Multi-level Surfel Map Construction for Accurate Localization in Intelligent Vehicles

Abstract The role of mapping in achieving accurate localization in intelligent vehicles is crucial. Accurate maps enable intelligent vehicles to attain accuracy in localization and path planning, ultimately enhancing the safety and overall driving experience. However, the interference of dynamic objects poses a challenge to map updates and subsequent localization. To address this issue, this study proposes a novel algorithm for constructing multi-level semantic surfel maps, along with a corresponding hierarchical localization method. The proposed method divides the map into three levels: semantic level, surfel level, and trajectory level. During the map construction phase, we utilize semantic segmentation technology to accurately distinguish between static and dynamic environments, filter out dynamic point clouds, and focus solely on modeling static scenes. This effectively eliminates the interference of dynamic objects on map updates, ensuring the construction of a precise and reliable static environment model. In the subsequent hierarchical localization phase, a node-level localization method is introduced, which achieves coarse localization by matching perception information with the map.The results of experiments conducted in various scenarios indicate an average absolute trajectory error of 4cm between the transformation matrices of each node in the map. Our algorithm achieves a 92% success rate in node-level localization and an average pose-level localization error of 8.2cm, demonstrating the feasibility and robustness of the proposed algorithm.