Daniel Adolfsson
I’m a Ph.D student in the MRO lab at Örebro University since mars 2017. I’m in the field of perception and my research topic is semantic mapping on multiple scales using Laser ranging.
2016 I graduated from Malardalens University where I studied my masters in robotics and electronics.
I’m currently active in the research project ILIAD (Intra-Logistics with Integrated Automatic Deployment: safe and scalable fleets in shared spaces). In the private sector I’m working with FUMO.
For more info visit my Linkedin:
Contact
Dr. Daniel Adolfsson
AASS Research Centre
School of Science and Technology
Örebro University
70182 Örebro, Sweden
Room T1206
Phone +46 (0)19 30 37 11
daniel.adolfsson@oru.se
Publications
Journal Articles
[1] | BFAR : improving radar odometry estimation using a bounded false alarm rate detector. Autonomous Robots, 48(8), 2024 [ BibTeX | DiVA ] |
[2] | Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large-Scale Radar Odometry in Diverse Environments. IEEE Transactions on robotics, 39(2):1476-1495, 2023 [ BibTeX | DiVA | PDF ] |
[3] | TBV Radar SLAM - Trust but Verify Loop Candidates. IEEE Robotics and Automation Letters, 8(6):3613-3620, 2023 [ BibTeX | DiVA ] |
[4] | The ILIAD Safety Stack : Human-Aware Infrastructure-Free Navigation of Industrial Mobile Robots. IEEE robotics & automation magazine 2023 [ BibTeX | DiVA ] |
[5] | CorAl : Introspection for robust radar and lidar perception in diverse environments using differential entropy. Robotics and Autonomous Systems, 155, 2022 [ BibTeX | DiVA ] |
Refereed Conference and Workshop Articles
[1] | Towards introspective loop closure in 4D radar SLAM. 2024 [ BibTeX | DiVA ] |
[2] | BFAR – Bounded False Alarm Rate detector for improved radar odometry estimation. 2021 [ BibTeX | DiVA ] |
[3] | CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), pages 5462-5469, 2021 [ BibTeX | DiVA | PDF ] |
[4] | CorAl – Are the point clouds Correctly Aligned?. In 10th European Conference on Mobile Robots (ECMR 2021), 10, 2021 [ BibTeX | DiVA | PDF ] |
[5] | NDT-Transformer : Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation. In 2021 IEEE International Conference on Robotics and Automation (ICRA) 2021 [ BibTeX | DiVA ] |
[6] | Oriented surface points for efficient and accurate radar odometry. 2021 [ BibTeX | DiVA ] |
[7] | Localising Faster : Efficient and precise lidar-based robot localisation in large-scale environments. In 2020 IEEE International Conference on Robotics and Automation (ICRA), pages 4386-4392, 2020 [ BibTeX | DiVA ] |
[8] | A Submap per Perspective : Selecting Subsets for SuPer Mapping that Afford Superior Localization Quality. In 2019 European Conference on Mobile Robots (ECMR) 2019 [ BibTeX | DiVA | PDF ] |
[9] | Improving Localisation Accuracy using Submaps in warehouses. 2018 [ BibTeX | DiVA | PDF ] |
[10] | Incorporating Ego-motion Uncertainty Estimates in Range Data Registration. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1389-1395, 2017 [ BibTeX | DiVA ] |
Theses
[1] | Robust large-scale mapping and localization : Combining robust sensing and introspection. Örebro University, School of Science and Technology, Ph.D. Thesis, 2023 [ BibTeX | DiVA | PDF ] |
[2] | Integrated Localization and Directed Communication for Acoustic Underwater Systems. Mälardalen University, School of Innovation, Design and Engineering, M.Sc. Thesis, 2016 [ BibTeX | PDF ] |
Find the complete BibTeX record here.