Multi-Session Mapping and Long-Term Localization for Autonomous Vehicles Using Radar

Localization of autonomous vehicles in existing maps is crucial for reliable navigation. Using previously constructed maps allows vehicles to estimate their pose without the inherent odometry drift. Building such maps involves aligning data recorded at different times and maintaining the map over time. While {LiDAR} sensors are commonly used for mapping due to their high accuracy, they are sensitive to adverse weather and involve high production costs. In this letter, we address the problem of long-term mapping and localization leveraging automotive radars, which are robust to weather conditions and offer a cost-effective alternative to {LiDARs}. In our approach, we construct maps of coinciding areas and align them by performing place recognition between them. Additionally, our multi-sequence loop detection and verification strategy for radar sensors is able to filter incorrect loop matches, enhancing trajectory alignment. Then, our novel map maintenance module handles radar noise and preserves persistent map points that remain reliable for localization. Subsequently, we estimate the robot poses in the resulting map by combining local odometry with scan-to-map matching, overcoming the complexities of sparse automotive radar data. We evaluate our method on public automotive radar datasets. The results show that our approach achieves state-of-the-art trajectory alignment, preserves persistent map points for localization, and reliably localizes within the constructed maps.