Name
Title | ||
|---|---|---|
CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry |
Abstract | ||
|---|---|---|
This paper presents an accurate, highly efficient and learning free method for large-scale radar odometry estimation. By using a simple filtering technique that keeps the strongest returns, we produce a clean radar data representation and reconstruct surface normals 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. Drift is additionally reduced by jointly registering the latest scan to a history of keyframes. We found that our odometry pipeline generalize well 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 merely on a single laptop CPU thread at 55 Hz. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IROS51168.2021.9636253 | 2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
DocType | ISSN | Citations |
Conference | 2153-0858 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Adolfsson | 1 | 0 | 0.34 |
| Martin Magnusson | 2 | 71 | 10.10 |
| Anas Alhashimi | 3 | 0 | 0.34 |
| Achim J. Lilienthal | 4 | 1468 | 113.18 |
| Henrik Andreasson | 5 | 544 | 38.96 |