Are We Ready for Radar to Replace Lidar in All-Weather Mapping and Localization?
| Autor: | Keenan Burnett, Yuchen Wu, David J. Yoon, Angela P. Schoellig, Timothy D. Barfoot |
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| Rok vydání: | 2022 |
| Předmět: |
Human-Computer Interaction
FOS: Computer and information sciences Computer Science - Robotics Control and Optimization Artificial Intelligence Control and Systems Engineering Mechanical Engineering Biomedical Engineering Computer Vision and Pattern Recognition Robotics (cs.RO) Computer Science Applications |
| DOI: | 10.48550/arxiv.2203.10174 |
| Popis: | We present an extensive comparison between three topometric localization systems: radar-only, lidar-only, and a cross-modal radar-to-lidar system across varying seasonal and weather conditions using the Boreas dataset. Contrary to our expectations, our experiments showed that our lidar-only pipeline achieved the best localization accuracy even during a snowstorm. Our results seem to suggest that the sensitivity of lidar localization to moderate precipitation has been exaggerated in prior works. However, our radar-only pipeline was able to achieve competitive accuracy with a much smaller map. Furthermore, radar localization and radar sensors still have room to improve and may yet prove valuable in extreme weather or as a redundant backup system. Code for this project can be found at: https://github.com/utiasASRL/vtr3 Comment: Version 3: Accepted to RA-L, presented at IROS 2022. Localization results updated due to improved ground truth and calibration. Also switched Huber Loss for Cauchy Loss for the radar-based approaches |
| Databáze: | OpenAIRE |
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