CFEAR-Teach-and-Repeat：快速且准确的仅雷达定位
CFEAR-Teach-and-Repeat: Fast and Accurate Radar-only Localization
- Chair of Perception for Intelligent Systems, Munich Institute of Robotics and Machine Intelligence, Technical University of Munich（感知智能系统教授团，慕尼黑机器人与机器智能研究所，慕尼黑技术大学）
- Robot Navigation and Perception Lab of the AASS Research Center, Örebro University（AASS研究中心机器人导航与感知实验室，奥雷布罗大学）
- Bosch Rexroth, Germany（德国博世· Rexroth公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
CFEAR-TR利用单个雷达实现快速且准确的仅雷达定位，精度达0.117米和0.096度，比先前方法提升63%，在恶劣天气下表现优异。
AI中文摘要：
在先前的地图中实现可靠的定位对于自主导航至关重要，尤其是在恶劣天气条件下，光学传感器可能失效。我们提出了CFEAR-TR，一种使用单个旋转雷达的教与重复定位流水线，旨在实现易于部署、轻量且在恶劣条件下稳健的导航。我们的方法通过将实时扫描同时对齐存储的教映射过程中的扫描和滑动窗口中的最近实时关键帧，来实现定位。这确保了在不同季节和天气现象下都能实现准确且稳健的位姿估计。雷达扫描使用一组稀疏的定向表面点来表示，这些点是从补偿多普勒效应的测量中计算得出的。地图存储在一个位姿图中，并在定位过程中进行遍历。在Boreas数据集的 held-out 测试序列上的实验表明，CFEAR-TR能够以最低的精度0.117米和0.096度进行定位，相比先前的最先进方法提高了多达63%，同时以29Hz的高效速度运行。这些结果大大缩小了与激光雷达级别的定位差距，特别是在航向估计方面。我们向社区提供了我们的C++实现。
英文摘要：
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.
