Equi-RO: A 4D mmWave Radar Odometry via Equivariant Networks
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914282461986816 |
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| author | Han, Zeyu Yang, Shuocheng Zhu, Minghan Zhang, Fang Xu, Shaobing Ghaffari, Maani Wang, Jianqiang |
| author_facet | Han, Zeyu Yang, Shuocheng Zhu, Minghan Zhang, Fang Xu, Shaobing Ghaffari, Maani Wang, Jianqiang |
| contents | Autonomous vehicles and robots rely on accurate odometry estimation in GPS-denied environments. While LiDARs and cameras struggle under extreme weather, 4D mmWave radar emerges as a robust alternative with all-weather operability and velocity measurement. In this paper, we introduce Equi-RO, an equivariant network-based framework for 4D radar odometry. Our algorithm pre-processes Doppler velocity into invariant node and edge features in the graph, and employs separate networks for equivariant and invariant feature processing. A graph-based architecture enhances feature aggregation in sparse radar data, improving inter-frame correspondence. Experiments on an open-source dataset and a self-collected dataset show Equi-RO outperforms state-of-the-art algorithms in accuracy and robustness. Overall, our method achieves 10.7% and 13.4% relative improvements in translation and rotation accuracy, respectively, compared to the best baseline on the open-source dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_20674 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Equi-RO: A 4D mmWave Radar Odometry via Equivariant Networks Han, Zeyu Yang, Shuocheng Zhu, Minghan Zhang, Fang Xu, Shaobing Ghaffari, Maani Wang, Jianqiang Robotics Computer Vision and Pattern Recognition Autonomous vehicles and robots rely on accurate odometry estimation in GPS-denied environments. While LiDARs and cameras struggle under extreme weather, 4D mmWave radar emerges as a robust alternative with all-weather operability and velocity measurement. In this paper, we introduce Equi-RO, an equivariant network-based framework for 4D radar odometry. Our algorithm pre-processes Doppler velocity into invariant node and edge features in the graph, and employs separate networks for equivariant and invariant feature processing. A graph-based architecture enhances feature aggregation in sparse radar data, improving inter-frame correspondence. Experiments on an open-source dataset and a self-collected dataset show Equi-RO outperforms state-of-the-art algorithms in accuracy and robustness. Overall, our method achieves 10.7% and 13.4% relative improvements in translation and rotation accuracy, respectively, compared to the best baseline on the open-source dataset. |
| title | Equi-RO: A 4D mmWave Radar Odometry via Equivariant Networks |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.20674 |