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

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 classificationresults, especially for classifying tree trunks.

Landmark Attribute Analysis for a High-Precision Landmark-Based Local Positioning System

“…Clustering helps detection by providing a shape to the points. Common clustering methods are classified into four groups: region growing methods [25,[34][35][36][37][38], scan linebased methods [39][40][41][42][43], voxel-based methods [44][45][46][47][48][49], and density-based methods [17,20,26,50,51]. These methods are sometimes integrated to improve detection accuracy [36][37][38][39][40].…”

Novel Approach to Automatic Traffic Sign Inventory Based on Mobile Mapping System Data and Deep Learning

“…Previously, proposed approaches tackled the particular properties of traffic signs (i.e., retro-reflectivity for night-time visibility, colour, shape, size, height, orientation, planarity, and verticality), usually following safety standards. These properties require traffic signs to be treated as different objects from traffic lights [11][12][13], poles [14], lanes [15][16][17], trees [18], and other objects present in roads. A review of approaches depending on the object can be found in [19].…”

Street Tree Information Extraction and Dynamics Analysis From Mobile Lidar Point Cloud

“…Point cloud voxelization: When the mobile LiDAR system is traveling in the street, massive amount of points was recorded, containing many types of objects connected by the ground points. Many researchers have adopted similar methods (Yue et al, 2015;Guan et al, 2015;Guan et al, 2016;Guan et al, 2019;Kang et al, 2018b;Liu et al, 2017) when extracting street trees from mobile LiDAR point clouds. The point clouds are assigned to three-dimensional voxels to simplify and regularize discretely distributed point clouds.…”

“…There are two main search modes. One is to use a cylinder model to fit with trunks and crowns (Kang et al, 2018b) to filter point clouds. The other is a hierarchical grid projection method, which searches and extracts the street tree point cloud based on the projection grids (Liu et al, 2017).…”