Ground segmentation based point cloud feature extraction for 3D LiDAR SLAM enhancement

Point cloud preprocessing lays the foundation for the realization of autonomous vehicles (AVs) as it is the backbone of 3D LiDAR simultaneous localization and mapping (SLAM). Matching feature points selection based on multiple classifiers from preprocessing techniques may significantly increase the chances of the good matching result, and thus reduces drift error accumulation. In this paper, a series of point cloud preprocessing and feature extraction methods were proposed, where LiDAR sensor is used only. Experiments indicate that our ground point segmentation algorithm is efficient, comparable to state-of-the-art methods, and even outperforms the general approaches when measured with certain metrics. Improvement in extracting edge features with vertical clustering can ensure stability and geometrical characteristics of features. With the implementation of the proposed point cloud preprocessing techniques on well-known pose estimation framework such as LeGOLOAM, higher accuracy with the reduction in both rotation and translation error in most dataset sequences is achieved. Finally, the proposed algorithm is examined and evaluated via KITTI, Semantic-KITTI, and our own VLP-16 campus datasets.