Robust and High-Precision Point Cloud Registration Method Based on 3D-NDT Algorithm for Vehicle Localization

Point cloud registration plays a pivotal role in LiDAR-based vehicle localization, as its robustness and accuracy directly influence the map generation quality and localization precision. The 3D Normal Distributions Transform (3D-NDT) is a competitive registration algorithm that excels in handling noise and dynamic changes in complex environments. However, its performance is hindered by the blurring of local point cloud features caused by voxelization. To address this limitation, this paper proposes an improved 3D-NDT registration method that leverages point cloud normal vector segmentation and voxelization techniques to optimize the registration process, improving both accuracy and matching scope. Initially, a plane point cloud clustering technique is employed for preliminary voxel partitioning. This is followed by plane point cloud subdivision, which further refines the voxels to ensure a uniform voxel cell size, thereby appropriately weighting each voxel cell's contribution to the objective function. The proposed method employs a dual-stage voxel division strategy, utilizing two distinct voxel sizes tailored to different optimization phases. In the first stage, a larger grid size broadens the matching scope and enhances the efficiency of preliminary registration. The resulting output serves as the starting point for the refined registration phase, where a smaller grid size is used to improve matching precision. Experimental results demonstrate that the proposed method achieves a 50% reduction in median translation error (from 1.0 × 10^-3 m to 0.5 × 10^-3 m) and a 50% reduction in median rotation error (from 0.01° to 0.005°) compared to the original 3D-NDT algorithm. Furthermore, it significantly improves matching robustness and accuracy by extending the effective matching range by 100% for translation (from 1.2 m to 2.4 m) and 25.6% for rotation (from 39° to 49°).