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.

翻译：在先验地图中进行可靠定位对于自主导航至关重要，尤其在光学传感器可能失效的恶劣天气条件下。我们提出了CFEAR-TR，一种使用单个旋转雷达的示教-回放定位流程，旨在恶劣条件下实现易于部署、轻量且鲁棒的导航。我们的方法通过将实时扫描数据同时与示教建图过程中存储的扫描数据以及近期实时关键帧滑动窗口进行联合配准来实现定位。这确保了在不同季节和天气现象下都能获得准确且鲁棒的位姿估计。雷达扫描数据使用一组稀疏的定向表面点来表示，这些点由经过多普勒补偿的测量值计算得出。地图存储在位姿图中，并在定位过程中进行遍历。在Boreas数据集的保留测试序列上的实验表明，CFEAR-TR能够以低至0.117米和0.096°的精度进行定位，相对于先前的最先进方法，其精度提升高达63%，同时以29 Hz的频率高效运行。这些结果显著缩小了与激光雷达级定位的差距，特别是在航向估计方面。我们将本工作的C++实现开源给研究社区。