Voxel-Based Extraction and Classification of 3-D Pole-Like Objects From Mobile LiDAR Point Cloud Data

Kang, Z.; Yang, Juntao; Zhong, Ruofei; Wu, Yongxing; Shi, Zhenwei; Lindenbergh, R.C.

doi:10.1109/JSTARS.2018.2869801

Voxel-Based Extraction and Classification of 3-D Pole-Like Objects From Mobile LiDAR Point Cloud Data

Title

Voxel-Based Extraction and Classification of 3-D Pole-Like Objects From Mobile LiDAR Point Cloud Data

Author

Kang, Z. (Capital Normal University)
Yang, Juntao (Capital Normal University)
Zhong, Ruofei (Capital Normal University)
Wu, Yongxing (Capital Normal University)
Shi, Zhenwei (Capital Normal University)
Lindenbergh, R.C. (TU Delft Optical and Laser Remote Sensing)

Date

2018-01-01

Abstract

The digital mapping of road environment is an important task for road infrastructure inventory and urban planning. Automatic extraction and classification of pole-like objects can remarkably reduce mapping cost and enhance work efficiency. Therefore, this paper proposes a voxel-based method that automatically extracts and classifies three-dimensional (3-D) pole-like objects by analyzing the spatial characteristics of objects. First, a voxel-based shape recognition is conducted to generate a set of pole-like object candidates. Second, according to their isolation and vertical continuity, the pole-like objects are detected and individualized using the proposed circular model with an adaptive radius and the vertical region growing algorithm. Finally, several semantic rules, consisting of shape features and spatial topological relationships, are derived for further classifying the extracted pole-like objects into four categories (i.e., lamp posts, utility poles, tree trunks, and others). The proposed method was evaluated using three datasets from mobile LiDAR point cloud data. The experimental results demonstrate that the proposed method efficiently extracted the pole-like objects from the three datasets, with extraction rates of 85.3%, 94.1%, and 92.3%. Moreover, the proposed method can achieve robust classification results, especially for classifying tree trunks.

Subject

Classification
Data mining
detection
Feature extraction
Laser radar
mobile LiDAR
Principal component analysis
principal component analysis (PCA)
Shape
Three-dimensional displays
Vegetation
vertical pole-like objects