detectors, Delta modulation, Mobile communication}, abstract = {This article presents the development and validation of an autonomous, gas sensitive microdrone that is capable of estimating the wind vector in real time using only the onboard control unit of the microdrone and performing gas distribution mapping (DM). Two different sampling approaches are suggested to address this problem. On the one hand, a predefined trajectory is used to explore the target area with the microdrone in a real-world gas DM experiment. As an alternative sampling approach, we introduce an adaptive strategy that suggests next sampling points based on an artificial potential field (APF). Initial results in real-world experiments demonstrate the capability of the proposed adaptive sampling strategy for gas DM and its use for gas source localization. }, year = {2012} } @inproceedings{Monroy618182, author = {Monroy, Javier G. and Lilienthal, Achim J. and Blanco, Jose Luis and Gonz{\’a;}lez-Jimenez, Javier and Trincavelli, Marco}, booktitle = {Proceedings of the IEEE Sensors Conference, 2012 : }, institution = {Örebro University, School of Science and Technology}, institution = {Dept. of System Engineering and Automation, University of Málaga, Spain}, institution = {Dept. of Civil Engineering, University of Málaga, Spain}, institution = {Dept. of System Engineering and Automation, University of Málaga, Spain}, pages = {1--4}, title = {Calibration of mox gas sensors in open sampling systems based on gaussian processes}, DOI = {10.1109/ICSENS.2012.6411464}, keywords = {Calibration, Estimation, Gas detectors, Microwave integrated circuits, Robot sensing systems, Training, Uncertainty}, abstract = {Calibration of metal oxide (MOX) gas sensor for continuous monitoring is a complex problem due to the highly dynamic characteristics of the gas sensor signal when exposed to natural environment (Open Sampling System - OSS). This work presents a probabilistic approach to the calibration of a MOX gas sensor based on Gaussian Processes (GP). The proposed approach estimates for every sensor measurement a probability distribution of the gas concentration. This enables the calculation of confidence intervals for the predicted concentrations. This is particularly important since exact calibration is hard to obtain due to the chaotic nature that dominates gas dispersal. The proposed approach has been tested with an experimental setup where an array of MOX sensors and a Photo Ionization Detector (PID) are placed downwind w.r.t. the gas source. The PID is used to obtain ground truth concentration. Comparison with standard calibration methods demonstrates the advantage of the proposed approach. }, ISBN = {9781457717659}, year = {2012} } @inproceedings{Pashami572463, author = {Pashami, Sepideh and Lilienthal, Achim J. and Trincavelli, Marco}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, title = {Change detection in an array of MOX sensors}, abstract = {In this article we present an algorithm for online detection of change points in the response of an array of metal oxide (MOX) gas sensors deployed in an open sampling system.True change points occur due to changes in the emission modality of the gas source. The main challenge for change point detection in an open sampling system is the chaotic nature of gas dispersion, which causes fluctuations in the sensor response that are not related to changes in the gas source. These fluctuations should not be considered change points in the sensor response. The presented algorithm is derived from the well known Generalized Likelihood Ratio algorithm and it is used both on the output of a single sensor as well on the output of two or more sensors on the array. The algorithm is evaluated with an experimental setup where a gas source changes in intensity, compound, or mixture ratio. The performance measures considered are the detection rate, the number of false alarms and the delay of detection. }, year = {2012} } @inproceedings{HernandezBennetts617886, author = {Hernandez Bennetts, Victor and Lilienthal, Achim J. and Trincavelli, Marco}, booktitle = {Sensors, 2012 IEEE : }, institution = {Örebro University, School of Science and Technology}, pages = {554--557}, title = {Creating true gas concentration maps in presence of multiple heterogeneous gas sources}, series = {IEEE Sensors}, DOI = {10.1109/ICSENS.2012.6411119}, keywords = {gas distribution mapping, mobile robots, environmental monitoring}, abstract = {Gas distribution mapping is a crucial task in emission monitoring and search and rescue applications. A common assumption made by state-of-the art mapping algorithms is that only one type of gaseous substance is present in the environment. For real world applications, this assumption can become very restrictive. In this paper we present an algorithm that creates gas concentration maps in a scenario where multiple heterogeneous gas sources are present. First, using an array of metal oxide (MOX) sensors and a pattern recognition algorithm, the chemical compound is identified. Then, for each chemical compound a gas concentration map using the readings of a Photo Ionization Detector (PID) is created. The proposed approach has been validated in experiments with the sensors mounted on a mobile robot which performed a predefined trajectory in a room where two gas sources emitting respectively ethanol and 2-propanol have been placed. }, ISBN = {978-1-4577-1766-6}, year = {2012} } @article{Pashami572461, author = {Pashami, Sepideh and Lilienthal, Achim J. and Trincavelli, Marco}, institution = {Örebro University, School of Science and Technology}, journal = {Sensors}, number = {12}, pages = {16404--16419}, publisher = {MDPI AG}, title = {Detecting changes of a distant gas source with an array of MOX gas sensors}, volume = {12}, DOI = {10.3390/s121216404}, keywords = {MOX sensor; open sampling system; sensor selection; change point detection}, abstract = {We address the problem of detecting changes in the activity of a distant gas source from the response of an array of metal oxide (MOX) gas sensors deployed in an open sampling system. The main challenge is the turbulent nature of gas dispersion and the response dynamics of the sensors. We propose a change point detection approach and evaluate it on individual gas sensors in an experimental setup where a gas source changes in intensity, compound, or mixture ratio. We also introduce an efficient sensor selection algorithm and evaluate the change point detection approach with the selected sensor array subsets. }, year = {2012} } @article{Stoyanov618701, author = {Stoyanov, Todor and Magnusson, Martin and Lilienthal, Achim J. and Andreasson, Henrik}, institution = {Örebro University, School of Science and Technology}, journal = {The international journal of robotics research}, note = {Funding Agencies:European Union FP7 - 270350Kunskaps och Kompetensutveckling Stiftelsen project SAUNA 20100315}, number = {12}, pages = {1377--1393}, title = {Fast and accurate scan registration through minimization of the distance between compact 3D NDT Representations}, volume = {31}, DOI = {10.1177/0278364912460895}, keywords = {point set registration; mapping; normal distributions transform}, abstract = {Registration of range sensor measurements is an important task in mobile robotics and has received a lot of attention. Several iterative optimization schemes have been proposed in order to align three-dimensional (3D) point scans. With the more widespread use of high-frame-rate 3D sensors and increasingly more challenging application scenarios for mobile robots, there is a need for fast and accurate registration methods that current state-of-the-art algorithms cannot always meet. This work proposes a novel algorithm that achieves accurate point cloud registration an order of a magnitude faster than the current state of the art. The speedup is achieved through the use of a compact spatial representation: the Three-Dimensional Normal Distributions Transform (3D-NDT). In addition, a fast, global-descriptor based on the 3D-NDT is defined and used to achieve reliable initial poses for the iterative algorithm. Finally, a closed-form expression for the covariance of the proposed method is also derived. The proposed algorithms are evaluated on two standard point cloud data sets, resulting in stable performance on a par with or better than the state of the art. The implementation is available as an open-source package for the Robot Operating system (ROS). }, year = {2012} } @inproceedings{HernandezBennetts1190204, author = {Hernandez Bennetts, Victor and Lilienthal, Achim and Khaliq, Ali Abdul and Pomareda Sese, Victor and Trincavelli, Marco}, booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Workshop on Robotics for Environmental Monitoring (WREM), Vilamoura, Portugal, October 7-12, 2012 : }, institution = {Örebro University, School of Science and Technology}, institution = {Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain}, institution = {Örebro University, Örebro, Sweden.}, title = {Gasbot : A Mobile Robotic Platform for Methane Leak Detection and Emission Monitoring}, keywords = {Mobile robot olfaction, remote sensor, landfill sites, gas tomography}, abstract = {Due to its environmental, economical and safety implications, methane leak detection is a crucial task to address in the biogas production industry. In this paper, we introduce Gasbot, a robotic platform that aims to automatize methane emission monitoring in landfills and biogas production sites. The distinctive characteristic of the Gasbot platform is the use of a Tunable Laser Absorption Spectroscopy (TDLAS) sensor, along with a novel gas distribution algorithm to generate methane concentration maps of indoor and outdoor exploration areas. The Gasbot platform has been tested in two different scenarios: an underground corridor, where a pipeline leak was simulated and in a decommissioned landfill site, where an artificial methane emission source was introduced. }, year = {2012} } @inproceedings{Saarinen1190203, author = {Saarinen, Jari and Andreasson, Henrik and Lilienthal, Achim}, booktitle = {2012 IEEE/RSJ International Conference on Intelligent Robots and Systems : }, institution = {Örebro University, School of Science and Technology}, institution = {Department of Automation and Systems Technology, Aalto University, Alto, Finland}, pages = {3489--3495}, title = {Independent Markov Chain Occupancy Grid Maps for Representation of Dynamic Environments}, series = {IEEE International Conference on Intelligent Robots and Systems}, DOI = {10.1109/IROS.2012.6385629}, keywords = {Markov chain, Poisson process, model of dynamics}, abstract = {In this paper we propose a new grid based approach to model a dynamic environment. Each grid cell is assumed to be an independent Markov chain (iMac) with two states. The state transition parameters are learned online and modeled as two Poisson processes. As a result, our representation not only encodes the expected occupancy of the cell, but also models the expected dynamics within the cell. The paper also presents a strategy based on recency weighting to learn the model parameters from observations that is able to deal with non-stationary cell dynamics. Moreover, an interpretation of the model parameters with discussion about the convergence rates of the cells is presented. The proposed model is experimentally validated using offline data recorded with a Laser Guided Vehicle (LGV) system running in production use. }, ISBN = {978-1-4673-1736-8}, ISBN = {978-1-4673-1737-5}, ISBN = {978-1-4673-1735-1}, year = {2012} } @article{HernandezBennetts524684, author = {Hernandez Bennetts, Victor and Lilienthal, Achim J. and Neumann, Patrick P. and Trincavelli, Marco}, institution = {Örebro University, School of Science and Technology}, institution = {BAM Federal Institute for Materials Research and Testing, Berlin, Germany}, journal = {Frontiers in Neuroengineering}, number = {20}, pages = {1--12}, title = {Mobile robots for localizing gas emission sources on landfill sites : is bio-inspiration the way to go?}, volume = {4}, DOI = {10.3389/fneng.2011.00020}, keywords = {Mobile Robotics, Mobile Robot Olfaction, Landfill Surveillance, Biologically Inspired Robots}, abstract = {Roboticists often take inspiration from animals for designing sensors, actuators, or algorithms that control the behavior of robots. Bio-inspiration is motivated with the uncanny ability of animals to solve complex tasks like recognizing and manipulating objects, walking on uneven terrains, or navigating to the source of an odor plume. In particular the task of tracking an odor plume up to its source has nearly exclusively been addressed using biologically inspired algorithms and robots have been developed, for example, to mimic the behavior of moths, dung beetles, or lobsters. In this paper we argue that biomimetic approaches to gas source localization are of limited use, primarily because animals differ fundamentally in their sensing and actuation capabilities from state-of-the-art gas-sensitive mobile robots. To support our claim, we compare actuation and chemical sensing available to mobile robots to the corresponding capabilities of moths. We further characterize airflow and chemosensor measurements obtained with three different robot platforms (two wheeled robots and one flying micro-drone) in four prototypical environments and show that the assumption of a constant and unidirectional airflow, which is the basis of many gas source localization approaches, is usually far from being valid. This analysis should help to identify how underlying principles, which govern the gas source tracking behavior of animals, can be usefully translated into gas source localization approaches that fully take into account the capabilities of mobile robots. We also describe the requirements for a reference application, monitoring of gas emissions at landfill sites with mobile robots, and discuss an engineered gas source localization approach based on statistics as an alternative to biologically inspired algorithms. }, year = {2012} } @inproceedings{Stoyanov524119, author = {Stoyanov, Todor and Magnusson, Martin and Lilienthal, Achim J.}, booktitle = {2012 IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, note = {Accepted for publication. Advance copy available at http://aass.oru.se/Research/Learning/publications/2012/Stoyanov_etal_2012-ICRA.pdf}, pages = {5196--5201}, title = {Point Set Registration through Minimization of the L-2 Distance between 3D-NDT Models}, series = {Proceedings - IEEE International Conference on Robotics and Automation}, DOI = {10.1109/ICRA.2012.6224717}, abstract = {Point set registration — the task of finding the best fitting alignment between two sets of point samples, is an important problem in mobile robotics. This article proposes a novel registration algorithm, based on the distance between Three- Dimensional Normal Distributions Transforms. 3D-NDT models — a sub-class of Gaussian Mixture Models with uniformly weighted, largely disjoint components, can be quickly computed from range point data. The proposed algorithm constructs 3DNDT representations of the input point sets and then formulates an objective function based on the L2 distance between the considered models. Analytic first and second order derivatives of the objective function are computed and used in a standard Newton method optimization scheme, to obtain the best-fitting transformation. The proposed algorithm is evaluated and shown to be more accurate and faster, compared to a state of the art implementation of the Iterative Closest Point and 3D-NDT Point-to-Distribution algorithms. }, ISBN = {9781467314053}, ISBN = {9781467314039}, year = {2012} } @inproceedings{Neumann541169, author = {Neumann, Patrick and Asadi, Sahar and Schiller, Jochen H. and Lilienthal, Achim J. and Bartholmai, Matthias}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, institution = {Institute of Computer Science, Freie Universität Berlin, Berlin, Germany}, institution = {Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany}, pages = {34--38}, title = {An artificial potential field based sampling strategy for a gas-sensitive micro-drone}, keywords = {autonomous UAV, chemical sensing, gas distribution modelling, gas source localization, gas sensors, mobile sensing system, quadrocopter, sensor planning, artificial potential field.}, abstract = {This paper presents a sampling strategy for mobile gas sensors. Sampling points are selected using a modified artificial potential field (APF) approach, which balances multiple criteria to direct sensor measurements towards locations of high mean concentration, high concentration variance and areas for which the uncertainty about the gas distribution model is still large. By selecting in each step the most often suggested close-by measurement location, the proposed approach introduces a locality constraint that allows planning suitable paths for mobile gas sensors. Initial results in simulation and in real-world experiments witha gas-sensitive micro-drone demonstrate the suitability of the proposed sampling strategy for gas distribution mapping and its use for gas source localization. }, year = {2011} } @inproceedings{Stoyanov540987, author = {Stoyanov, Todor and Louloudi, Athanasia and Andreasson, Henrik and Lilienthal, Achim J.}, booktitle = {Proceedings of the 5th European Conference on Mobile Robots, ECMR 2011 : }, institution = {Örebro University, School of Science and Technology}, pages = {19--24}, title = {Comparative evaluation of range sensor accuracy in indoor environments}, abstract = {3D range sensing is one of the important topics in robotics, as it is often a component in vital autonomous subsystems like collision avoidance, mapping and semantic perception. The development of affordable, high frame rate and precise 3D range sensors is thus of considerable interest. Recent advances in sensing technology have produced several novel sensors that attempt to meet these requirements. This work is concerned with the development of a holistic method for accuracy evaluation of the measurements produced by such devices. A method for comparison of range sensor output to a set of reference distance measurements is proposed. The approach is then used to compare the behavior of three integrated range sensing devices, to that of a standard actuated laser range sensor. Test cases in an uncontrolled indoor environment are performed in order to evaluate the sensors’ performance in a challenging, realistic application scenario. }, year = {2011} } @inproceedings{Asadi540979, author = {Asadi, Sahar and Badica, Costin and Comes, Tina and Conrado, Claudine and Evers, Vanessa and Groen, Frans and Illie, Sorin and Steen Jensen, Jan and Lilienthal, Achim J. and Milan, Bianca and Neidhart, Thomas and Nieuwenhuis, Kees and Pashami, Sepideh and Pavlin, Gregor and Pehrsson, Jan and Pinchuk, Rani and Scafes, Mihnea and Schou-Jensen, Leo and Schultmann, Frank and Wijngaards, Niek}, booktitle = {Proceedings of the 25th EnviroInfo Conference "Environmental Informatics" : }, institution = {Örebro University, School of Science and Technology}, institution = {Örebro University, School of Science and Technology}, institution = {University of Craiova, Craiova, Romania}, institution = {Karslruhe Institute of Technology, Karslruhe, Germany}, institution = {Thales Research and Technology, Delft, The Netherlands}, institution = {University of Amsterdam, Amsterdam, The Netherlands}, institution = {University of Amsterdam, Amsterdam, The Netherlands}, institution = {University of Craiova, Craiova, Romania}, institution = {Danish Emergency Management Agency (DEMA), Birkerød, Denmark}, institution = {DCMR, Delft, The Netherlands}, institution = {Space Applications Services, Zaventem, Belgium}, institution = {Thales Research and Technology, Delft, The Netherlands}, institution = {Thales Research and Technology, Delft, The Netherlands}, institution = {Prolog Development Center, Brøndby Copenhagen, Denmark}, institution = {Space Applications and Services, Zaventem, Belgium}, institution = {University of Craiova, Craiova, Romania}, institution = {DCMR, Brøndby Copenhagen, Denmark}, institution = {Karslruhe Institute of Technology, Karlsruhe, Germany}, institution = {Thales Research and Technology, Delft, the Netherlands}, note = {DMCR: the joint environmental protection agency of the province of South Holland and 16 municipalities}, pages = {920--931}, title = {ICT solutions supporting collaborative information acquisition, situation assessment and decision making in contemporary environmental management problems : the DIADEM approach}, abstract = {This paper presents a framework of ICT solutions developed in the EU research project DIADEM that supports environmental management with an enhanced capacity to assess population exposure and health risks, to alert relevant groups and to organize efficient response. The emphasis is on advanced solutions which are economically feasible and maximally exploit the existing communication, computing and sensing resources. This approach enables efficient situation assessment in complex environmental management problems by exploiting relevant information obtained from citizens via the standard communication infrastructure as well as heterogeneous data acquired through dedicated sensing systems. This is achieved through a combination of (i) advanced approaches to gas detection and gas distribution modelling, (ii) a novel service-oriented approach supporting seamless integration of human-based and automated reasoning processes in large-scale collaborative sense making processes and (iii) solutions combining Multi-Criteria Decision Analysis, Scenario-Based Reasoning and advanced human-machine interfaces. This paper presents the basic principles of the DIADEM solutions, explains how different techniques are combined to a coherent decision support system and briefly discusses evaluation principles and activities in the DIADEM project. }, ISBN = {978-3-8440-0451-9}, year = {2011} } @incollection{Lilienthal540974, author = {Lilienthal, Achim J.}, booktitle = {Intelligent Systems for Machine Olfaction : Tools and Methodologies}, institution = {Örebro University, School of Science and Technology}, pages = {249--276}, title = {Improved gas source localization with a mobile robot by learning analytical gas dispersal models from statistical gas distribution maps using evolutionary algorithms}, DOI = {10.4018/978-1-61520-915-6.ch010}, abstract = {The method presented in this chapter computes an estimate of the location of a single gas sourcefrom a set of localised gas sensor measurements. The estimation process consists of three steps.First, a statistical model of the time-averaged gas distribution is estimated in the form of a two-dimensional grid map. In order to compute the gas distribution grid map the Kernel DM algorithm isapplied, which carries out spatial integration by convolving localised sensor readings and modelling theinformation content of the point measurements with a Gaussian kernel. The statistical gas distributiongrid map averages out the transitory effects of turbulence and converges to a representation of thetime-averaged spatial distribution of a target gas. The second step is to learn the parameters ofan analytical model of average gas distribution. Learning is achieved by nonlinear least squaresfitting of the analytical model to the statistical gas distribution map using Evolution Strategies (ES),which are a special type of Evolutionary Algorithms (EA). This step provides an analysis of thestatistical gas distribution map regarding the airflow conditions and an alternative estimate of thegas source location, i.e. the location predicted by the analytical model in addition to the location ofthe maximum in the statistical gas distribution map. In the third step, an improved estimate of thegas source position can then be derived by considering the maximum in the statistical gas distributionmap, the best fit as well as the corresponding fitness value. Different methods to select the mosttruthful estimate are introduced and a comparison regarding their accuracy is presented, based on atotal of 34 hours of gas distribution mapping experiments with a mobile robot. This chapter is anextended version of a paper by the authors (Lilienthal et al. [2005]). }, ISBN = {9781615209156}, year = {2011} } @inproceedings{Stoyanov524116, author = {Stoyanov, Todor and Magnusson, Martin and Almqvist, H{\aa}kan and Lilienthal, Achim J.}, booktitle = {2011 IEEE International Conference on Robotics and Automation (ICRA) : }, institution = {Örebro University, School of Science and Technology}, note = {Proceedings athttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5501116}, title = {On the Accuracy of the 3D Normal Distributions Transform as a Tool for Spatial Representation}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ICRA.2011.5979584}, abstract = {The Three-Dimensional Normal Distributions Transform (3D-NDT) is a spatial modeling technique with applications in point set registration, scan similarity comparison, change detection and path planning. This work concentrates on evaluating three common variations of the 3D-NDT in terms of accuracy of representing sampled semi-structured environments. In a novel approach to spatial representation quality measurement, the 3D geometrical modeling task is formulated as a classification problem and its accuracy is evaluated with standard machine learning performance metrics. In this manner the accuracy of the 3D-NDT variations is shown to be comparable to, and in some cases to outperform that of the standard occupancy grid mapping model. }, ISBN = {978-1-61284-385-8}, year = {2011} } @inproceedings{Trincavelli475978, author = {Trincavelli, Marco and Vergara, A. and Rulkov, N. and Murguia, J. S. and Lilienthal, Achim J. and Huerta, R.}, booktitle = {Olfaction and electronic nose : Proceedings of the 14th international symposium on olfaction and electonic nose}, institution = {Örebro University, School of Science and Technology}, pages = {225--227}, title = {Optimizing the operating temperature for an array of MOX sensors on an open sampling system}, series = {AIP Conference Proceedings}, number = {1362}, DOI = {10.1063/1.3626368}, keywords = {Metal Oxide Gas Sensors, Operating Temperature Optimization, Air Pollution Monitoring, Gas Leak Detection}, abstract = {Chemo-resistive transduction is essential for capturing the spatio-temporal structure of chemical compounds dispersed in different environments. Due to gas dispersion mechanisms, namely diffusion, turbulence and advection, the sensors in an open sampling system, i.e. directly exposed to the environment to be monitored, are exposed to low concentrations of gases with many fluctuations making, as a consequence, the identification and monitoring of the gases even more complicated and challenging than in a controlled laboratory setting. Therefore, tuning the value of the operating temperature becomes crucial for successfully identifying and monitoring the pollutant gases, particularly in applications such as exploration of hazardous areas, air pollution monitoring, and search and rescue I. In this study we demonstrate the benefit of optimizing the sensor's operating temperature when the sensors are deployed in an open sampling system, i.e. directly exposed to the environment to be monitored. }, ISBN = {978-0-7354-0920-0}, year = {2011} } @inproceedings{Echelmeyer1190428, author = {Echelmeyer, Wolfgang and Kirchheim, Alice and Lilienthal, Achim and Akbiyik, H{\"u}lya and Bonini, Marco}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, institution = {University of Reutlingen, Reutlingen, Germany}, institution = {School of Science and Technology, Örebro University, Örebro, Sweden}, institution = {University of Reutlingen, Reutlingen, Germany}, institution = {University of Reutlingen, Reutlingen, Germany}, title = {Performance Indicators for Robotics Systems in Logistics Applications}, abstract = {The transfer of research results to market-ready products is often a costly and time-consuming process. In order to generate successful products, researchers must cooperate with industrial companies; both the industrial and academic partners need to have a detailed understanding of the requirements of all parties concerned. Academic researchers need to identify the performance indicators for technical systems within a business environment and be able to apply them. Inservice logistics today, nearly all standardized mass goods are unloaded manually with one reason for this being the undefined position and orientation of the goods in the carrier. A study regarding the qualitative and quantitative properties of goods that are transported in containers shows that there is a huge economic relevance for autonomous systems. In 2008, more than 8,4 billion Twenty-foot equivalent units (TEU) were imported and unloaded manually at European ports, corresponding to more than 331,000 billion single goods items. Besides the economic relevance, the opinion of market participants is an important factor for the success of new systems on the market. The main outcomes of a study regarding the challenges, opportunities and barriers in robotic-logistics, allow for the estimation of the economic efficiency of performance indicators, performance flexibility and soft factors. The economic efficiency of the performance parameters is applied to the parcel robot – a cognitive system to unload parcels autonomously from containers. In the following article, the results of the study are presented and the resultant conclusions discussed. }, year = {2011} } @article{Petrovitc444371, author = {Petrovitc, Ivan and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Robotics and Autonomous Systems}, note = {Editorial material.}, number = {5}, pages = {263--264}, title = {Special issue ECMR 2009}, volume = {59}, DOI = {10.1016/j.robot.2011.02.014}, year = {2011} } @incollection{Asadi541144, author = {Asadi, Sahar and Reggente, Matteo and Stachniss, Cyrill and Plagemann, Christian and Lilienthal, Achim J.}, booktitle = {Intelligent systems for machine olfaction : tools and methodologies}, edition = {1}, institution = {Örebro University, School of Science and Technology}, institution = {University of Freiburg, Freiburg, Germany}, institution = {Stanford University, Stanford CA, USA}, pages = {153--179}, title = {Statistical gas distribution modeling using kernel methods}, DOI = {10.4018/978-1-61520-915-6.ch006}, keywords = {Gas sensors, Gas distribution modelling, Statistical Gas Distribution Modelling, Kernel density estimation, Kernel regression, Gaussian Processes, Gaussian Process Mixture Models, Environmental monitoring, Gas source localization}, abstract = {Gas distribution models can provide comprehensive information about a large number of gas concentration measurements, highlighting, for example, areas of unusual gas accumulation. They can also help to locate gas sources and to plan where future measurements should be carried out. Current physical modeling methods, however, are computationally expensive and not applicable for real world scenarios with real-time and high resolution demands. This chapter reviews kernel methodsthat statistically model gas distribution. Gas measurements are treated as randomvariables, and the gas distribution is predicted at unseen locations either using akernel density estimation or a kernel regression approach. The resulting statistical  apmodelsdo not make strong assumptions about the functional form of the gas distribution,such as the number or locations of gas sources, for example. The majorfocus of this chapter is on two-dimensional models that provide estimates for themeans and predictive variances of the distribution. Furthermore, three extensionsto the presented kernel density estimation algorithm are described, which allow toinclude wind information, to extend the model to three dimensions, and to reflecttime-dependent changes of the random process that generates the gas distributionmeasurements. All methods are discussed based on experimental validation usingreal sensor data. }, ISBN = {9781615209156}, year = {2011} } @inproceedings{Asadi540989, author = {Asadi, Sahar and Pashami, Sepideh and Loutfi, Amy and Lilienthal, Achim J.}, booktitle = {Olfaction and Electronic Nose : proceedings of the 14th International Symposium on Olfaction and Electronic Nose (ISOEN)}, institution = {Örebro University, School of Science and Technology}, pages = {281--282}, title = {TD Kernel DM+V : time-dependent statistical gas distribution modelling on simulated measurements}, series = {AIP Conference Proceedings}, number = {1362}, DOI = {10.1063/1.3651651}, abstract = {To study gas dispersion, several statistical gas distribution modelling approaches have been proposed recently. A crucial assumption in these approaches is that gas distribution models are learned from measurements that are generated by a time-invariant random process. While a time-independent random process can capture certain fluctuations in the gas distribution, more accurate models can be obtained by modelling changes in the random process over time. In this work we propose a time-scale parameter that relates the age of measurements to their validity for building the gas distribution model in a recency function. The parameters of the recency function define a time-scale and can be learned. The time-scale represents a compromise between two conflicting requirements for obtaining accurate gas distribution models: using as many measurements as possible and using only very recent measurements. We have studied several recency functions in a time-dependent extension of the Kernel DM+V algorithm (TD Kernel DM+V). Based on real-world experiments and simulations of gas dispersal (presented in this paper) we demonstrate that TD Kernel DM+V improves the obtained gas distribution models in dynamic situations. This represents an important step towards statistical modelling of evolving gas distributions. }, ISBN = {978-0-7354-0920-0}, year = {2011} } @article{Andreasson274835, author = {Andreasson, Henrik and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, journal = {Robotics and Autonomous Systems}, note = {Selected papers from the 2007 European Conference on Mobile Robots (ECMR ’07)}, number = {2}, pages = {157--165}, title = {6D scan registration using depth-interpolated local image features}, volume = {58}, DOI = {10.1016/j.robot.2009.09.011}, keywords = {Registration, Vision, Laser Range Finder, SLAM}, abstract = {This paper describes a novel registration approach that is based on a combination of visual and 3D range information.To identify correspondences, local visual features obtained from images of a standard color camera are compared and the depth of matching features (and their position covariance) is determined from the range measurements of a 3D laserscanner. The matched depth-interpolated image features allows to apply registration with known correspondences.We compare several ICP variants in this paper and suggest an extension that considers the spatial distance betweenmatching features to eliminate false correspondences. Experimental results are presented in both outdoor and indoor environments. In addition to pair-wise registration, we also propose a global registration method that registers allscan poses simultaneously. }, year = {2010} } @article{Cielniak383180, author = {Cielniak, Grzegorz and Duckett, Tom and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Sch Comp Sci, Lincoln Univ, Lincoln, England}, institution = {Sch Comp Sci, Lincoln Univ, Lincoln, England}, journal = {Robotics and Autonomous Systems}, number = {5}, pages = {435--443}, title = {Data association and occlusion handling for vision-based people tracking by mobile robots}, volume = {58}, DOI = {10.1016/j.robot.2010.02.004}, keywords = {AdaBoost, Occlusion detection, Thermal vision, Colour vision, Bayesian estimation}, abstract = {This paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is incorporated into the tracker. The paper presents a comprehensive, quantitative evaluation of the whole system and its different components using several real world data sets. (C) 2010 Elsevier B.V. All rights reserved. }, year = {2010} } @inproceedings{Ferri524121, author = {Ferri, Gabriele and Mondini, Alessio and Manzi, Alessandro and Mazzolai, Barbara and Laschi, Cecilia and Mattoli, Virgilio and Reggente, Matteo and Stoyanov, Todor and Lilienthal, Achim J. and Lettere, Marco and Dario, Paolo.}, booktitle = {Proceedings of ICRA Workshop on Networked and Mobile Robot Olfaction in Natural, Dynamic Environments : }, institution = {Örebro University, School of Science and Technology}, institution = {Scuola Superiore Sant'Anna, Pisa, Italy}, institution = {Scuola Superiore Sant'Anna, Pisa, Italy}, institution = {Scuola Superiore Sant'Anna, Pisa, Italy}, institution = {Scuola Superiore Sant'Anna, Pisa, Italy}, institution = {Scuola Superiore Sant'Anna, Pisa, Italy}, institution = {Scuola Superiore Sant'Anna, Pisa, Italy}, institution = {Scuola Superiore Sant'Anna, Pisa, Italy}, institution = {Scuola Superiore Sant'Anna, Pisa, Italy}, note = {Conference url: http://icra2010.grasp.upenn.edu/?q=overview}, title = {DustCart, a Mobile Robot for Urban Environments : Experiments of Pollution Monitoring and Mapping during Autonomous Navigation in Urban Scenarios}, keywords = {mobile robots, urban robots, gas mapping, navigation}, abstract = {In the framework of DustBot European project, aimed at developing a new multi-robot system for urban hygiene management, we have developed a twowheeled robot: DustCart. DustCart aims at providing a solution to door-to-door garbage collection: the robot, called by a user, navigates autonomously to his/her house; collects the garbage from the user and discharges it in an apposite area. An additional feature of DustCart is the capability to monitor the air pollution by means of an on board Air Monitoring Module (AMM). The AMM integrates sensors to monitor several atmospheric pollutants, such as carbon monoxide (CO), particular matter (PM10), nitrogen dioxide (NO2), ozone (O3) plus temperature (T) and relative humidity (rHu). An Ambient Intelligence platform (AmI) manages the robots’ operations through a wireless connection. AmI is able to collect measurements taken by different robots and to process them to create a pollution distribution map. In this paper we describe the DustCart robot system, focusing on the AMM and on the process of creating the pollutant distribution maps. We report results of experiments of one DustCart robot moving in urban scenarios and producing gas distribution maps using the Kernel DM+V algorithm. These experiments can be considered as one of the first attempts to use robots as mobile monitoring devices that can complement the traditional fixed stations. }, year = {2010} } @inproceedings{Pashami534451, author = {Pashami, Sepideh and Asadi, Sahar and Lilienthal, Achim J.}, booktitle = { : }, institution = {Örebro University, School of Science and Technology}, note = {Proceedings available (after registration) athttp://www.opensourcecfd.com/conference2010/proceedings/content/home.php}, title = {Integration of OpenFOAM Flow Simulation and Filament-Based Gas Propagation Models for Gas Dispersion Simulation}, keywords = {Gas dispersion, CFD, OpenFOAM}, abstract = {In this paper, we present a gas dispersal simulation package which integrates OpenFOAM flow simulation and a filament-based gas propagation model to simulate gas dispersion for compressible flows with a realistic turbulence model. Gas dispersal simulation can be useful for many applications. In this paper, we focus on the evaluation of statistical gas distribution models. Simulated data offer several advantages for this purpose, including the availability of ground truth information, repetition of experiments with the exact same constraints and that intricate issue which come with using real gas sensors can be avoided.Apart from simulation results obtained in a simulated wind tunnel (designed to be equivalent to its real-world counterpart), we present initial results with time-independent and time-dependent statistical modelling approaches applied to simulated and real-world data. }, year = {2010} } @inproceedings{Stoyanov445259, author = {Stoyanov, Todor and Magnusson, Martin and Andreasson, Henrik and Lilienthal, Achim J.}, booktitle = {IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems (IROS 2010) : }, institution = {Örebro University, School of Science and Technology}, pages = {3263--3268}, title = {Path planning in 3D environments using the normal distributions transform}, DOI = {10.1109/IROS.2010.5650789}, abstract = {Planning feasible paths in fully three-dimensional environments is a challenging problem. Application of existing algorithms typically requires the use of limited 3D representations that discard potentially useful information. This article proposes a novel approach to path planning that utilizes a full 3D representation directly: the Three-Dimensional Normal Distributions Transform (3D-NDT). The well known wavefront planner is modified to use 3D-NDT as a basis for map representation and evaluated using both indoor and outdoor data sets. The use of 3D-NDT for path planning is thus demonstrated to be a viable choice with good expressive capabilities. }, ISBN = {978-1-4244-6675-7}, year = {2010} } @article{Valgren306578, author = {Valgren, Christoffer and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Department of Computer Science, Örebro University, Örebro, Sweden}, journal = {Robotics and Autonomous Systems}, number = {2}, pages = {149--156}, title = {SIFT, SURF {\&}amp; seasons : Appearance-based long-term localization in outdoor environments}, volume = {58}, DOI = {10.1016/j.robot.2009.09.010}, keywords = {Localization, Scene Recognition, Outdoor Environments}, abstract = {In this paper, we address the problem of outdoor, appearance-based topological localization, particularly over long periods of time where seasonal changes alter the appearance of the environment. We investigate a straight-forward method that relies on local image features to compare single image pairs. We rst look into which of the dominating image feature algorithms, SIFT or the more recent SURF, that is most suitable for this task. We then ne-tune our localization algorithm in terms of accuracy, and also introduce the epipolar constraint to further improve the result. The nal localization algorithm is applied on multiple data sets, each consisting of a large number of panoramic images, which have been acquired over a period of nine months with large seasonal changes. The nal localization rate in the single-image matching, cross-seasonal case is between 80 to 95%. }, year = {2010} } @inproceedings{Reggente444953, author = {Reggente, Matteo and Lilienthal, Achim J.}, booktitle = {2010 IEEE SENSORS}, institution = {Örebro University, School of Science and Technology}, pages = {999--1004}, title = {The 3D-kernel DM+V/W algorithm : using wind information in three dimensional gas distribution modelling with a mobile robot}, series = {IEEE Sensors}, DOI = {10.1109/ICSENS.2010.5690924}, abstract = {In this paper we present a statistical method to build three-dimensional gas distribution maps from gas sensor and wind measurements obtained with a mobile robot in uncontrolled environments. The particular contribution of this paper is to introduce and evaluate an algorithm for 3D statistical gas distribution mapping, that takes into account airflow information. 3D-Kernel DM+V/W algorithm uses a multivariate Gaussian weighting function to model the information provided by the gas sensors and an ultrasonic anemometer. The proposed algorithm is evaluated with respect to the ability of the obtained models to predict unseen measurements. The results based on 15 trials with a mobile robot in an indoor environment show improvements in the model performance when using the 3D kernel DM+V/W algorithm. Moreover the model is able to adapt to the dynamical changes of the environment learning the hyper-parameter from the sensors readings. }, ISBN = {978-1-4244-8168-2}, year = {2010} } @article{Reggente445325, author = {Reggente, Matteo and Mondini, Alessio and Ferri, Gabriele and Mazzolai, Barbara and Manzi, Alessandro and Gabelletti, Matteo and Dario, Paolo and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {CRIM Laboratory, Scuola Superiore Sant'Anna, Pisa, Italy }, institution = {CRIM Laboratory, Scuola Superiore Sant'Anna, Pisa, Italy }, institution = {Centre in MicroBioRobotics IIT at SSSA, Italian Institute of Technology, Pisa, Italy }, institution = {Arts Laboratory, Scuola Superiore Sant'Anna, Pisa, Italy }, institution = {Arts Laboratory, Scuola Superiore Sant'Anna, Pisa, Italy }, institution = {CRIM Laboratory, Scuola Superiore Sant'Anna, Pisa, Italy }, journal = {Chemical Engineering Transactions}, pages = {273--278}, publisher = {AIDIC Servizi}, title = {The DustBot System : Using Mobile Robots to Monitor Pollution in Pedestrian Area}, volume = {23}, DOI = {10.3303/CET1023046}, abstract = {The EU project DustBot addresses urban hydeience. Two types of robots were designed, the DustClean robot to autonomously clean pedestrian areas, and the DustCart robot for door-to-door garbage collection. Three prototype robots were built and equipped with electronic noses so as to enable them to collect environmental data while performing their urban hygiene tasks. Essentially, the robots act as a mobile, wirless node in a sensor network. In this paper we give an overview of the DusBot platform focusig on the Air Monitoring Module (AMM). We descibe the data flow between the robots throught the ubiquitous network to a gas distribution modelling server, where a gas deisribution model is computed. We descibe the Kernel DM+V algorithn, an approach to create statistical gas disdtribution models in the form of predictive mean and variance discrtized onto a grid map. Finally we present and discuss results obtained with the DustBot AMM during experimental trails performex in outdoor public places; a courtyard in Pontedera, Italy and a pedestrian square in Örebro, Sweden. }, ISBN = {978-88-95608-14-3}, year = {2010} } @inproceedings{Lilienthal274853, author = {Lilienthal, Achim J. and Reggente, Matteo and Trincavelli, Marco and Blanco, Jose Luis and Gonzalez, Javier}, booktitle = {IEEE/RSJ international conference on intelligent robots and systems : IROS 2009}, institution = {Örebro University, School of Science and Technology}, institution = {Dept. of System Engineering and Automation, University of Malaga}, pages = {570--576}, title = {A statistical approach to gas distribution modelling with mobile robots : the Kernel DM+V algorithm}, series = {IEEE Conference Publications}, DOI = {10.1109/IROS.2009.5354304}, abstract = {Gas distribution modelling constitutes an ideal application area for mobile robots, which – as intelligent mobile gas sensors – offer several advantages compared to stationary sensor networks. In this paper we propose the Kernel DM+V algorithm to learn a statistical 2-d gas distribution model from a sequence of localized gas sensor measurements. The algorithm does not make strong assumptions about the sensing locations and can thus be applied on a mobile robot that is not primarily used for gas distribution monitoring, and also in the case of stationary measurements. Kernel DM+V treats distribution modelling as a density estimation problem. In contrast to most previous approaches, it models the variance in addition to the distribution mean. Estimating the predictive variance entails a significant improvement for gas distribution modelling since it allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. Estimating the predictive variance also provides the means to learn meta parameters and to suggest new measurement locations based on the current model. We derive the Kernel DM+V algorithm and present a method for learning the hyper-parameters. Based on real world data collected with a mobile robot we demonstrate the consistency of the obtained maps and present a quantitative comparison, in terms of the data likelihood of unseen samples, with an alternative approach that estimates the predictive variance. }, ISBN = {978-1-4244-3803-7}, year = {2009} } @inproceedings{Astrand274865, author = {{\AA}strand, Bj{\"o}rn and R{\"o}gnvaldsson, Thorsteinn and Bouguerra, Abdelbaki and Andreasson, Henrik and Lilienthal, Achim J.}, booktitle = {Proceedings of the 4th Swedish Workshop on Autonomous Robotics (SWAR)}, institution = {Örebro University, School of Science and Technology}, institution = {Halmstad University}, institution = {Halmstad University}, pages = {56--57}, title = {An Autonomous Robotic System for Load Transportation}, year = {2009} } @inproceedings{Bouguerra274885, author = {Bouguerra, Abdelbaki and Andreasson, Henrik and Lilienthal, Achim J. and {\AA}strand, Bj{\"o}rn and R{\"o}gnvaldsson, Thorsteinn}, booktitle = {2009 IEEE Conference on Emerging Technologies & Factory Automation (EFTA 2009) : }, institution = {Örebro University, School of Science and Technology}, institution = {Halmstad University, Halmstad, Sweden}, institution = {Halmstad University, Halmstad, Sweden}, pages = {1563--1566}, title = {An autonomous robotic system for load transportation}, series = {IEEE International Conference on Emerging Technologies and Factory Automation-ETFA}, DOI = {10.1109/ETFA.2009.5347247}, keywords = {AGV system; Autonomous robotic systems; Dynamic environments; Material handling; Object Detection; Runtimes}, abstract = {This paper presents an overview of an autonomous robotic material handling system. The goal of the system is to extend the functionalities of traditional AGVs to operate in highly dynamic environments. Traditionally, the reliable functioning of AGVs relies on the availability of adequate infrastructure to support navigation. In the target environments of our system, such infrastructure is difficult to setup in an efficient way. Additionally, the location of objects to handle are unknown, which requires that the system be able to detect and track object positions at runtime. Another requirement of the system is to be able to generate trajectories dynamically, which is uncommon in industrial AGV systems. }, ISBN = {978-1-4244-2727-7}, ISBN = {978-1-4244-2728-4}, year = {2009} } @inproceedings{Magnusson391763, author = {Magnusson, Martin and Andreasson, Henrik and N{\"u}chter, A. and Lilienthal, Achim J.}, booktitle = {IEEE International Conference on Robotics and Automation 2009 (ICRA '09) : }, institution = {Örebro University, School of Science and Technology}, institution = {Jacobs University Bremen, Bremen, Germany}, note = {Funding Agency:Atlas Copco Rock Drills }, pages = {23--28}, title = {Appearance-based loop detection from 3D laser data using the normal distributions transform}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ROBOT.2009.5152712}, abstract = {We propose a new approach to appearance based loop detection from metric 3D maps, exploiting the NDT surface representation. Locations are described with feature histograms based on surface orientation and smoothness, and loop closure can be detected by matching feature histograms. We also present a quantitative performance evaluation using two realworld data sets, showing that the proposed method works well in different environments.© 2009 IEEE. }, ISBN = {9781424427888}, ISBN = {9781424427895}, year = {2009} } @article{Magnusson274842, author = {Magnusson, Martin and Andreasson, Henrik and N{\"u}chter, Andreas and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Jacobs University Bremen}, journal = {Journal of Field Robotics}, number = {11-12}, pages = {892--914}, title = {Automatic appearance-based loop detection from three-dimensional laser data using the normal distributions transform}, volume = {26}, DOI = {10.1002/rob.20314}, abstract = {We propose a new approach to appearance-based loop detection for mobile robots, usingthree-dimensional (3D) laser scans. Loop detection is an important problem in the simultaneouslocalization and mapping (SLAM) domain, and, because it can be seen as theproblem of recognizing previously visited places, it is an example of the data associationproblem. Without a flat-floor assumption, two-dimensional laser-based approaches arebound to fail in many cases. Two of the problems with 3D approaches that we address inthis paper are how to handle the greatly increased amount of data and how to efficientlyobtain invariance to 3D rotations.We present a compact representation of 3D point cloudsthat is still discriminative enough to detect loop closures without false positives (i.e.,detecting loop closure where there is none). A low false-positive rate is very important becausewrong data association could have disastrous consequences in a SLAM algorithm.Our approach uses only the appearance of 3D point clouds to detect loops and requires nopose information. We exploit the normal distributions transform surface representationto create feature histograms based on surface orientation and smoothness. The surfaceshape histograms compress the input data by two to three orders of magnitude. Becauseof the high compression rate, the histograms can be matched efficiently to compare theappearance of two scans. Rotation invariance is achieved by aligning scans with respectto dominant surface orientations. We also propose to use expectation maximization to fit a gamma mixture model to the output similarity measures in order to automatically determinethe threshold that separates scans at loop closures from nonoverlapping ones.Wediscuss the problem of determining ground truth in the context of loop detection and thedifficulties in comparing the results of the few available methods based on range information.Furthermore, we present quantitative performance evaluations using three realworlddata sets, one of which is highly self-similar, showing that the proposed methodachieves high recall rates (percentage of correctly identified loop closures) at low falsepositiverates in environments with different characteristics. }, year = {2009} } @inproceedings{Lilienthal274903, author = {Lilienthal, Achim J. and Asadi, Sahar and Reggente, Matteo}, booktitle = {Olfaction and electronic nose : proceedings}, institution = {Örebro University, School of Science and Technology}, pages = {65--68}, title = {Estimating predictive variance for statistical gas distribution modelling}, series = {AIP conference proceedings}, number = {1137}, DOI = {10.1063/1.3156628}, keywords = {Gas distribution modelling, gas sensing, mobile robot olfaction, density estimation, model evaluation}, abstract = {Recent publications in statistical gas distribution modelling have proposed algorithms that model mean and variance of a distribution. This paper argues that estimating the predictive concentration variance entails not only a gradual improvement but is rather a significant step to advance the field. This is, first, since the models much better fit the particular structure of gas distributions, which exhibit strong fluctuations with considerable spatial variations as a result of the intermittent character of gas dispersal. Second, because estimating the predictive variance allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. It also enables solid comparisons of different modelling approaches, and provides the means to learn meta parameters of the model, to determine when the model should be updated or re-initialised, or to suggest new measurement locations based on the current model. We also point out directions of related ongoing or potential future research work. }, ISBN = {978-0-7354-0674-2}, year = {2009} } @inproceedings{Magnusson274922, author = {Magnusson, Martin and N{\"u}chter, Andreas and L{\"o}rken, Christopher and Lilienthal, Achim J. and Hertzberg, Joachim}, booktitle = {Proceedings of the 2009 IEEE international conference on Robotics and Automation, ICRA'09 : }, institution = {Örebro University, School of Science and Technology}, institution = {Jacobs University Bremen, Bremen, Germany; Knowledge Systems Research Group of the Institute of Computer Science, University of Osnabrück, Germany}, institution = {Institute of Computer Science, University of Osnabrück, Germany}, institution = {Institute of Computer Science, University of Osnabrück, Germany}, note = {Funding Agency:Atlas Copco Rock Drills }, pages = {2263--2268}, title = {Evaluation of 3D registration reliability and speed : a comparison of ICP and NDT}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ROBOT.2009.5152538}, abstract = {To advance robotic science it is important to perform experiments that can be replicated by other researchers to compare different methods. However, these comparisons tend to be biased, since re-implementations of reference methods often lack thoroughness and do not include the hands-on experience obtained during the original development process. This paper presents a thorough comparison of 3D scan registration algorithms based on a 3D mapping field experiment, carried out by two research groups that are leading in the field of 3D robotic mapping. The iterative closest points algorithm (ICP) is compared to the normal distributions transform (NDT). We also present an improved version of NDT with a substantially larger valley of convergence than previously published versions. }, ISBN = {9781424427888}, ISBN = {9781424427895}, year = {2009} } @inproceedings{Charusta274830, author = {Charusta, Krzysztof and Dimitrov, Dimitar and Lilienthal, Achim J. and Iliev, Boyko}, booktitle = {2009 International Conference on Advanced Robotics : }, institution = {Örebro University, School of Science and Technology}, pages = {1--6}, title = {Extraction of grasp-related features by human dual-hand object exploration}, keywords = {robotic grasping, programming-by-demonstration}, abstract = {We consider the problem of objects exploration for grasping purposes, specifically in cases where vision based methods are not applicable. A novel dual-hand object exploration method is proposed that takes benefits from a human demonstration to enrich knowledge about an object. The user handles an object freely using both hands, without restricting the object pose. A set of grasp-related features obtained during exploration is demonstrated and utilized to generate grasp oriented bounding boxes that are basis for pre-grasp hypothesis. We believe that such exploration done in a natural and user friendly way creates important link between an operator intention and a robot action. }, URL = {https://ieeexplore.ieee.org/document/5174680}, ISBN = {978-1-4244-4855-5}, year = {2009} } @article{Loutfi274847, author = {Loutfi, Amy and Coradeschi, Silvia and Lilienthal, Achim J. and Gonzalez, Javier}, institution = {Örebro University, School of Science and Technology}, institution = {Dept. of System Engineering and Automation, University of Malaga}, journal = {Robotica (Cambridge. Print)}, number = {2}, pages = {311--319}, title = {Gas Distribution Mapping of Multiple Odour Sources using a Mobile Robot}, volume = {27}, DOI = {10.1017/S0263574708004694}, abstract = {Mobile olfactory robots can be used in a number of relevant application areas where a better understanding of agas distribution is needed, such as environmental monitoring and safety and security related fields. In this paper wepresent a method to integrate the classification of odours together with gas distribution mapping. The resulting odourmap is then correlated with the spatial information collected from a laser range scanner to form a combined map.Experiments are performed using a mobile robot in large and unmodified indoor and outdoor environments. Multipleodour sources are used and are identified using only transient information from the gas sensor response. The resultingmulti level map can be used as a intuitive representation of the collected odour data for a human user. }, year = {2009} } @article{Stachniss274845, author = {Stachniss, Cyrill and Plagemann, Christian and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {University of Freiburg}, institution = {Stanford University}, journal = {Autonomous Robots}, number = {2-3}, pages = {187--202}, title = {Learning Gas Distribution Models Using Sparse Gaussian Process Mixtures}, volume = {26}, DOI = {10.1007/s10514-009-9111-5}, keywords = {Gas distribution modeling, Gas sensing, Gaussian processes, Mixture models}, abstract = {In this paper, we consider the problem of learning two-dimensional spatial models of gas distributions. To build models of gas distributions that can be used to accurately predict the gas concentration at query locations is a challenging task due to the chaotic nature of gas dispersal. We formulate this task as a regression problem. To deal with the specific properties of gas distributions, we propose a sparse Gaussian process mixture model, which allows us to accurately represent the smooth background signal and the areas with patches of high concentrations. We furthermore integrate the sparsification of the training data into an EM procedure that we apply for learning the mixture components and the gating function. Our approach has been implemented and tested using datasets recorded with a real mobile robot equipped with an electronic nose. The experiments demonstrate that our technique is well-suited for predicting gas concentrations at new query locations and that it outperforms alternative and previously proposed methods in robotics. }, year = {2009} } @inproceedings{Bouguerra274878, author = {Bouguerra, Abdelbaki and Andreasson, Henrik and Lilienthal, Achim J. and {\AA}strand, Bj{\"o}rn and R{\"o}gnvaldsson, Thorsteinn}, booktitle = {Proceedings of the 4th European conference on mobile robots (ECMR) : }, institution = {Örebro University, School of Science and Technology}, institution = {Halmstad University}, institution = {Halmstad University, Sweden}, pages = {93--98}, title = {MALTA : a system of multiple autonomous trucks for load transportation}, keywords = {Autonomous Vehicles, Load Handling, AGVs}, abstract = {This paper presents an overview of an autonomousrobotic material handling system. The goal of the system is toextend the functionalities of traditional AGVs to operate in highlydynamic environments. Traditionally, the reliable functioning ofAGVs relies on the availability of adequate infrastructure tosupport navigation. In the target environments of our system,such infrastructure is difficult to setup in an efficient way.Additionally, the location of objects to handle are unknown,which requires that the system be able to detect and track objectpositions at runtime. Another requirement of the system is to beable to generate trajectories dynamically, which is uncommon inindustrial AGV systems. }, ISBN = {978-953-6037-54-4}, year = {2009} } @inproceedings{Stoyanov524115, author = {Stoyanov, Todor and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE International Conference on Advanced Robotics (ICAR) : }, institution = {Örebro University, School of Science and Technology}, note = {Proceedings athttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5166725}, title = {Maximum Likelihood Point Cloud Acquisition from a Rotating Laser Scanner on a Moving Platform}, abstract = {This paper describes an approach to acquire locally consistent range data scans from a moving sensor platform. Data from a vertically mounted rotating laser scanner and odometry position estimates are fused and used to estimate maximum likelihood point clouds. An estimation algorithm is applied to reduce the accumulated error after a full rotation of the range finder. A configuration consisting of a SICK laser scanner mounted on a rotational actuator is described and used to evaluate the proposed approach. The data sets analyzed suggest a significant improvement in point cloud consistency, even over a short travel distance. }, URL = {https://ieeexplore.ieee.org/abstract/document/5174672}, year = {2009} } @inproceedings{Stoyanov274893, author = {Stoyanov, Todor and Lilienthal, Achim J.}, booktitle = {International conference on advanced robotics, ICAR 2009. : }, institution = {Örebro University, School of Science and Technology}, pages = {1--6}, title = {Maximum likelihood point cloud acquisition from a mobile platform}, abstract = {This paper describes an approach to acquire locally consistent range data scans from a moving sensor platform. Data from a vertically mounted rotating laser scanner and odometry position estimates are fused and used to estimate maximum likelihood point clouds. An estimation algorithm is applied to reduce the accumulated error after a full rotation of the range finder. A configuration consisting of a SICK laser scanner mounted on a rotational actuator is described and used to evaluate the proposed approach. The data sets analyzed suggest a significant improvement in point cloud consistency, even over a short travel distance. }, ISBN = {978-1-4244-4855-5}, year = {2009} } @inproceedings{Reggente274869, author = {Reggente, Matteo and Lilienthal, Achim J.}, booktitle = {Proceedings of Eurosensors XXIII conference : }, institution = {Örebro University, School of Science and Technology}, note = {Ing{\aa}r i: Procedia Chemistry (ISSN: 1876-6196) Volume 1, Issue 1, 2009}, pages = {481--484}, title = {Statistical evaluation of the kernel DM+V/W algorithm for building gas distribution maps in uncontrolled environments}, series = {Procedia Chemistry}, number = {1}, volume = {1}, DOI = {10.1016/j.proche.2009.07.120}, keywords = {gas distribution; e-nose; gas sensing; mobile robots; kernel density estimation; model evaluation}, abstract = {In this paper we present a statistical evaluation of the Kernel DM+V/W algorithm to build two-dimensional gas distribution maps with a mobile robot. In addition to gas sensor measurements from an "e-nose" the Kernel DM+V/W algorithm also takes into account wind information received from an ultrasonic anemometer. We evaluate the method based on real measurements in three uncontrolled environments with very different properties. As a measure for the model quality we compute how well unseen measurements are predicted in terms of the data likelihood. A paired Wilcoxon signed rank test shows a significant improvement (at a confidence level of 95%) of the model quality when using wind information. }, year = {2009} } @inproceedings{Reggente274906, author = {Reggente, Matteo and Lilienthal, Achim J.}, booktitle = {Olfaction and electronic nose : }, institution = {Örebro University, School of Science and Technology}, pages = {109--112}, title = {Three-dimensional statistical gas distribution mapping in an uncontrolled indoor environment}, series = {AIP conference proceedings}, number = {1137}, DOI = {10.1063/1.3156484}, keywords = {3D-gas distribution, e-nose, gas sensing, mobile robots, kernel density estimation, model evaluation}, abstract = {In this paper we present a statistical method to build three-dimensional gas distribution maps (3D-DM). The proposed mapping technique uses kernel extrapolation with a tri-variate Gaussian kernel that models the likelihood that a reading represents the concentration distribution at a distant location in the three dimensions. The method is evaluated using a mobile robot equipped with three "e-noses" mounted at different heights. Initial experiments in an uncontrolled indoor environment are presented and evaluated with respect to the ability of the 3D map, computed from the lower and upper nose, to predict the map from the middle nose. }, ISBN = {978-0-7354-0674-2}, year = {2009} } @inproceedings{Reggente274849, author = {Reggente, Matteo and Lilienthal, Achim J.}, booktitle = {IEEE sensors, vols 1-3 : }, institution = {Örebro University, School of Science and Technology}, pages = {1637--1642}, title = {Using local wind information for gas distribution mapping in outdoor environments with a mobile robot}, DOI = {10.1109/ICSENS.2009.5398498}, abstract = {In this paper we introduce a statistical method tobuild two-dimensional gas distribution maps (Kernel DM+V/Walgorithm). In addition to gas sensor measurements, the proposedmethod also takes into account wind information by modelingthe information content of the gas sensor measurements as abivariate Gaussian kernel whose shape depends on the measuredwind vector. We evaluate the method based on real measurementsin an outdoor environment obtained with a mobile robot thatwas equipped with gas sensors and an ultrasonic anemometerfor wind measurements. As a measure of the model quality wecompute how well unseen measurements are predicted in termsof the data likelihood. The initial results are encouraging andshow a clear improvement of the proposed method compared tothe case where wind is not considered. }, ISBN = {978-1-4244-4548-6}, year = {2009} } @incollection{Reggente613834, author = {Reggente, Matteo and Lilienthal, Achim J.}, booktitle = {2009 IEEE SENSORS, VOLS 1-3 : }, institution = {Örebro University, School of Science and Technology}, pages = {1715--1720}, title = {Using local wind information for gas distribution mapping in outdoor environments with a mobile robot}, series = {2009 IEEE SENSORS, VOLS 1-3}, DOI = {10.1109/ICSENS.2009.5398498}, abstract = {In this paper we introduce a statistical method to build two-dimensional gas distribution maps (Kernel DM+V/W algorithm). In addition to gas sensor measurements, the proposed method also takes into account wind information by modeling the information content of the gas sensor measurements as a bivariate Gaussian kernel whose shape depends on the measured wind vector. We evaluate the method based on real measurements in an outdoor environment obtained with a mobile robot that was equipped with gas sensors and an ultrasonic anemometer for wind measurements. As a measure of the model quality we compute how well unseen measurements are predicted in terms of the data likelihood. The initial results are encouraging and show a clear improvement of the proposed method compared to the case where wind is not considered. }, ISBN = {978-1-4244-4548-6}, year = {2009} } @inproceedings{Magnusson137559, author = {Magnusson, Martin and N{\"u}chter, Andreas and L{\"o}rken, Christopher and Lilienthal, Achim J. and Hertzberg, Joachim}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Workshop : }, institution = {Örebro University, Department of Technology}, institution = {Institute of Computer Science, University of Osnabrück, Osnabrück, Germany}, institution = {Institute of Computer Science, University of Osnabrück, Osnabrück, Germany}, institution = {Institute of Computer Science, University of Osnabrück, Osnabrück, Germany}, title = {3D mapping the Kvarntorp mine : a rield experiment for evaluation of 3D scan matching algorithms}, keywords = {Scan matching, registration, SLAM}, abstract = {This paper presents the results of a field experiment in the Kvarntorp mine outside of Örebro in Sweden. 3D mapping of the underground mine has been used to compare two scan matching methods, namely the iterative closest point algorithm (ICP) and the normal distributions transform (NDT). The experimental results of the algorithm are compared in terms of robustness and speed. For robustness we measure how reliably 3D scans are registered with respect to different starting pose estimates. Speed is evaluated running the authors’ best implementations on the same hardware. This leads to an unbiased comparison. In these experiments, NDT was shown to converge form a larger range of initial pose estimates than ICP, and to perform faster. }, year = {2008} } @article{Andreasson158115, author = {Andreasson, Henrik and Duckett, Tom and Lilienthal, Achim J.}, institution = {Örebro University, Department of Technology}, institution = {University of Lincoln, University of Lincoln, UK}, journal = {IEEE Transactions on Robotics}, number = {5}, pages = {991--1001}, title = {A Minimalistic Approach to Appearance-Based Visual SLAM}, volume = {24}, DOI = {10.1109/TRO.2008.2004642}, keywords = {Omnidirectional vision, simultaneous localization and mapping (SLAM)}, abstract = {This paper presents a vision-based approach to SLAM in indoor / outdoor environments with minimalistic sensing and computational requirements. The approach is based on a graph representation of robot poses, using a relaxation algorithm to obtain a globally consistent map. Each link corresponds to a relative measurement of the spatial relation between the two nodes it connects. The links describe the likelihood distribution of the relative pose as a Gaussian distribution. To estimate the covariance matrix for links obtained from an omni-directional vision sensor, a novel method is introduced based on the relative similarity of neighbouring images. This new method does not require determining distances to image features using multiple view geometry, for example. Combined indoor and outdoor experiments demonstrate that the approach can handle qualitatively different environments (without modification of the parameters), that it can cope with violations of the “flat floor assumption” to some degree, and that it scales well with increasing size of the environment, producing topologically correct and geometrically accurate maps at low computational cost. Further experiments demonstrate that the approach is also suitable for combining multiple overlapping maps, e.g. for solving the multi-robot SLAM problem with unknown initial poses. }, year = {2008} } @article{Persson137571, author = {Persson, Martin and Duckett, Tom and Lilienthal, Achim J.}, institution = {Örebro University, School of Science and Technology}, institution = {Örebro University, Department of Natural Sciences}, institution = {Department of Computing and Informatics, University of Lincoln, Lincoln, UK}, journal = {Robotics and Autonomous Systems}, number = {6}, pages = {483--492}, title = {Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping}, volume = {56}, DOI = {10.1016/j.robot.2008.03.002}, keywords = {Semantic Mapping, Aerial Images, Mobile Robotics}, abstract = {This work investigates the use of semantic information to link ground level occupancy maps and aerial images. A ground level semantic map, which shows open ground and indicates the probability of cells being occupied by walls of buildings, is obtained by a mobile robot equipped with an omni-directional camera, GPS and a laser range finder. This semantic information is used for local and global segmentation of an aerial image. The result is a map where the semantic information has been extended beyond the range of the robot sensors and predicts where the mobile robot can find buildings and potentially driveable ground. }, year = {2008} } @inproceedings{Stachniss137562, author = {Stachniss, Cyril and Plagemann, Christian and Lilienthal, Achim J. and Burgard, Wolfram}, booktitle = {Robotics : science and systems IV}, institution = {Örebro University, School of Science and Technology}, institution = {Dept. for Computer Science, Albert-Ludwigs-University, Freiburg, Germany}, institution = {Dept. for Computer Science, Albert-Ludwigs-University, Freiburg, Germany}, institution = {Dept. for Computer Science, Albert-Ludwigs-University, Freiburg, Germany}, note = {Accepted as oral presentation (acceptance rate {\&}lt;15{\%}), selected from these papers as one of the best conference papers}, pages = {310--317}, title = {Gas distribution modeling using sparse Gaussian process mixture models}, volume = {4}, DOI = {10.15607/rss.2008.iv.040}, keywords = {Gas distribution modeling, gas sensing, Gaussian processes, mixture models}, abstract = {In this paper, we consider the problem of learning a two dimensional spatial model of a gas distribution with a mobile robot. Building maps that can be used to accurately predict the gas concentration at query locations is a challenging task due to the chaotic nature of gas dispersal. We present an approach that formulates this task as a regression problem. To deal with the specific properties of typical gas distributions, we propose a sparse Gaussian process mixture model. This allows us to accurately represent the smooth background signal as well as areas of high concentration. We integrate the sparsification of the training data into an EM procedure used for learning the mixture components and the gating function. Our approach has been implemented and tested using datasets recorded with a real mobile robot equipped with an electronic nose. We demonstrate that our models are well suited for predicting gas concentrations at new query locations and that they outperform alternative methods used in robotics to carry out in this task. }, ISBN = {9780262513098}, year = {2008} } @inproceedings{Persson137594, author = {Persson, Martin and Duckett, Tom and Lilienthal, Achim J.}, booktitle = {Recent Progress in Robotics : Viable Robotic Service to Human}, institution = {Örebro University, School of Science and Technology}, institution = {Department of Computing and Informatics, University of Lincoln, Lincoln, UK}, pages = {157--169}, title = {Improved mapping and image segmentation by using semantic information to link aerial images and ground-level information}, series = {Lecture Notes in Control and Information Sciences}, number = {370}, DOI = {10.1007/978-3-540-76729-9_13}, keywords = {Semantic Mapping, Aerial Images, Mobile Robotics}, abstract = {This paper investigates the use of semantic information to link ground-level occupancy maps and aerial images. A ground-level semantic map is obtained by a mobile robot equipped with an omnidirectional camera, differential GPS and a laser range finder. The mobile robot uses a virtual sensor for building detection (based on omnidirectional images) to compute the ground-level semantic map, which indicates the probability of the cells being occupied by the wall of a building. These wall estimates from a ground perspective are then matched with edges detected in an aerial image. The result is used to direct a region- and boundary-based segmentation algorithm for building detection in the aerial image. This approach addresses two difficulties simultaneously: 1) the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in monocular aerial images. With the suggested method building outlines can be detected faster than the mobile robot can explore the area by itself, giving the robot an ability to “see” around corners. At the same time, the approach can compensate for the absence of elevation data in segmentation of aerial images. Our experiments demonstrate that ground-level semantic information (wall estimates) allows to focus the segmentation of the aerial image to find buildings and produce a ground-level semantic map that covers a larger area than can be built using the onboard sensors. }, ISBN = {978-3-540-76728-2}, year = {2008} } @inproceedings{Valgren137555, author = {Valgren, Christoffer and Lilienthal, Achim J.}, booktitle = {2008 IEEE international conference on robotics and automation : }, institution = {Örebro University, Department of Technology}, institution = {Örebro University, Örebro, Sweden}, pages = {1856--1861}, eid = {4543477}, title = {Incremental spectral clustering and seasons : appearance-based localization in outdoor environments}, series = {IEEE International Conference on Robotics and Automation}, DOI = {10.1109/ROBOT.2008.4543477}, keywords = {Apperance based localisation, topological mapping, spectral clustering}, abstract = {The problem of appearance-based mapping and navigation in outdoor environments is far from trivial. In this paper, an appearance-based topological map, covering a large, mixed indoor and outdoor environment, is built incrementally by using panoramic images. The map is based on image similarity, so that the resulting segmentation of the world corresponds closely to the human concept of a place. Using high-resolution images and the epipolar constraint, the resulting map is shown to be very suitable for localization, even when the environment has undergone seasonal changes. }, ISBN = {978-1-4244-1646-2}, year = {2008} } @inproceedings{Huhle139019, author = {Huhle, Benjamin and Magnusson, Martin and Straßer, Wolfgang and Lilienthal, Achim J.}, booktitle = {2008 IEEE international conference on robotics and automation : }, institution = {Örebro University, Department of Technology}, institution = {Department of Graphical Interactive Systems WSI/GRIS, University of Tübingen, Germany}, institution = {Department of Graphical Interactive Systems WSI/GRIS, University of Tübingen, Germany}, pages = {4025--4030}, eid = {4543829}, title = {Registration of colored 3D point clouds with a Kernel-based extension to the normal distributions transform}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ROBOT.2008.4543829}, abstract = {We present a new algorithm for scan registration of colored 3D point data which is an extension to the Normal Distributions Transform (NDT). The probabilistic approach of NDT is extended to a color-aware registration algorithm by modeling the point distributions as Gaussian mixture-models in color space. We discuss different point cloud registration techniques, as well as alternative variants of the proposed algorithm. Results showing improved robustness of the proposed method using real-world data acquired with a mobile robot and a time-of-flight camera are presented. }, ISBN = {978-1-4244-1646-2}, year = {2008} } @inproceedings{Trincavelli138918, author = {Trincavelli, Marco and Reggente, Matteo and Coradeschi, Silvia and Loutfi, Amy and Ishida, Hiroshi and Lilienthal, Achim J.}, booktitle = {2008 IEEE/RSJ International Conference on Intelligent Robots and Systems : }, institution = {Örebro University, Department of Technology}, institution = {Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan}, note = {Funding Agency:Japan Society for the Promotion of Science}, pages = {2210--2215}, eid = {4650755}, title = {Towards environmental monitoring with mobile robots}, DOI = {10.1109/IROS.2008.4650755}, keywords = {Mobile, robot, olfaction}, abstract = {In this paper we present initial experiments towards environmental monitoring with a mobile platform. A prototype of a pollution monitoring robot was set up which measures the gas distribution using an “electronic nose” and provides three dimensional wind measurements using an ultrasonic anemometer. We describe the design of the robot and the experimental setup used to run trials under varying environmental conditions. We then present the results of the gas distribution mapping. The trials which were carried out in three uncontrolled environments with very different properties: an enclosed indoor area, a part of a long corridor with open ends and a high ceiling, and an outdoor scenario are presented and discussed. }, ISBN = {978-1-4244-2057-5}, year = {2008} } @inproceedings{Lilienthal138564, author = {Lilienthal, Achim J. and Loutfi, Amy and Blanco, Jose Luis and Galindo, Cipriano and Gonzalez, Javier}, booktitle = {ECMR 2007 : Proceedings of the European Conference on Mobile Robots}, institution = {Örebro University, Department of Technology}, institution = {Dept. of System Engineering and Automation, University of Malaga, Malaga, Spain}, institution = {Dept. of System Engineering and Automation, University of Malaga, Malaga, Spain}, institution = {Dept. of System Engineering and Automation, University of Malaga, Malaga, Spain}, pages = {126--131}, title = {A Rao-Blackwellisation approach to GDM-SLAM : integrating SLAM and gas distribution mapping (GDM)}, abstract = {In this paper we consider the problem of creating a two dimensional spatial representation of gas distribution with a mobile robot. In contrast to previous approaches to the problem of gas distribution mapping (GDM) we do not assume that the robot has perfect knowledge about its position. Instead we develop a probabilistic framework for simultaneous localisation and occupancy and gas distribution mapping (GDM/SLAM) that allows to account for the uncertainty about the robot’s position when computing the gas distribution map. Considering the peculiarities of gas sensing in real-world environments, we show which dependencies in the posterior over occupancy and gas distribution maps can be neglected under certain practical assumptions. We develop a Rao-Blackwellised particle filter formulation of the GDM/SLAM problem that allows to plug in any algorithm to compute a gas distribution map from a sequence of gas sensor measurements and a known trajectory. In this paper we use the Kernel Based Gas Distribution Mapping (Kernel- GDM) method. As a first step towards outdoor gas distribution mapping we present results obtained in a large, uncontrolled, partly open indoor environment. }, URL = {http://ecmr07.informatik.uni-freiburg.de/proceedings/ECMR07_0060.pdf}, year = {2007} } @inproceedings{Persson138561, author = {Persson, Martin and Duckett, Tom and Lilienthal, Achim J.}, booktitle = {Proceedings of the IROS Workshop "From Sensors to Human Spatial Concepts" : }, institution = {Örebro University, Department of Technology}, institution = {Department of Computing and Informatics, University of Lincoln, Lincoln, Uk}, pages = {17--24}, title = {Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping}, abstract = {This paper investigates the use of semantic information to link ground-level occupancy maps and aerial images. A ground-level semantic map is obtained by a mobile robot equipped with an omnidirectional camera, differential GPS and a laser range finder. The mobile robot uses a virtual sensor for building detection (based on omnidirectional images) to compute the ground-level semantic map, which indicates the probability of the cells being occupied by the wall of a building. These wall estimates from a ground perspective are then matched with edges detected in an aerial image. The result is used to direct a region- and boundary-based segmentation algorithm for building detection in the aerial image. This approach addresses two difficulties simultaneously: 1) the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in monocular aerial images. With the suggested method building outlines can be detected faster than the mobile robot can explore the area by itself, giving the robot an ability to "see" around corners. At the same time, the approach can compensate for the absence of elevation data in segmentation of aerial images. Our experiments demonstrate that ground-level semantic information (wall estimates) allows to focus the segmentation of the aerial image to find buildings and produce a ground-level semantic map that covers a larger area than can be built using the onboard sensors. }, year = {2007} } @inproceedings{Andreasson138559, author = {Andreasson, Henrik and Magnusson, Martin and Lilienthal, Achim}, booktitle = {2007 IEEE/RSJ international conference on intelligent robots and systems : }, institution = {Örebro University, Department of Technology}, institution = {Örebro University, Department of Natural Sciences}, pages = {3429--3435}, eid = {4399381}, title = {Has something changed here? : Autonomous difference detection for security patrol robots}, DOI = {10.1109/IROS.2007.4399381}, abstract = {This paper presents a system for autonomous change detection with a security patrol robot. In an initial step a reference model of the environment is created and changes are then detected with respect to the reference model as differences in coloured 3D point clouds, which are obtained from a 3D laser range scanner and a CCD camera. The suggested approach introduces several novel aspects, including a registration method that utilizes local visual features to determine point correspondences (thus essentially working without an initial pose estimate) and the 3D-NDT representation with adaptive cell size to efficiently represent both the spatial and colour aspects of the reference model. Apart from a detailed description of the individual parts of the difference detection system, a qualitative experimental evaluation in an indoor lab environment is presented, which demonstrates that the suggested system is able register and detect changes in spatial 3D data and also to detect changes that occur in colour space and are not observable using range values only. }, ISBN = {978-1-4244-0912-9}, year = {2007} } @inproceedings{Cielniak137568, author = {Cielniak, Grzegorz and Duckett, Tom and Lilienthal, Achim J.}, booktitle = {2007 IEEE/RSJ international conference on intelligent robots and systems : }, institution = {Örebro University, Department of Technology}, institution = {Department of Computing and Informatics, University of Lincoln, Lincoln, United Kingdom}, institution = {Department of Computing and Informatics, University of Lincoln, Lincoln, United Kingdom}, pages = {3436--3441}, title = {Improved data association and occlusion handling for vision-based people tracking by mobile robots}, DOI = {10.1109/IROS.2007.4399507}, keywords = {Person tracking, robot vision, occlusion handling}, abstract = {This paper presents an approach for tracking multiple persons using a combination of colour and thermal vision sensors on a mobile robot. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is then incorporated into the tracker. }, ISBN = {978-1-4244-0912-9}, year = {2007} } @inproceedings{Persson138566, author = {Persson, Martin and Duckett, Tom and Lilienthal, Achim J.}, booktitle = {Proceedings of the IEEE international conference on advanced robotics : ICAR 2007}, institution = {Örebro University, Department of Technology}, institution = {Department of Computing and Informatics, University of Lincoln, Lincoln, United Kingdom}, pages = {924--929}, title = {Improved mapping and image segmentation by using semantic information to link aerial images and ground-level information}, abstract = {This paper investigates the use of semantic information to link ground-level occupancy maps and aerial images. In the suggested approach a ground-level semantic map is obtained by a mobile robot equipped with an omnidirectional camera, differential GPS and a laser range finder. The mobile robot uses a virtual sensor for building detection (based on omnidirectional images) to compute the ground-level semantic map, which indicates the probability of the cells being occupied by the wall of a building. These wall estimates from a ground perspective are then matched with edges detected in an aerial image. The result is used to direct a region- and boundary-based segmentation algorithm for building detection in the aerial image. This approach addresses two difficulties simultaneously: 1) the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in monocular aerial images. With the suggested method building outlines can be detected faster than the mobile robot can explore the area by itself, giving the robot an ability to "see" around corners. At the same time, the approach can compensate for the absence of elevation data in segmentation of aerial images. Our experiments demonstrate that ground-level semantic information (wall estimates) allows to focus the segmentation of the aerial image to find buildings and produce a groundlevel semantic map that covers a larger area than can be built using the onboard sensors along the robot trajectory. }, year = {2007} } @inproceedings{Valgren139071, author = {Valgren, Christoffer and Duckett, Tom and Lilienthal, Achim J.}, booktitle = {2007 IEEE international conference on robotics and automation (ICRA) : }, institution = {Örebro University, Department of Technology}, institution = {University of Lincoln, United Kingdom}, note = {Funding Agency:The Swedish Defence Material Administration}, pages = {4283--4288}, title = {Incremental spectral clustering and its application to topological mapping}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ROBOT.2007.364138}, abstract = {This paper presents a novel use of spectral clustering algorithms to support cases where the entries in the affinity matrix are costly to compute. The method is incremental – the spectral clustering algorithm is applied to the affinity matrix after each row/column is added – which makes it possible to inspect the clusters as new data points are added. The method is well suited to the problem of appearance-based, on-line topological mapping for mobile robots. In this problem domain, we show that we can reduce environment-dependent parameters of the clustering algorithm to just a single, intuitive parameter. Experimental results in large outdoor and indoor environments show that we can close loops correctly by computing only a fraction of the entries in the affinity matrix. The accompanying video clip shows how an example map is produced by the algorithm. }, ISBN = {978-1-4244-0601-2}, year = {2007} } @inproceedings{Lilienthal138318, author = {Lilienthal, Achim J. and Loutfi, Amy and Blanco, Jose Luis and Galindo, Cipriano and Gonzalez, Javier}, booktitle = {Proceedings of ICRA Workshop on Robotic Olfaction : Towards Real Applications. ICRA 2007}, institution = {Örebro University, Department of Technology}, institution = {System Engineering and Automation Department, University of Malaga, Malaga, Spain}, institution = {System Engineering and Automation Department, University of Malaga, Malaga, Spain}, institution = {System Engineering and Automation Department, University of Malaga, Malaga, Spain}, pages = {21--28}, title = {Integrating SLAM into gas distribution mapping}, abstract = {In this paper we consider the problem of creating a spatial representation of a gas distribution in an environment using a mobile robot equipped with gas sensors. The gas distribution mapping method used models the information content of a given measurement about the average concentration distribution with respect to the point of measurement. In this paper, we present an extension which can consider the uncertainty about the robot’s position in the gas distribution mapping. We present a preliminary result where a mobile robot equipped with gas sensors creates a map of a large indoor environment, using both spatial and olfactory information. }, year = {2007} } @inproceedings{Andreasson138560, author = {Andreasson, Henrik and Duckett, Tom and Lilienthal, Achim J.}, booktitle = {2007 IEEE international conference on robotics and automation (ICRA) : }, institution = {Örebro University, Department of Technology}, institution = {Dept. of Computing & Informatics, University of Lincoln, Lincoln, United Kingdom}, pages = {4096--4101}, eid = {4209726}, title = {Mini-SLAM : minimalistic visual SLAM in large-scale environments based on a new interpretation of image similarity}, series = {IEEE International Conference on Robotics and Automation ICRA}, DOI = {10.1109/ROBOT.2007.364108}, abstract = {This paper presents a vision-based approach to SLAM in large-scale environments with minimal sensing and computational requirements. The approach is based on a graphical representation of robot poses and links between the poses. Links between the robot poses are established based on odometry and image similarity, then a relaxation algorithm is used to generate a globally consistent map. To estimate the covariance matrix for links obtained from the vision sensor, a novel method is introduced based on the relative similarity of neighbouring images, without requiring distances to image features or multiple view geometry. Indoor and outdoor experiments demonstrate that the approach scales well to large-scale environments, producing topologically correct and geometrically accurate maps at minimal computational cost. Mini-SLAM was found to produce consistent maps in an unstructured, large-scale environment (the total path length was 1.4 km) containing indoor and outdoor passages. }, ISBN = {978-1-4244-0601-2}, year = {2007} } @inproceedings{Andreasson138558, author = {Andreasson, Henrik and Triebel, Rudolph and Lilienthal, Achim J.}, booktitle = {Autonomos Agents and Robots : }, institution = {Örebro University, Department of Technology}, institution = {Department of Computer Science, University of Freiburg, Freiburg, Germany}, pages = {83--90}, eid = {4399381}, publisher = {Springer}, title = {Non-iterative Vision-based Interpolation of 3D Laser Scans}, series = {Studies in Computational Intelligence}, number = {76}, volume = {76}, DOI = {10.1007/978-3-540-73424-6_10}, keywords = {3D range sensor, laser range scanner, vision-based depth interpolation, 3D vision}, abstract = {3D range sensors, particularly 3D laser range scanners, enjoy a rising popularity and are used nowadays for many different applications. The resolution 3D range sensors provide in the image plane is typically much lower than the resolution of a modern colour camera. In this chapter we focus on methods to derive a highresolution depth image from a low-resolution 3D range sensor and a colour image. The main idea is to use colour similarity as an indication of depth similarity, based on the observation that depth discontinuities in the scene often correspond to colour or brightness changes in the camera image. We present five interpolation methods and compare them with an independently proposed method based on Markov random fields. The proposed algorithms are non-iterative and include a parameter-free vision-based interpolation method. In contrast to previous work, we present ground truth evaluation with real world data and analyse both indoor and outdoor data. }, ISBN = {978-3-540-73423-9}, year = {2007} } @inproceedings{Persson138567, author = {Persson, Martin and Duckett, Tom and Valgren, Christoffer and Lilienthal, Achim J.}, booktitle = {Proceedings of the 2007 IEEE International symposium on computational intelligence in robotics and automation, CIRA 2007 : }, institution = {Örebro University, Department of Technology}, institution = {Department of Computing and Informatics, University of Lincoln, Lincoln, United Kingdom}, institution = {Department of Technology, Örebro University, Örebro, Sweden}, note = {Funding Agency:Swedish Defence Material Administration}, pages = {236--242}, eid = {4269870}, title = {Probabilistic semantic mapping with a virtual sensor for building/nature detection}, DOI = {10.1109/CIRA.2007.382870}, abstract = {In human-robot communication it is often important to relate robot sensor readings to concepts used by humans. We believe that access to semantic maps will make it possible for robots to better communicate information to a human operator and vice versa. The main contribution of this paper is a method that fuses data from different sensor modalities, range sensors and vision sensors are considered, to create a probabilistic semantic map of an outdoor environment. The method combines a learned virtual sensor (understood as one or several physical sensors with a dedicated signal processing unit for recognition of real world concepts) for building detection with a standard occupancy map. The virtual sensor is applied on a mobile robot, combining classifications of sub-images from a panoramic view with spatial information (location and orientation of the robot) giving the likely locations of buildings. This information is combined with an occupancy map to calculate a probabilistic semantic map. Our experiments with an outdoor mobile robot show that the method produces semantic maps with correct labeling and an evident distinction between "building" objects from "nature" objects }, ISBN = {978-1-4244-0789-7}, year = {2007} } @article{Magnusson138557, author = {Magnusson, Martin and Lilienthal, Achim J. and Duckett, Tom}, institution = {Örebro University, Department of Technology}, institution = {Department of Computing and Informatics, University of Lincoln, Lincoln, United Kingdom}, journal = {Journal of Field Robotics}, note = {Special issue on mining robotics.}, number = {10}, pages = {803--827}, title = {Scan registration for autonomous mining vehicles using 3D-NDT}, volume = {24}, DOI = {10.1002/rob.20204}, abstract = {Scan registration is an essential sub-task when building maps based on range finder data from mobile robots. The problem is to deduce how the robot has moved between consecutive scans, based on the shape of overlapping portions of the scans. This paper presents a new algorithm for registration of 3D data. The algorithm is a generalisation and improvement of the normal distributions transform (NDT) for 2D data developed by Biber and Straßer, which allows for accurate registration using a memory-efficient representation of the scan surface. A detailed quantitative and qualitative comparison of the new algorithm with the 3D version of the popular ICP (iterative closest point) algorithm is presented. Results with actual mine data, some of which were collected with a new prototype 3D laser scanner, show that the presented algorithm is faster and slightly more reliable than the standard ICP algorithm for 3D registration, while using a more memory-efficient scan surface representation. }, year = {2007} } @inproceedings{Valgren138562, author = {Valgren, Christoffer and Lilienthal, Achim J.}, booktitle = {ECMR 2007 : Proceedings of the European Conference on Mobile Robots}, institution = {Örebro University, Department of Technology}, institution = {Örebro University, Örebro, Sweden}, pages = {253--258}, title = {SIFT, SURF and seasons : long-term outdoor localization using local features}, abstract = {Local feature matching has become a commonly used method to compare images. For mobile robots, a reliable method for comparing images can constitute a key component for localization and loop closing tasks. In this paper, we address the issues of outdoor appearance-based topological localization for a mobile robot over time. Our data sets, each consisting of a large number of panoramic images, have been acquired over a period of nine months with large seasonal changes (snowcovered ground, bare trees, autumn leaves, dense foliage, etc.). Two different types of image feature algorithms, SIFT and the more recent SURF, have been used to compare the images. We show that two variants of SURF, called U-SURF and SURF-128, outperform the other algorithms in terms of accuracy and speed. }, URL = {http://ecmr07.informatik.uni-freiburg.de/proceedings/ECMR07_0050.pdf}, year = {2007} } @article{Persson447970, author = {Persson, Martin and Duckett, Tom and Lilienthal, Achim J.}, institution = {Örebro University, Department of Technology}, institution = {Department of Computing and Informatics, University of Lincoln, Lincoln, UK}, journal = {Robotics and Autonomous Systems}, number = {5}, pages = {383--390}, title = {Virtual sensors for human concepts : building detection by an outdoor mobile robot}, volume = {55}, DOI = {10.1016/j.robot.2006.12.002}, abstract = {In human-robot communication it is often important to relate robot sensor readings to concepts used by humans. We suggest the use of a virtual sensor (one or several physical sensors with a dedicated signal processing unit for the recognition of real world concepts) and a method with which the virtual sensor can learn from a set of generic features. The virtual sensor robustly establishes the link between sensor data and a particular human concept. In this work, we present a virtual sensor for building detection that uses vision and machine learning to classify the image content in a particular direction as representing buildings or non-buildings. The virtual sensor is trained on a diverse set of image data, using features extracted from grey level images. The features are based on edge orientation, the configurations of these edges, and on grey level clustering. To combine these features, the AdaBoost algorithm is applied. Our experiments with an outdoor mobile robot show that the method is able to separate buildings from nature with a high classification rate, and to extrapolate well to images collected under different conditions. Finally, the virtual sensor is applied on the mobile robot, combining its classifications of sub-images from a panoramic view with spatial information (in the form of location and orientation of the robot) in order to communicate the likely locations of buildings to a remote human operator. (c) 2006 Elsevier B.V. All rights reserved. }, year = {2007} } @inproceedings{Andreasson138563, author = {Andreasson, Henrik and Lilienthal, Achim}, booktitle = {ECMR 2007 : Proceedings of the European Conference on Mobile Robots}, institution = {Örebro University, Department of Technology}, institution = {Örebro University, Department of Natural Sciences}, institution = {aass}, pages = {192--197}, title = {Vision aided 3D laser scanner based registration}, keywords = {Registration, Vision}, abstract = {This paper describes a vision and 3D laser based registration approach which utilizes visual features to identify correspondences. Visual features are obtained from the images of a standard color camera and the depth of these features is determined by interpolating between the scanning points of a 3D laser range scanner, taking into consideration the visual information in the neighbourhood of the respective visual feature. The 3D laser scanner is also used to determine a position covariance estimate of the visual feature. To exploit these covariance estimates, an ICP algorithm based on the Mahalanobis distance is applied. Initial experimental results are presented in a real world indoor laboratory environment }, URL = {http://ecmr07.informatik.uni-freiburg.de/proceedings/ECMR07_0059.pdf}, year = {2007} } @article{Lilienthal137695, author = {Lilienthal, Achim J. and Loutfi, Amy and Duckett, Tom}, institution = {Örebro University, Department of Technology}, institution = {Dept. of Computing and Informatics, UKUniversity of Lincoln, Lincoln}, journal = {Sensors}, number = {11}, pages = {1616--1678}, publisher = {M D P I AG}, title = {Airborne chemical sensing with mobile robots}, volume = {6}, DOI = {10.3390/s6111616}, keywords = {Mobile robot olfaction, gas distribution mapping, trail guidance, gas source localisation, gas source tracing, gas source declaration}, abstract = {Airborne chemical sensing with mobile robots has been an active research area since the beginning of the 1990s. This article presents a review of research work in this field, including gas distribution mapping, trail guidance, and the different subtasks of gas source localisation. Due to the difficulty of modelling gas distribution in a real world environment with currently available simulation techniques, we focus largely on experimental work and do not consider publications that are purely based on simulations. }, year = {2006} } @inproceedings{Valgren138255, author = {Valgren, Christoffer and Lilienthal, Achim J. and Duckett, Tom}, booktitle = {2006 IEEE/RSJ international conference on intelligent robots and systems : }, institution = {Örebro University, Department of Technology}, institution = {Örebro University, Örebro, Sweden}, institution = {Department of Computing and Informatics, University of Lincoln, Brayford Pool, Lincoln, United Kingdom}, pages = {3441--3447}, eid = {4058933}, title = {Incremental topological mapping using omnidirectional vision}, DOI = {10.1109/IROS.2006.282583}, abstract = {This paper presents an algorithm that builds topological maps, using omnidirectional vision as the only sensor modality. Local features are extracted from images obtained in sequence, and are used both to cluster the images into nodes and to detect links between the nodes. The algorithm is incremental, reducing the computational requirements of the corresponding batch algorithm. Experimental results in a complex, indoor environment show that the algorithm produces topologically correct maps, closing loops without suffering from perceptual aliasing or false links. Robustness to lighting variations was further demonstrated by building correct maps from combined multiple datasets collected over a period of 2 months. }, ISBN = {978-1-4244-0258-8}, year = {2006} } @inproceedings{Lilienthal138258, author = {Lilienthal, Achim J. and Duckett, Tom and Ishida, Hiroshi and Werner, Felix}, booktitle = {The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006, BioRob 2006 : }, institution = {Örebro University, Department of Technology}, institution = {Tokyo Univ. of Agriculture and Technology, Dept. of Mechanical Systems Engineering, Tokyo, Japan}, institution = {Wilhelm-Schickard Institute, University of Tübingen, Tübingen, Germany}, pages = {733--738}, eid = {1639177}, title = {Indicators of gas source proximity using metal oxide sensors in a turbulent environment}, series = {Proceedings of the IEEE RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics}, DOI = {10.1109/BIOROB.2006.1639177}, keywords = {Mobile nose, gas source localisation, turbulent gas distribution}, abstract = {This paper addresses the problem of estimating proximity to a gas source using concentration measurements. In particular, we consider the problem of gas source declaration by a mobile robot equipped with metal oxide sensors in a turbulent indoor environment. While previous work has shown that machine learning classifiers can be trained to detect close proximity to a gas source, it is difficult to interpret the learned models. This paper investigates possible underlying indicators of gas source proximity, comparing three different statistics derived from the sensor measurements of the robot. A correlation analysis of 1056 trials showed that response variance (measured as standard deviation) was a better indicator than average response. An improved result was obtained when the standard deviation was normalized to the average response for each trial, a strategy that also reduces calibration problems. }, ISBN = {978-1-4244-0039-3}, year = {2006} } @inproceedings{Jun138256, author = {Jun, Li and Lilienthal, Achim J. and Martìnez-Marìn, Tomas and Duckett, Tom}, booktitle = {2006 IEEE/RSJ international conference on intelligent robots and systems : }, institution = {Örebro University, Department of Technology}, institution = {Department of Physics, System Engineering and Signal Theory, University of Alicante, Alicante, Spain}, pages = {2656--2662}, eid = {4058792}, title = {Q-RAN : a constructive reinforcement learning approach for robot behavior learning}, DOI = {10.1109/IROS.2006.281986}, abstract = {This paper presents a learning system that uses Q-learning with a resource allocating network (RAN) for behavior learning in mobile robotics. The RAN is used as a function approximator, and Q-learning is used to learn the control policy in `off-policy' fashion that enables learning to be bootstrapped by a prior knowledge controller, thus speeding up the reinforcement learning. Our approach is verified on a PeopleBot robot executing a visual servoing based docking behavior in which the robot is required to reach a goal pose. Further experiments show that the RAN network can also be used for supervised learning prior to reinforcement learning in a layered architecture, thus further improving the performance of the docking behavior. }, ISBN = {978-1-4244-0258-8}, year = {2006} } @inproceedings{Skoglund138390, author = {Skoglund, Alexander and Duckett, Tom and Iliev, Boyko and Lilienthal, Achim J. and Palm, Rainer}, booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) ,2006 : }, institution = {Örebro University, Department of Technology}, pages = {4339--4341}, title = {Teaching by demonstration of robotic manipulators in non-stationary environments}, abstract = {In this paper we propose a system consisting of a manipulator equipped with range sensors, that is instructed to follow a trajectory demonstrated by a human teacher wearing a motion capturing device. During the demonstration a three dimensional occupancy grid of the environment is built using the range sensor information and the trajectory. The demonstration is followed by an exploration phase, where the robot undergoes self-improvement of the task, during which the occupancy grid is used to avoid collisions. In parallel a reinforcement learning (RL) agent, biased by the demonstration, learns a point-to-point task policy. When changes occur in the workspace, both the occupancy grid and the learned policy will be updated online by the system. }, year = {2006} } @inproceedings{Persson138257, author = {Persson, Martin and Duckett, Tom and Lilienthal, Achim J.}, booktitle = {Proceedings of the IROS 2006 workshop : From Sensors toHuman Spatial Concepts}, institution = {Örebro University, Department of Technology}, institution = {Department of Computing and Informatics, University of Lincoln, Lincoln, UK}, pages = {21--26}, title = {Virtual sensors for human concepts : building detection by an outdoor mobile robot}, keywords = {Human–robot communication, Human concepts, Virtual sensor, Automatic building detection, AdaBoost}, abstract = {In human–robot communication it is often important to relate robot sensor readings to concepts used by humans. We suggest the use of a virtual sensor (one or several physical sensors with a dedicated signal processing unit for the recognition of real world concepts) and a method with which the virtual sensor can learn from a set of generic features. The virtual sensor robustly establishes the link between sensor data and a particular human concept. In this work, we present a virtual sensor for building detection that uses vision and machine learning to classify the image content in a particular direction as representing buildings or non-buildings. The virtual sensor is trained on a diverse set of image data, using features extracted from grey level images. The features are based on edge orientation, the configurations of these edges, and on grey level clustering. To combine these features, the AdaBoost algorithm is applied. Our experiments with an outdoor mobile robot show that the method is able to separate buildings from nature with a high classification rate, and to extrapolate well to images collected under different conditions. Finally, the virtual sensor is applied on the mobile robot, combining its classifications of sub-images from a panoramic view with spatial information (in the form of location and orientation of the robot) in order to communicate the likely locations of buildings to a remote human operator. }, year = {2006} } @inproceedings{Andreasson138254, author = {Andreasson, Henrik and Lilienthal, Achim J. and Triebel, Rudolph}, booktitle = {Proceedings of the Third International Conference on Autonomous Robots and Agents : }, institution = {Örebro University, Department of Technology}, institution = {Department of Computer Science, University of Freiburg, Germany}, pages = {455--460}, title = {Vision based interpolation of 3D laser scans}, keywords = {3D range sensor, laser range scanner, vision-based depth interpolation, 3D vision}, abstract = {3D range sensors, particularly 3D laser range scanners, enjoy a rising popularity and are used nowadays for many different applications. The resolution 3D range sensors provide in the image plane is typically much lower than the resolution of a modern color camera. In this paper we focus on methods to derive a high-resolution depth image from a low-resolution 3D range sensor and a color image. The main idea is to use color similarity as an indication of depth similarity, based on the observation that depth discontinuities in the scene often correspond to color or brightness changes in the camera image. We present five interpolation methods and compare them with an independently proposed method based on Markov Random Fields. The algorithms proposed in this paper are non-iterative and include a parameter-free vision-based interpolation method. In contrast to previous work, we present ground truth evaluation with real world data and analyse both indoor and outdoor data. Further, we suggest and evaluate four methods to determine a confidence measure for the accuracy of interpolated range values. }, year = {2006} } @inproceedings{Lilienthal138296, author = {Lilienthal, Achim J. and Streichert, Felix and Zell, Andreas}, booktitle = {Proceedings of the 2005 IEEE International Conference on Robotics and Automation : }, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, pages = {3564--3569}, eid = {1570662}, title = {Model-based shape analysis of gas concentration grinmaps for improved gas source localisation}, DOI = {10.1109/ROBOT.2005.1570662}, keywords = {Gas concentration mapping, gas source localisation}, abstract = {This work addresses the capability to use concentration gridmaps to locate a static gas source. In previous works it was found that depending on the shape of the mapped gas distribution (corresponding to different airflow conditions) the gas source location can be sometimes approximated with high accuracy by the maximum in the concentration map while this is not possible in other cases. This paper introduces a method to distinguish both cases by analysing the shape of the obtained concentration map in terms of a model of the time-averaged gas distribution known from physics. The parameters of the model that approximates the concentration map most closely are determined by nonlinear least squares fitting using evolution strategies (ES). The best fit also provides a better estimate of the gas source position in situations where the concentration maximum estimate fails. Different methods to select the most truthful estimate are introduced in this work and a comparison regarding their accuracy is presented, based on a total of 34h of concentration mapping experiments. }, ISBN = {0-7803-8914-X}, year = {2005} } @article{Lilienthal137827, author = {Lilienthal, Achim J. and Duckett, Tom}, institution = {Örebro University, Department of Technology}, institution = {University of Tübingen, WSI, Tübingen, Germany}, journal = {Robotics and Autonomous Systems}, number = {1}, pages = {3--16}, title = {Building gas concentration gridmaps with a mobile robot}, volume = {48}, DOI = {10.1016/j.robot.2004.05.002}, keywords = {Mobile nose, Gas distribution mapping, Gas source localisation}, abstract = {This paper addresses the problem of mapping the structure of a gas distribution by creating concentration gridmaps from the data collected by a mobile robot equipped with gas sensors. By contrast to metric gridmaps extracted from sonar or laser range scans, a single measurement from a gas sensor provides information about a comparatively small area. To overcome this problem, a mapping technique is introduced that uses a Gaussian weighting function to model the decreasing likelihood that a particular reading represents the true concentration with respect to the distance from the point of measurement. This method is evaluated in terms of its suitability regarding the slow response and recovery of the gas sensors, and experimental comparisons of different exploration strategies are presented. The stability of the mapped structures and the capability to use concentration gridmaps to locate a gas source are also discussed. }, year = {2004} } @article{Lilienthal137816, author = {Lilienthal, Achim J. and Duckett, Tom}, institution = {Örebro University, Department of Technology}, institution = {W.-Schickard-Inst. for Comp. Science, University of Tübingen, Tübingen, Germany}, journal = {Advanced Robotics}, number = {8}, pages = {817--834}, title = {Experimental analysis of gas-sensitive Braitenberg vehicles}, volume = {18}, DOI = {10.1163/1568553041738103}, keywords = {Gas-sensitive mobile robot, gas source localization, turbulent gas distribution, Braitenberg vehicle}, abstract = {This article addresses the problem of localising a static gas source in an indoor environment by a mobile robot. In contrast to previous works, the environment is not artificially ventilated to produce a strong unidirectional airflow. Here, the dominant transport mechanisms of gas molecules are turbulence and convection flow rather than diffusion, which results in a patchy, chaotically fluctuating gas distribution. Two Braitenberg-type strategies (positive and negative tropotaxis) based on the instantaneously measured spatial concentration gradient were investigated. Both strategies were shown to be of potential use for gas source localisation. As a possible solution to the problem of gas source declaration (the task of determining with certainty that the gas source has been found), an indirect localisation strategy based on exploration and concentration peak avoidance is suggested. Here, a gas source is located by exploiting the fact that local concentration maxima occur more frequently near the gas source compared to distant regions }, year = {2004} } @inproceedings{Lilienthal138299, author = {Lilienthal, Achim J. and Ulmer, Holger and Fr{\"o}hlich, Holger and St{\"u}tzle, Andreas and Werner, Felix and Zell, Andreas}, booktitle = {2004 IEEE International Conference on Robotics and Automation : }, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, pages = {1430--1435}, title = {Gas source declaration with a mobile robot}, DOI = {10.1109/ROBOT.2004.1308025}, abstract = {As a sub-task of the general gas source localisation problem, gas source declaration is the process of determining the certainty that a source is in the immediate vicinity. Due to the turbulent character of gas transport in a natural indoor environment, it is not sufficient to search for instantaneous concentration maxima, in order to solve this task. Therefore, this paper introduces a method to classify whether an object is a gas source or not from a series of concentration measurements, recorded while the robot performs a rotation manoeuvre in front of a possible source. For three different gas source positions, a total of 288 declaration experiments were carried out at different robot-to-source distances. Based on these readings, two machine learning techniques (ANN, SVM) were evaluated in terms of their classification performance. With learning parameters that were optimised by grid search, a maximal hit rate of approximately 87.5% could be obtained using a support vector machine }, ISBN = {0-7803-8232-3}, year = {2004} } @inproceedings{Lilienthal138298, author = {Lilienthal, Achim J. and Ulmer, Holger and Fr{\"o}hlich, Holger and Werner, Felix and Zell, Andreas}, booktitle = {2004 IEEE/RSJ international conference on intelligent robots and systems, 2004 (IROS 2004) : }, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, pages = {1444--1449}, title = {Learning to detect proximity to a gas source with a mobile robot}, volume = {4}, DOI = {10.1109/IROS.2004.1389599}, abstract = {As a sub-task of the general gas source localisation problem, gas source declaration is the process of determining the certainty that a source is in the immediate vicinity. Due to the turbulent character of gas transport in a natural indoor environment, it is not sufficient to search for instantaneous concentration maxima, in order to solve this task. Therefore, this paper introduces a method to classify whether an object is a gas source from a series of concentration measurements, recorded while the robot performs a rotation manoeuvre in front of a possible source. For three different gas source positions, a total of 1056 declaration experiments were carried out at different robot-to-source distances. Based on these readings, support vector machines (SVM) with optimised learning parameters were trained and the cross-validation classification performance was evaluated. The results demonstrate the feasibility of the approach to detect proximity to a gas source using only gas sensors. The paper presents also an analysis of the classification rate depending on the desired declaration accuracy, and a comparison with the classification rate that can be achieved by selecting an optimal threshold value regarding the mean sensor signal. }, ISBN = {0-7803-8463-6}, year = {2004} } @inproceedings{Lilienthal138319, author = {Lilienthal, Achim J. and Duckett, Tom}, booktitle = {ROSE 2003 - 1st IEEE International Workshop on Robotic Sensing 2003 : }, institution = {Örebro University, Department of Technology}, institution = {University of Tübingen, Tübingen, Germany}, eid = {1218709}, title = {A stereo electronic nose for a mobile inspection robot}, DOI = {10.1109/ROSE.2003.1218709}, abstract = {This paper describes the design of a gas-sensitive system that is suitable for use on a mobile robot ("mobile nose"). The stereo architecture comprises two equivalent sets of gas sensors mounted inside separated ventilated tubes (or "nostrils"). To characterise the dynamic response, the whole system is modelled as a first-order sensor. The corresponding parameters, including the response and recovery time, can be obtained by fitting this model to the values recorded during a simple experiment described in this paper. Our experiments confirmed the suitability of the applied model and permitted a quantitative comparison of different set-ups. It is shown that using suction fans lowers the recovery time of the metal oxide gas sensors by a factor of two, while a solid separation between the tubes (a "septum") is necessary to maintain the sensitivity of the mobile nose to concentration gradients. }, year = {2003} } @inproceedings{Lilienthal138320, author = {Lilienthal, Achim J. and Duckett, Tom}, booktitle = {ROSE 2003 - 1st IEEE International Workshop on Robotic Sensing 2003: Sensing and Perception in 21st Century Robotics : Sensing and Perception in 21st Century Robotics}, institution = {Örebro University, Department of Technology}, institution = {University of Tübingen, Tübingen, Germany}, eid = {1218705}, title = {An absolute positioning system for 100 euros}, DOI = {10.1109/ROSE.2003.1218705}, abstract = {This paper describes an absolute positioning system, which provides accurate and reliable measurements using low-cost equipment that is easy to set up. The system uses a number of fixed web-cameras to track a distinctly coloured object. In order to calculate the (x,y) position of this object, estimates calculated by triangulation from each combination of two cameras are combined, resulting in centimeter-level accuracy. Example applications, including tracking of mobile robots and persons, are described. An extended set-up is also introduced, which allows determination of the heading of a two coloured object from single images }, year = {2003} } @inproceedings{Cielniak138321, author = {Cielniak, Grzegorz and Miladinovic, Mihajlo and Hammarin, Daniel and G{\"o}ransson, Linus and Lilienthal, Achim J. and Duckett, Tom}, booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops : }, institution = {Örebro University, Department of Technology}, institution = {Dept. of Technology, AASS, Örebro University, Örebro, Sweden}, institution = {Dept. of Technology, AASS, Örebro University, Örebro, Sweden}, institution = {Dept. of Technology, AASS, Örebro University, Örebro, Sweden}, eid = {4624346}, title = {Appearance-based tracking of persons with an omnidirectional vision sensor}, volume = {7}, DOI = {10.1109/CVPRW.2003.10072}, abstract = {This paper addresses the problem of tracking a moving person with a single, omnidirectional camera. An appearance-based tracking system is described which uses a self-acquired appearance model and a Kalman filter to estimate the position of the person. Features corresponding to ``depth cues'' are first extracted from the panoramic images, then an artificial neural network is trained to estimate the distance of the person from the camera. The estimates are combined using a discrete Kalman filter to track the position of the person over time. The ground truth information required for training the neural network and the experimental analysis was obtained from another vision system, which uses multiple webcams and triangulation to calculate the true position of the person. Experimental results show that the tracking system is accurate and reliable, and that its performance can be further improved by learning multiple, person-specific appearance models }, ISBN = {0769519008}, year = {2003} } @inproceedings{Lilienthal138301, author = {Lilienthal, Achim J. and Duckett, Tom}, booktitle = {Autonome Mobile Systeme 2003 : }, institution = {Örebro University, Department of Technology}, institution = {WSI, University of Tübingen, Tübingen, Germany}, pages = {161--171}, title = {Approaches to gas source tracing and declaration by pure chemo-tropotaxis}, volume = {18}, DOI = {10.1007/978-3-642-18986-9_17}, abstract = {This paper addresses the problem of localising a static gas source in an uncontrolled indoor environment by a mobile robot. In contrast to previous works, especially the condition of an environment that is not artificially ventilated to produce a strong unidirectional airflow is considered. Here, the propagation of the analyte molecules is dominated by turbulence and convection flow rather than diffusion, thus creating a patchy distribution of spatially distributed eddies. Positive and negative tropotaxis, based on the spatial concentration gradient measured by a pair of electrochemical gas sensor arrays, were investigated. Both strategies were implemented utilising a direct sensor-motor coupling (a Braitenberg vehicle) and were shown to be useful to accomplish the gas source localisation task. As a possible solution to the problem of gas source declaration (the task of determining with certainty that the gas source has been found), an indirect localisation strategy based on exploration and concentration peak avoidance is suggested. Here, a gas source is located by exploiting the fact that local concentration maxima occur more frequently near the gas source compared to distant regions }, ISBN = {978-3-540-20142-7}, ISBN = {978-3-642-18986-9}, year = {2003} } @inproceedings{Lilienthal138302, author = {Lilienthal, Achim J. and Duckett, Tom}, booktitle = {Proceedings : 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003)}, institution = {Örebro University, Department of Technology}, institution = {University of Tübingen, Tübingen, Germany}, pages = {118--123}, title = {Creating gas concentration gridmaps with a mobile robot}, volume = {3}, DOI = {10.1109/IROS.2003.1250615}, abstract = {This paper addresses the problem of mapping the features of a gas distribution by creating concentration gridmaps from the data collected by a mobile robot equipped with an electronic nose. By contrast to metric gridmaps extracted from sonar or laser range scans, a single measurement of the electronic nose provides information about a comparatively small area. To overcome this problem, a mapping technique is introduced that uses a Gaussian density function to model the decreasing likelihood that a particular reading represents the true concentration with respect to the distance from the point of measurement. This method is evaluated in terms of its suitability regarding the slow response and recovery of the gas sensors. The stability of the mapped features and the capability to use concentration gridmaps to locate a gas source are also discussed. }, ISBN = {0-7803-7860-1}, year = {2003} } @inproceedings{Lilienthal138304, author = {Lilienthal, Achim J. and Duckett, Tom}, booktitle = {Proceedings of the 11th International Conference on Advanced Robotics 2003 : }, institution = {Örebro University, Department of Technology}, institution = {University of Tübingen, Tübingen, Germany}, note = {[Best Paper Award on ICAR 2003]}, pages = {375--380}, publisher = {Coimbra, University}, title = {Experimental analysis of smelling Braitenberg vehicles}, volume = {1-3}, abstract = {This paper addresses the problem of localisation of a static odour source in an unstructured indoor environment by a mobile robot using electrochemical gas sensors. In particular, reactive localisation strategies based on the instantaneously measured spatial concentration gradient are considered. In contrast to previous works, the environment is not artificially ventilated to produce a strong constant airflow, and thus the distribution of the odour molecules is dominated by turbulence. An experimental set-up is presented that enables different strategies for odour source localisation to be compared directly in a precisely measured experiment. Two alternative strategies that utilise a direct sensor-motor coupling are then investigated and a detailed numerical analysis of the results is presented, including tests of statistical significance. Both tested strategies proved to be useful to accomplish the localisation task. As a possible solution to the problem of detecting that the odour source - which is usually not corresponding to the global concentration maximum - was found, one of the tested strategies exploits the fact that local concentration maxima occur more frequently near the odour source compared to distant regions }, ISBN = {972-96889-8-2}, year = {2003} } @inproceedings{Wandel138311, author = {Wandel, Michael and Lilienthal, Achim J. and Duckett, Tom and Weimar, Udo and Zell, Andreas}, booktitle = {Proceedings of the IEEE international conference on advanced robotics 2003 : }, institution = {Örebro University, Department of Technology}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, pages = {507--512}, publisher = {University of Coimbra}, title = {Gas distribution in unventilated indoor environments inspected by a mobile robot}, volume = {1-3}, abstract = {Gas source localisation with robots is usually performed in environments with a strong, unidirectional airflow created by artificial ventilation. This tends to create a strong, well defined analyte plume and enables upwind searching. By contrast, this paper presents experiments conducted in unventilated rooms. Here, the measured concentrations also indicate an analyte plume with, however, different properties concerning its shape, width, concentration profile and stability over time. In the results presented in this paper, two very different mobile robotic systems for odour sensing were investigated in different environments, and the similarities as well as differences in the analyte gas distributions measured are discussed. }, ISBN = {972-96889-8-2}, year = {2003} } @inproceedings{Lilienthal138303, author = {Lilienthal, Achim J. and Duckett, Tom}, booktitle = {Proceedings of the European conference on mobile robots : ECMR 2003}, institution = {Örebro University, Department of Technology}, institution = {University of Tübingen, Tübingen, Germany}, pages = {159--164}, title = {Gas source localisation by constructing concentration gridmaps with a mobile robot}, abstract = {This paper addresses the problem of mapping the features of a gas distribution by creating concentration gridmaps with a mobile robot equipped with a gas-sensitive system ("mobile nose"). By contrast to metric gridmaps extracted from sonar or laser range scans, a gas sensor measurement provides information about a comparatively small area. To overcome this problem, a mapping technique is introduced that uses a Gaussian density function to model the decreasing likelihood that a particular reading represents the true concentration with respect to the distance from the point of measurement. The structure of the mapped features is discussed with respect to the parameters of the applied density function, the evolution of the gas distribution over time, and the capability to locate a gas source. }, year = {2003} } @inproceedings{Lilienthal138300, author = {Lilienthal, Achim J. and Reiman, Denis and Zell, Andreas}, booktitle = {Autonome mobile systeme 2003 : }, institution = {WSI, University of Tubingen, Tübingen, Germany}, institution = {WSI, University of Tubingen, Tübingen, Germany}, institution = {WSI, University of Tubingen, Tübingen, Germany}, pages = {150--160}, title = {Gas source tracing with a mobile robot using an adapted moth strategy}, volume = {18}, DOI = {10.1007/978-3-642-18986-9_16}, abstract = {As a sub-task of the general gas source localisation problem, gas source tracing is supposed to guide a gas-sensitive mobile system towards a source by using the cues determined from the gas distribution sensed along a driven path. This paper reports on an investigation of a biologically inspired gas source tracing strategy. Similar to the behaviour of the silkworm moth Bombyx mori, the implemented behaviour consists of a fixed motion pattern that realises a local search, and a mechanism that (re-)starts this motion pattern if an increased gas concentration is sensed. While the moth uses the local airflow direction to orient the motion pattern, this is not possible for a mobile robot due to the detection limits of currently available anemometers. Thus, an alternative method was implemented that uses an asymmetric motion pattern, which is biased towards the side where higher gas sensor readings were obtained. The adaptated strategy was implemented and tested on an experimental platform. This paper describes the strategy and evaluates its performance in terms of the ability to drive the robot towards a gas source and to keep it within close proximity of the source }, ISBN = {978-3-540-20142-7}, ISBN = {978-3-642-18986-9}, year = {2003} } @inproceedings{Lilienthal138312, author = {Lilienthal, Achim J. and Wandel, Michael R. and Weimar, Udo and Zell, Andreas}, booktitle = {Robotik 2002 : Leistungsstand - Anwendungen - Visionen - Trends}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, pages = {689--694}, publisher = {V D I-V D E - VERLAG GMBH}, title = {Detection and Localization of an Odour Source by an autonomous mobile Robot}, series = {VDI Berichte}, number = {1679}, volume = {1679}, abstract = {This paper presents studies concerning the use of an electronic nose on an autonomous mobile robot. In particular experiments were introduced in which a mobile robot generates two dimensional concentration maps of a known target gas in an unventilated room. It was shown that these concentration maps are clearly related to the position of the odour source. Moreover our results show that if accurate localization of the odour source itself is desired one has to consider weak air currents which usually occur even in closed unventilated rooms (often caused by convection). }, ISBN = {3-18-091679-6}, year = {2002} } @inproceedings{Wandel138322, author = {Wandel, Michael and Lilienthal, Achim J. and Zell, Andreas and Weimar, Udo}, booktitle = {Proceedings of the international symposium on olfaction and electronic nose : ISOEN 2002}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, pages = {128--129}, title = {Mobile robot using different senses}, year = {2002} } @inproceedings{Lilienthal138313, author = {Lilienthal, Achim J. and Zell, Andreas and Wandel, Michael R. and Weimar, Udo}, booktitle = {Proceedings of EUROBOT 2001, 4th European workshop on advanced mobile robots : }, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, pages = {1--8}, title = {Experiences using gas sensors on an autonomous mobile robot}, abstract = {This paper reports on experiences concerning the deployment of gas sensors on an autonomous mobile robot. It particularly addresses the suitability of the developed system to localize a distant odour source. First experiments were undertaken in which the robot was ordered to move along different weakly ventilated corridors, while keeping track of its center (framing a '1D' scenario). The measured sensor values show evident peaks that roughly indicate the location of the odour source, if the robot moves with a speed not too low. In this case the system proved to be well suited to detect even weak odour sources. Otherwise the observed course of the received values show many peaks hardly correlated with the location of the odour source. Several investigations were performed to clear up this behaviour but it is still not possible to make concluding statements about the reasons. Finally the setup to perform experiments in a '2D' scenario is described and concerning results of first investigations are presented. It was shown that the utilized system is also capable of detecting a distant odour source in a 2D environment and that the somewhat harder localization task has to account for some weak airflow even in closed, unventilated rooms. }, year = {2001} } @inproceedings{Wandel138314, author = {Wandel, Michael R. and Weimar, Udo and Lilienthal, Achim J. and Zell, Andreas}, booktitle = {The 8th IEEE international conference on electronics, circuits and systems : ICECS 2001}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, pages = {1247--1250}, eid = {957441}, title = {Leakage localisation with a mobile robot carrying chemical sensors}, series = {Proceedings of the IEEE International Conference on Electronics, Circuits, and Systems}, number = {3}, volume = {3}, DOI = {10.1109/ICECS.2001.957441}, abstract = {On the way to developing an electronic watchman one more sense, i.e. gas sensing facilities, are added to an autonomous mobile robot. For the gas detection, up to eight metal oxide sensors are operated using a commercial sensor system. The robot is able to move and navigate autonomously. The geometric information is extracted from laser range finder data. This input is used to build up an internal map while driving. Using the new sensor the localisation of a gas source in unventilated in-house environments is performed. First experiments in a one-dimensional case show a very good correlation between the peak and the gas source. The one-dimensional concentration profile is repeatedly recorded and stable for at least two hours. The two-dimensional experiments exhibit a circulation of the air within the room due to temperature and hence density effects. The latter is limiting the available recording time for the two-dimensional mapping }, ISBN = {0-7803-7057-0}, year = {2001} } @inproceedings{Lilienthal138315, author = {Lilienthal, Achim J. and Wandel, Michael and Weimar, Udo and Zell, Andreas}, booktitle = {Proceedings 2001 ICRA : IEEE international conference on robotics and automation}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, institution = {University of Tübingen, Tübingen, Germany}, pages = {4005--4010}, title = {Sensing odour sources in indoor environments without a constant airflow by a mobile robot}, volume = {1}, DOI = {10.1109/ROBOT.2001.933243}, abstract = {This paper describes the assembly of a mobile odour sensing system and investigates its practical operation in an indoor environment without a constant airflow. Lacking a constant airflow leads to a problem which cannot be neglected in real world applications. The response of the metal oxide gas sensors used is dominated by air turbulence rather than concentration differences. We show that this problem can be overcome by driving the robot with a constant speed, thus adding an extra constant airflow relative to the gas sensors location. If the robot's speed is not too low the system described proved to be well suited to detect even weak odour sources. Since driving with constant speed is an indispensable condition to perform the basic tasks of a mobile odour sensing system, a new localization strategy is proposed, which takes this into account. }, ISBN = {0-7803-6576-3}, year = {2001} } @inproceedings{Lilienthal138317, author = {Lilienthal, Achim J. and Wandel, Michael and Weimar, Udo and Zell, Andreas}, booktitle = {Autonome Mobile Systeme 2000 : }, institution = {The University of Tübingen, Tübingen, Germany}, institution = {The University of Tübingen, Tübingen, Germany}, institution = {The University of Tübingen, Tübingen, Germany}, institution = {The University of Tübingen, Tübingen, Germany}, pages = {201--209}, title = {Ein autonomer mobiler Roboter mit elektronischer Nase}, series = {Informatik Aktuell}, number = {16}, volume = {16}, ISBN = {3-540-41214-X}, year = {2000} }