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arXiv · 2503.01438

CAO-RONet: A Robust 4D Radar Odometry with Exploring More Information from Low-Quality Points

Zhiheng Li·Yubo Cui·Ningyuan Huang·Chenglin Pang·Zheng Fang
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Abstract

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.

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Zhiheng Li, Yubo Cui, Ningyuan Huang, Chenglin Pang, Zheng Fang. 2025-03-03. CAO-RONet: A Robust 4D Radar Odometry with Exploring More Information from Low-Quality Points. https://arxiv.org/abs/2503.01438

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