CFEAR-Teach-and-Repeat: Fast and Accurate Radar-only Localization
This work provides a more accurate and robust radar-only localization solution for autonomous vehicles, particularly beneficial for navigation in challenging weather where optical sensors are unreliable.
This paper addresses the problem of radar-only localization for autonomous navigation in adverse weather conditions. The proposed CFEAR-TR pipeline achieves a localization accuracy of 0.117 m and 0.096°, representing an improvement of up to 63% over the previous state of the art, while operating at 29 Hz.
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.