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LSAH: A fast and efficient local surface feature for point cloud registration
Lu RR(鲁荣荣)1,2,3,4; Zhu F(朱枫)1,3,4; Wu QX(吴清潇)1,3,4; Kong YZ(孔研自)1,2,3,4
Department光电信息技术研究室
Conference Name9th International Conference on Graphic and Image Processing, ICGIP 2017
Conference DateOctober 14-16, 2017
Conference PlaceQingdao, China
Author of SourceOcean University of China ; University of Portsmouth
Source PublicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSPIE
Publication PlaceBellingham, WA
2017
Pages1-8
Indexed ByEI ; CPCI(ISTP)
EI Accession number20181905168338
WOS IDWOS:000434707200051
Contribution Rank1
ISSN0277-786X
ISBN978-15106-1741-4
KeywordPoint Cloud Registration Local Surface Patch Coarse To Fine
Abstract

Point cloud registration is a fundamental task in high level three dimensional applications. Noise, uneven point density and varying point cloud resolutions are the three main challenges for point cloud registration. In this paper, we design a robust and compact local surface descriptor called Local Surface Angles Histogram (LSAH) and propose an effectively coarse to fine algorithm for point cloud registration. The LSAH descriptor is formed by concatenating five normalized sub-histograms into one histogram. The five sub-histograms are created by accumulating a different type of angle from a local surface patch respectively. The experimental results show that our LSAH is more robust to uneven point density and point cloud resolutions than four state-of-the-art local descriptors in terms of feature matching. Moreover, we tested our LSAH based coarse to fine algorithm for point cloud registration. The experimental results demonstrate that our algorithm is robust and efficient as well.

Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/22061
Collection光电信息技术研究室
Corresponding AuthorZhu F(朱枫)
Affiliation1.Shenyang Institute of Automation, CAS, Shenyang 110016, China;
2.University of Chinese Academy of Sciences, Beijing 100049, China;
3.Key Laboratory of Opto-Electronic Information Processing, CAS, Shenyang 110016, China;
4.Key Lab of Image Understanding and Computer Vision, Liaoning Province, Shenyang 110016, China
Recommended Citation
GB/T 7714
Lu RR,Zhu F,Wu QX,et al. LSAH: A fast and efficient local surface feature for point cloud registration[C]//Ocean University of China, University of Portsmouth. Bellingham, WA:SPIE,2017:1-8.
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