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

作者：Zhou Ying^*; Wang Dan; Xie Xiang; Ren Yiyi; Li Guolin; Deng Yangdong; Wang Zhihua

来源：IEEE Geoscience and Remote Sensing Letters, 2014, 11(11): 1981-1985.

DOI：10.1109/LGRS.2014.2316009

• Clustering
• point cloud
• scan line
• segmentation
• slopes

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.

• 出版日期2014-11
• 单位清华大学