A Skeleton-Based Hierarchical Method for Detecting 3-D Pole-Like Objects From Mobile LiDAR Point Clouds

• Published in: IEEE geoscience and remote sensing letters 2019-05, Vol.16 (5), p.801-805
• Main Authors: Yang, Juntao, Kang, Zhizhong, Akwensi, Perpetual Hope
• Format: Article
• Language: English
• Subjects: Algorithms; Autonomous navigation; Buildings; Clustering; Detection; Facades; Laplace equations; Laplacian smoothing; Lidar; Methods; Navigation; Object recognition; Occlusion; pole-like object extraction; Principal component analysis; principal component analysis (PCA); Principal components analysis; Roads; Shape; Shape recognition; Smoothing methods; Three dimensional models; Three-dimensional displays; Voronoi tessellation
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2018.2882694

The pole-like object detection is of significance for robot navigation, autonomous driving, road infrastructure inventory, and detailed 3-D map generation. In this letter, we develop a skeleton-based hierarchical method for automatic detection of pole-like objects from mobile LiDAR point clouds. First, coarse extraction of building facades is adopted for the occlusion analysis. Second, slice-based Euclidean clustering algorithm is implemented to derive a set of pole-like object candidates. Third, skeleton-based principal component analysis shape recognition is presented to robustly locate all possible positions of pole-like objects. Finally, a Voronoi-constrained vertical region growing algorithm is proposed to adaptively producing the individual pole-like objects. Experiments were conducted on the public Paris-Lille-3-D data set. Experimental results demonstrate that the proposed method is robust and efficient for extracting the pole-like objects, with average quality of 90.43%. Furthermore, the proposed method outperforms other existing methods, especially for detecting pole-like objects with a large radius.