Computer Science ›› 2022, Vol. 49 ›› Issue (8): 143-149.doi: 10.11896/jsjkx.210300275

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Spatial Multi-feature Segmentation of 3D Lidar Point Cloud

YANG Wen-kun, YUAN Xiao-pei, CHEN Xiao-feng, GUO Rui   

  1. School of Automation,Northwestern Polytechnical University,Xi’an 710129,China
  • Received:2021-03-29 Revised:2021-08-04 Published:2022-08-02
  • About author:YANG Wen-kun,born in 1996,postgraduate.His main research interests include image processing and lidar remote sensing.
    CHEN Xiao-feng,born in 1974,Ph.D,associate professor.His main research interests include traffic information engineering and control,machine vision and embedded system application.
  • Supported by:
    Equipment Pre-research Field Fund (61404130125,61404130118) and Natural Science Foundation of Shaanxi Province,China(2019JQ-418).

Abstract: Multi-layer solid-state lidar has become an important tool for environment perception of unmanned platform,and has been widely used in vehicle-mounted environment modeling.Due to the low resolution of lidar,the sensitivity of environmental noise,and the complexity of the scene,the fast and effective segmentation of the scene becomes a key problem in the real-time environment modeling.In view of the obvious curvature difference between buildings and vegetation in the actual collected point cloud data,this paper proposes an improved fast segmentation method of 3D point cloud based on multi-layer lidar.After the initial segmentation of building facade is realized based on curvature segmentation,the weighted Euclidean clustering is used for the second iterative segmentation of the initial segmented point cloud,which can reduce the iterative process and avoid falling into local optimum.Through the unmanned platform data acquisition and processing experiments and public data experiments,the effectiveness of this method in the segmentation of building and vegetation is verified.According to the final segmentation results of the scene,the over segmentation rate,under segmentation rate and correct segmentation rate of the scene are counted,and compared with the traditional region growing segmentation algorithm.The results show that the algorithm has strong applicability and segmentation accuracy in different scenes.

Key words: 3D point cloud, Building, Curvature segmentation, Lidar, Weighted Euclidean distance

CLC Number: 

  • TP79
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