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

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. Fir...

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Bibliographic Details
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
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Summary: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.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2018.2882694