A Fast and Accurate Segmentation Method for Ordered LiDAR Point Cloud of Large-Scale Scenes

This letter proposes an efficient two-step segmentation method for large-scale 3-D point cloud data collected by the mobile laser scanners. First, a new scan-line-based ground segmentation algorithm is designed to filter the points corresponding to the ground with high accuracy. Second, we propose a self-adaptive Euclidean clustering algorithm to further separate the off-ground points corresponding to different objects. Experiments show that our method delivers superior segmentation results on scanned data. In fact, the proposed method can be used in complex scenes including slope and bumpy road at an error rate of 0.674% and a computing throughput of over 20 million points/s.