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arXiv · 2509.20674

Equi-RO: A 4D mmWave Radar Odometry via Equivariant Networks

Zeyu Han·Shuocheng Yang·Minghan Zhu·Fang Zhang·Shaobing Xu·Maani Ghaffari·Jianqiang Wang
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Abstract

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.

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Zeyu Han, Shuocheng Yang, Minghan Zhu, Fang Zhang, Shaobing Xu, Maani Ghaffari, Jianqiang Wang. 2025-09-25. Equi-RO: A 4D mmWave Radar Odometry via Equivariant Networks. https://arxiv.org/abs/2509.20674

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