CAO-RONet：一种探索低质量点中更多信息的鲁棒4D雷达里程计
CAO-RONet: A Robust 4D Radar Odometry with Exploring More Information from Low-Quality Points
AI总结：
针对4D雷达低质量点影响里程计任务的问题，提出CAO-RONet框架，通过局部补全、上下文感知关联和基于窗口的优化器实现鲁棒自我运动估计，性能提升约50%且精度媲美LiDAR里程计。
摘要：
近期，4D毫米波雷达在恶劣条件（如雨雾）下展现出比LiDAR和相机更稳定的感知能力。然而，低质量的雷达点阻碍了其应用，尤其是在需要密集且精确匹配的里程计任务中。为了充分挖掘4D雷达的潜力，我们引入了一种基于学习的里程计框架，能够从有限且不确定的几何信息中实现鲁棒的自我运动估计。首先，针对稀疏的雷达点，我们提出了局部补全以补充缺失结构，并为对齐两帧提供更密集的指导。然后，具有分层结构的上下文感知关联在特征相似性的辅助下灵活匹配不同尺度的点，并通过相关性平衡改善局部匹配一致性。最后，我们提出了一种基于窗口的优化器，利用历史先验建立耦合状态估计并纠正帧间匹配误差。我们在View-of-Delft数据集上验证了算法的优越性，性能较先前方法提升了约50%，并提供了与LiDAR里程计相当的精度。我们的代码将会开源。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
