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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
作者部门光电信息技术研究室
会议名称9th International Conference on Graphic and Image Processing, ICGIP 2017
会议日期October 14-16, 2017
会议地点Qingdao, China
会议主办者Ocean University of China ; University of Portsmouth
会议录名称Proceedings of SPIE - The International Society for Optical Engineering
出版者SPIE
出版地Bellingham, WA
2017
页码1-8
收录类别EI ; CPCI(ISTP)
EI收录号20181905168338
WOS记录号WOS:000434707200051
产权排序1
ISSN号0277-786X
ISBN号978-15106-1741-4
关键词Point Cloud Registration Local Surface Patch Coarse To Fine
摘要

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.

语种英语
引用统计
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/22061
专题光电信息技术研究室
通讯作者Zhu F(朱枫)
作者单位1.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
推荐引用方式
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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