Robust Localization in Map Changing Environments Based on Hierarchical Approach of Sliding Window Optimization and Filtering

Precise localization in map-changing environments is a major challenge faced in the case of autonomous vehicles. In such environments, map-matching-based localization can result in an incorrect vehicle pose because the High-Definition map (HD map) and the sensor measurements from the real environments are different. In order to solve this problem, this paper proposes robust localization in map-changing environments based on a hierarchical approach of sliding window optimization and filtering. The changing environments are explicitly modeled by a sub-map from the sliding-window-based graph optimization, and the generated sub-map is used for the map matching of the real-time filter. Since the optimized sub-map includes sensor measurement from both the changed and unchanged regions, it reduces the proportion of the changed region in the total matching region, thereby increasing the robustness of the map matching. The proposed hierarchical localization algorithm is verified and evaluated via simulation and experiments. The results show that the proposed algorithm provides a robust vehicle pose in map-changing environments.