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Enhanced ground segmentation method for Lidar point clouds in human-centric autonomous robot systems
Phuong Minh Chu1; Seoungjae Cho1; Jisun Park1; Simon Fong2; Kyungeun Cho1
2019-05-09
Source PublicationHuman-centric Computing and Information Sciences
ISSN2192-1962
Volume9Issue:1
Abstract

Ground segmentation is an important step for any autonomous and remote-controlled systems. After separating ground and nonground parts, many works such as object tracking and 3D reconstruction can be performed. In this paper, we propose an efficient method for segmenting the ground data of point clouds acquired from multi-channel Lidar sensors. The goal of this study is to completely separate ground points and nonground points in real time. The proposed method segments ground data efficiently and accurately in various environments such as flat terrain, undulating/rugged terrain, and mountainous terrain. First, the point cloud in each obtained frame is divided into small groups. We then focus on the vertical and horizontal directions separately, before processing both directions concurrently. Experiments were conducted, and the results showed the effectiveness of the proposed ground segment method. For flat and sloping terrains, the accuracy is over than 90%. Besides, the quality of the proposed method is also over than 80% for bumpy terrains. On the other hand, the speed is 145 frames per second. Therefore, in both simple and complex terrains, we gained good results and real-time performance.

KeywordHuman-centric Internet Of Things Autonomous Robot Point Cloud Ground Segmentation
DOI10.1186/s13673-019-0178-5
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000467572700001
PublisherSPRINGEROPEN, CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
Scopus ID2-s2.0-85065673531
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Citation statistics
Cited Times [WOS]:11   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorKyungeun Cho
Affiliation1.Department of Multimedia Engineering,Dongguk University-Seoul,Seoul,30 Pildong-ro 1-gil, Jung-gu,04620,South Korea
2.Department of Computer and Information Science,University of Macau,Macau,3000,China
Recommended Citation
GB/T 7714
Phuong Minh Chu,Seoungjae Cho,Jisun Park,et al. Enhanced ground segmentation method for Lidar point clouds in human-centric autonomous robot systems[J]. Human-centric Computing and Information Sciences,2019,9(1).
APA Phuong Minh Chu,Seoungjae Cho,Jisun Park,Simon Fong,&Kyungeun Cho.(2019).Enhanced ground segmentation method for Lidar point clouds in human-centric autonomous robot systems.Human-centric Computing and Information Sciences,9(1).
MLA Phuong Minh Chu,et al."Enhanced ground segmentation method for Lidar point clouds in human-centric autonomous robot systems".Human-centric Computing and Information Sciences 9.1(2019).
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