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

• URL: http://arxiv.org/abs/2509.20674v1
• Date: Thu, 25 Sep 2025 02:21:05 GMT
• Title: Equi-RO: A 4D mmWave Radar Odometry via Equivariant Networks
• Abstract summary: We introduce Equi-RO, an equivariant network-based framework for 4D radar odometry.<n>A graph-based architecture enhances feature aggregation in sparse radar data.<n> Experiments on the open-source dataset and self-collected dataset show Equi-RO outperforms state-of-the-art algorithms in accuracy and robustness.
• Score: 19.594498796044835
• License: http://creativecommons.org/licenses/by/4.0/
• 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 the open-source dataset and self-collected dataset show Equi-RO outperforms state-of-the-art algorithms in accuracy and robustness. Overall, our method achieves 10.7% and 20.0% relative improvements in translation and rotation accuracy, respectively, compared to the best baseline on the open-source dataset.

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