Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.01438 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909521527439360 |
|---|---|
| author | Li, Zhiheng Cui, Yubo Huang, Ningyuan Pang, Chenglin Fang, Zheng |
| author_facet | Li, Zhiheng Cui, Yubo Huang, Ningyuan Pang, Chenglin Fang, Zheng |
| contents | Recently, 4D millimetre-wave radar exhibits more stable perception ability than LiDAR and camera under adverse conditions (e.g. rain and fog). However, low-quality radar points hinder its application, especially the odometry task that requires a dense and accurate matching. To fully explore the potential of 4D radar, we introduce a learning-based odometry framework, enabling robust ego-motion estimation from finite and uncertain geometry information. First, for sparse radar points, we propose a local completion to supplement missing structures and provide denser guideline for aligning two frames. Then, a context-aware association with a hierarchical structure flexibly matches points of different scales aided by feature similarity, and improves local matching consistency through correlation balancing. Finally, we present a window-based optimizer that uses historical priors to establish a coupling state estimation and correct errors of inter-frame matching. The superiority of our algorithm is confirmed on View-of-Delft dataset, achieving around a 50% performance improvement over previous approaches and delivering accuracy on par with LiDAR odometry. Our code will be available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01438 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CAO-RONet: A Robust 4D Radar Odometry with Exploring More Information from Low-Quality Points Li, Zhiheng Cui, Yubo Huang, Ningyuan Pang, Chenglin Fang, Zheng Robotics Recently, 4D millimetre-wave radar exhibits more stable perception ability than LiDAR and camera under adverse conditions (e.g. rain and fog). However, low-quality radar points hinder its application, especially the odometry task that requires a dense and accurate matching. To fully explore the potential of 4D radar, we introduce a learning-based odometry framework, enabling robust ego-motion estimation from finite and uncertain geometry information. First, for sparse radar points, we propose a local completion to supplement missing structures and provide denser guideline for aligning two frames. Then, a context-aware association with a hierarchical structure flexibly matches points of different scales aided by feature similarity, and improves local matching consistency through correlation balancing. Finally, we present a window-based optimizer that uses historical priors to establish a coupling state estimation and correct errors of inter-frame matching. The superiority of our algorithm is confirmed on View-of-Delft dataset, achieving around a 50% performance improvement over previous approaches and delivering accuracy on par with LiDAR odometry. Our code will be available. |
| title | CAO-RONet: A Robust 4D Radar Odometry with Exploring More Information from Low-Quality Points |
| topic | Robotics |
| url | https://arxiv.org/abs/2503.01438 |