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3d hough transform algorithm for ground surface extraction from lidar point clouds
Wei Song1,4; LingFeng Zhang1; Yifei Tian2; Simon Fong2; Su Sun3
2019-10-21
Conference Name2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
Source PublicationProceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019
Pages916-921
Conference Date14-17 July 2019
Conference PlaceAtlanta, GA, USA
PublisherIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Abstract

Currently, the application of ground surface extraction technology in 3D point clouds has attracted extensive research attention. Such technology is widely used in the environmental perception and local navigation functions of Unmanned Ground Vehicles (UGVs). However, due to the heterogeneous density and unstructured spatial distribution of point clouds, the computational time and space complexity is relatively high in ground detection. In addition, since light detection and ranging (LiDAR) sensing can obtain more than 700,000 points per second, ground point clouds require more memory, compared to non-ground point clouds. Thus, ground point clouds extraction is a vitally important function for UGVs to realize intelligent driving. To extract precise ground information, this paper proposes a 3D Hough transform (3DHT) algorithm for ground detection from 3D LiDAR point clouds. We create a special Hough space within the walking slope range and transform all of the 3D points into this 3D Hough space according to the proposed 3DHT algorithm. Then, the maximal peak is extracted and ground equation parameters are obtained using the inverse Hough transform. To reduce the time required, we apply a Graphics Processing Unit (GPU) parallel computation method to reduce the number of typically exhaustive iterations.

KeywordLidar Point Clouds Ground Surface Extraction 3d Hough Transform Parallel Computing
DOI10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00163
URLView the original
Indexed ByCPCI-S
Language英語English
WOS Research AreaComputer Science ; Science & Technology - Other Topics ; Telecommunications
WOS SubjectComputer Science, Theory & Methods ; Green & Sustainable Science & Technology ; Telecommunications
WOS IDWOS:000579857700140
Scopus ID2-s2.0-85074859010
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorWei Song
Affiliation1.North China University of Technology,Beijing,China
2.Department of Computer and Information Science,University of Macau,Macao
3.Department of Computer Science,Georgia State University,Atlanta,United States
4.Beijing Key Lab on Urban Intelligent Traffic Control Technology,Beijing,China
Recommended Citation
GB/T 7714
Wei Song,LingFeng Zhang,Yifei Tian,et al. 3d hough transform algorithm for ground surface extraction from lidar point clouds[C]:IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA,2019:916-921.
APA Wei Song,LingFeng Zhang,Yifei Tian,Simon Fong,&Su Sun.(2019).3d hough transform algorithm for ground surface extraction from lidar point clouds.Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019,916-921.
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