A Data Association Method Based on Pose Refinement and Mismatch Rejection for HD map Localization

Robust and high-precision real-time localization is one of the fundamental requirements for autonomous vehicles. Traditional localization methods based on sensor fusion, vision and LiDAR, suffer from drawbacks like odometry drift and overly dense point cloud maps, which limit their widespread application in autonomous driving tasks. However, in current localization methods depending on HD map, data association faces challenges. These challenges include issues of statistical consistency between features and HD map, as well as mismatches between individual entities of them, which result from large searching pose errors and the occurrence of repeated landmarks. This paper proposes a robust data association method within a semantic localization framework based on HD map. Firstly, the paper refines the coarse pose of the vehicle using the Cruising Model, according to the localization results from the previous two frames, and addresses the statistical consistency issue between semantic features and HD map. Additionally, a RANSAC-based mismatch elimination method is introduced to resolve feature mismatches. Finally, the proposed algorithm is validated on the Waymo dataset. Experimental results demonstrate a matching accuracy of 98.36%, with Euler angle and position errors both below 0.02 degrees and 2 cm, respectively.