Radar-only ego-motion estimation in difficult settings via graph matching
Radar detects stable, long-range objects under variable weather and lighting conditions, making it a reliable and versatile sensor well suited for ego-motion estimation. In this work, we propose a radar-only odometry pipeline that is highly robust to radar artifacts (e.g., speckle noise and false po...
| Published in: | 2019 International Conference on Robotics and Automation (ICRA) |
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| Main Authors: | , |
| Format: | Conference Object |
| Language: | unknown |
| Published: |
IEEE
2019
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| Subjects: | |
| Online Access: | https://doi.org/10.1109/ICRA.2019.8793990 https://ora.ox.ac.uk/objects/uuid:216cf226-3b70-486e-92b8-a1bb65e51299 |
| Summary: | Radar detects stable, long-range objects under variable weather and lighting conditions, making it a reliable and versatile sensor well suited for ego-motion estimation. In this work, we propose a radar-only odometry pipeline that is highly robust to radar artifacts (e.g., speckle noise and false positives) and requires only one input parameter. We demonstrate its ability to adapt across diverse settings, from urban UK to off-road Iceland, achieving a scan matching accuracy of approximately 5.20 cm and 0.0929 deg when using GPS as ground truth (compared to visual odometry’s 5.77 cm and 0.1032 deg). We present algorithms for key point extraction and data association, framing the latter as a graph matching optimization problem, and provide an in-depth system analysis. |
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