提升低质量雷达点的定位精度，实现雨雾中的稳定4D雷达里程计。
CAO-RONet: A Robust 4D Radar Odometry with Exploring More Information from Low-Quality Points
- 通过局部补全增强稀疏点云，提供更密集的匹配引导。
- 基于上下文关联与分层匹配，提升多尺度点云对齐一致性。
- 利用历史先验进行窗口优化，显著降低帧间匹配误差。
近期，4D毫米波雷达在雨雾等恶劣环境下表现出比激光雷达和摄像头更稳定的感知能力。然而，低质量雷达点限制了其应用，尤其在需要稠密精确匹配的里程计任务中。为充分挖掘4D雷达潜力，本文提出一种基于学习的里程计框架，实现从有限且不确定几何信息中鲁棒估计自身运动。针对稀疏雷达点，提出局部补全方法以补充缺失结构，并为帧间对齐提供更稠密指导；设计上下文感知的分层关联机制，借助特征相似性灵活匹配不同尺度点，并通过相关性平衡提升局部匹配一致性；最后引入基于窗口的优化器，利用历史先验建立耦合状态估计，纠正帧间匹配误差。在View-of-Delft数据集上验证，性能相较先前方法提升约50%，精度达到与激光雷达里程计相当水平。代码将公开。
原文摘要 · Abstract (English)
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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