High-Precision and Fast LiDAR Odometry and Mapping Algorithm

Abstract

LiDAR SLAM technology is an important method for the accurate navigation of automatic vehicles and is a prerequisite for the safe driving of automatic vehicles in the unstructured road environment of complex parks. This paper proposes a LiDAR fast point cloud registration algorithm that can realize fast and accurate localization and mapping of automatic vehicle point clouds through a combination of a normal distribution transform (NDT) and point-to-line iterative closest point (PLICP). First, the NDT point cloud registration algorithm is applied for the rough registration of point clouds between adjacent frames to achieve a rough estimate of the pose of automatic vehicles. Then, the PLICP point cloud registration algorithm is adopted to correct the rough registration result of the point cloud. This step completes the precise registration of the point cloud and achieves an accurate estimate of the pose of the automatic vehicle. Finally, cloud registration is accumulated over time, and the point cloud information is continuously updated to construct the point cloud map. Through numerous experiments, we compared the proposed algorithm with PLICP. The average number of iterations of the point cloud registration between adjacent frames was reduced by 6.046. The average running time of the point cloud registration between adjacent frames decreased by 43.05156 ms. The efficiency of the point cloud registration calculation increased by approximately 51.7%. By applying the KITTI dataset, the computational efficiency of NDT-ICP was approximately 60% higher than that of LeGO-LOAM. The proposed method realizes the accurate localization and mapping of automatic vehicles relying on vehicle LiDAR in a complex park environment and was applied to a Small Cyclone automatic vehicle. The results indicate that the proposed algorithm is reliable and effective.