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
[1]MUSIALSKI P,WONKA P,ALIAGA D G,et al.A Survey ofUrban Reconstruction [J].Computer Graphics Forum,2013,32(6):146-177.
[2]HE Y R,ZHENG Y M,PAN H P,et al.Real Three-dimensional Modeling and Application of Complex Construction based on the Point Cloud Data [J].Remote Sensing Technology and Application,2016,31(6):1091-1099.
[3]ZHANG P Z,ZHANG H Y.A Review of Features and Labels Dimensionality Reduction Methods of Multi Label Data[J].Journal of Chongqing Technology and Business University(Na-tural Science Edition),2020,37(5):23-29.
[4]HONG S X,WANG J X.Geomatics S O.Aerial LiDAR Building Point-cloud Extraction Algorithm Combining OTSU and Iterative TIN[J].Remote Sensing Information,2018,33(6):79-85.
[5]YANG Y,YANG G,ZHENG T,et al.Feature extraction me-thod based on 2.5-dimensions lidar platformfor indoor mobile robots localization[C]//2017 IEEE International Conference on Cybernetics and Intelligent Systems(CIS) and Automation and Mechatronics(RAM).2017:28-34.
[6]WEI Z,YANG B Q,LI Q Q.Automated extraction of building footprints from mobile LIDAR point clouds[J].Journal of Remote Sensing,2012,16(2):286-296.
[7]ZHU J T,WANG L,ZHAO Z.Point cloud segmentation ofcomplex building roof based on region growing algorithm[J].Remote Sensing of Land and Resources,2019,31(4):20-25.
[8]HU H L,LI Z,JIN X G,et al.Curve Skeleton Extraction From 3D Point Clouds Through Hybrid Feature Point Shifting and Clustering[J].Computer Graphics Forum,2020,39(6):111-132.
[9]AHMED M,RAIHAN SERAJ R,ISLAM S M S.The k-means Algorithm:A Comprehensive Survey and Performance Evaluation [J].Electronics,2020,9(8):1295.
[10]SUN S L,YANG Q,ZHANG Y M.A Parameter Simplification Convolutional Neural Network Model for Image Segmentation[J].Journal of Chongqing University of Technology(Natural Science),2021,35(3)145-151.
[11]WU Y L,LI Y Q.Convolutional Network Based Pathological Nucleus Segmentation[J].Journal of Chongqing Technology and Business University(Natural Science Edition),2019,36(3):67-71.
[12]QIU Z,ZHUANG Y,YAN F,et al.RGB-DI images and fullconvolution neural network-based outdoor scene understanding for mobile robots[J].IEEE Transactions on Instrumentation and Measurement,2018,68(1):27-37.
[13]KHAN I,LUO Z,HUANG J Z,et al.Variable Weighting inFuzzy K-means Clustering To Determine the Number of Clusters[J].IEEE Transactions on Knowledge and Data Enginee-ring,2020,32(9):1938-1853.
[14]HE Y B,CHEN R L,WU K,et al.Point cloud simplificationmethod based on K-means clustering[J].Progress in Laser and Optoelectronics,2019,56(9):96-99.
[15]DENG D S.Application of DBSCAN Algorithm in Data Sam-pling[J].Journal of Physics:Conference Series,2020,1617(1):012088.
[16]CUI Z.Extracting facade information of the side hall of the Forbidden City by European clustering [J].Science and Technology Innovation,2020(18):75-78.
[17]BORCS A,NAGY B,BENEDEK C.Instant Object Detection in Lidar Point Clouds[J].IEEE Geoscience & Remote Sensing Letters,2017,14(7):992-996.
[18]SHI H E,SUN X Y,HUANG J H.Dehazing Algorithm for Remote Sensing Image Optimization Based on Curvature Filtering[J].Acta Photonica Sinica,2021,50(2):0210003.
[19]LI S L.Research of Pulsed Laser Ranging system[D].Guilin:Guilin Universoty of Electronic Technology,2015.
[20]ZHANG X S,HUA S G.Surface Reconstruction from Point Cloud Combining Plane Projection and Region-growing[J].Mechanical&Electrical Englneering Technology,2020,49(6):23-24,153.
[1] SUN Xuan, WANG Huan-xiao. Capability Building for Government Big Data Safety Protection:Discussions from Technologicaland Management Perspectives [J]. Computer Science, 2022, 49(4): 67-73.
[2] ZHANG Hu, BAI Ping. Graph Convolutional Networks with Long-distance Words Dependency in Sentences for Short Text Classification [J]. Computer Science, 2022, 49(2): 279-284.
[3] TIAN Ye, CHEN Hong-wei, WANG Fa-sheng, CHEN Xing-wen. Overview of SLAM Algorithms for Mobile Robots [J]. Computer Science, 2021, 48(9): 223-234.
[4] XIONG Zhao-yang, WANG Ting. Image Recognition for Building Components Based on Convolutional Neural Network [J]. Computer Science, 2021, 48(6A): 51-56.
[5] ZHAO Xin-can, CHANG Han-xing, JIN Ren-biao. 3D Point Cloud Shape Completion GAN [J]. Computer Science, 2021, 48(4): 192-196.
[6] LI Ke-yue, CHEN Yi, NIU Shao-zhang. Social E-commerce Text Classification Algorithm Based on BERT [J]. Computer Science, 2021, 48(2): 87-92.
[7] XIAO Kui, CHEN Zhi-xiong, LIU Guo-jun, HUANG Zhi-fang. Approach for Discovering Prerequisite Relationships Between User Generated Learning Resources [J]. Computer Science, 2019, 46(6A): 598-600.
[8] HENG Hong-jun, WANG Rui. Long-term Operational Situation Assessment System for Terminal Buildings [J]. Computer Science, 2019, 46(5): 310-314.
[9] LI Guang-jing, BAO Hong, XU Cheng. Real-time Road Edge Extraction Algorithm Based on 3D-Lidar [J]. Computer Science, 2018, 45(9): 294-298.
[10] ZHAO Er-ping, MENG Xiao-feng. Spatial Index of 3D Point Cloud Data Based on Spark [J]. Computer Science, 2018, 45(9): 213-219.
[11] YU Yong and GUO Qian. Behavioral Model Construction Method for Mobile Applications Based on Smali Code [J]. Computer Science, 2017, 44(11): 207-220.
[12] ZHAO Er-ping, DANG Hong-en and LIU Wei. Research on Detail Level Index Technology of Massive 3D Point Cloud Data in Virtual Tourism [J]. Computer Science, 2017, 44(10): 171-176.
[13] QIN Xu-jia, CHEN Lou-heng, TAN Xiao-jun, ZHENG Hong-bo and ZHANG Mei-yu. Layer by Layer Triangulation Algorithm for 3D Point Clouds from Structured Light Vision [J]. Computer Science, 2016, 43(Z11): 383-387.
[14] ZENG Lan-ling, ZHANG Wei, YANG Yang and ZHAN Yong-zhao. Multiple Density Leaf Reconstruction Based on Limited Details [J]. Computer Science, 2016, 43(8): 292-296.
[15] ZHANG Zhi-gang, SUN Li-cai and WANG Pei. Research on Pedestrian Detection Method Based on Laser Scanning [J]. Computer Science, 2016, 43(7): 328-330.
Full text



No Suggested Reading articles found!