Semiautomated Extraction of Street Light Poles From Mobile LiDAR Point-Clouds

Yongtao Yu, Jonathan Li, Haiyan Guan, Cheng Wang, Jun Yu
2015 IEEE Transactions on Geoscience and Remote Sensing  
This paper proposes a novel algorithm for extracting street light poles from vehicleborne mobile light detection and ranging (LiDAR) point-clouds. First, the algorithm rapidly detects curb-lines and segments a point-cloud into road and nonroad surface points based on trajectory data recorded by the integrated position and orientation system onboard the vehicle. Second, the algorithm accurately extracts street light poles from the segmented nonroad surface points using a novel pairwise 3-D shape
more » ... pairwise 3-D shape context. The proposed algorithm is tested on a set of point-clouds acquired by a RIEGL VMX-450 mobile LiDAR system. The results show that road surfaces are correctly segmented, and street light poles are robustly extracted with a completeness exceeding 99%, a correctness exceeding 97%, and a quality exceeding 96%, thereby demonstrating the efficiency and feasibility of the proposed algorithm to segment road surfaces and extract street light poles from huge volumes of mobile LiDAR point-clouds. Index Terms-Light pole extraction, mobile light detection and ranging (LiDAR), point-cloud, road surface segmentation, shape context.
doi:10.1109/tgrs.2014.2338915 fatcat:j7f3iz7onncr7f5udc5qbkyun4