单雷达实现高精度定位，恶劣天气下仍稳定可靠。
CFEAR-Teach-and-Repeat: Fast and Accurate Radar-only Localization
- 融合历史扫描与实时关键帧，联合对齐提升定位精度。
- 在Boreas数据集上达0.117米、0.096度误差，比之前最优提升63%。
- 适合需要轻量级、抗恶劣天气的自动驾驶系统部署。
在恶劣天气下，基于先验地图的可靠定位对自主导航至关重要，此时光学传感器可能失效。本文提出CFEAR-TR，一种仅使用单个旋转雷达的教-重复定位流程，旨在实现易于部署、轻量化且鲁棒的导航。该方法通过将实时扫描同时对齐至教学阶段存储的扫描和最近的实时关键帧滑动窗口，确保在不同季节和天气条件下均能实现精确稳定的位姿估计。雷达扫描采用多普勒补偿测量计算出的稀疏有向表面点表示，地图以位姿图形式存储，并在定位时遍历。在Boreas数据集预留测试序列上的实验表明，CFEAR-TR可实现最低0.117米、0.096°的定位误差，相比此前最先进方法最高提升63%，且运行效率达29 Hz。该结果显著缩小了与激光雷达级定位的差距，尤其在航向估计方面。我们已向社区开源该项目的C++实现。
原文摘要 · Abstract (English)
Reliable localization in prior maps is essential for autonomous navigation, particularly under adverse weather, where optical sensors may fail. We present CFEAR-TR, a teach-and-repeat localization pipeline using a single spinning radar, which is designed for easily deployable, lightweight, and robust navigation in adverse conditions. Our method localizes by jointly aligning live scans to both stored scans from the teach mapping pass, and to a sliding window of recent live keyframes. This ensures accurate and robust pose estimation across different seasons and weather phenomena. Radar scans are represented using a sparse set of oriented surface points, computed from Doppler-compensated measurements. The map is stored in a pose graph that is traversed during localization. Experiments on the held-out test sequences from the Boreas dataset show that CFEAR-TR can localize with an accuracy as low as 0.117 m and 0.096°, corresponding to improvements of up to 63% over the previous state of the art, while running efficiently at 29 Hz. These results substantially narrow the gap to lidar-level localization, particularly in heading estimation. We make the C++ implementation of our work available to the community.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。