Hierarchical Optimization of 3D Point Cloud Registration

doi:10.3390/s20236999

	Hierarchical Optimization of 3D Point Cloud Registration
	Liu, Huikai 1,2; Zhang, Yue 1,2; Lei, Linjian1,3 ; Xie, Hui 1,2; Li, Yan 1,2; Sun, Shengli 1
	2020-12
发表期刊	SENSORS (IF:3.4[JCR-2023],3.7[5-Year])
卷号	20 期号:23
发表状态	已发表
DOI	10.3390/s20236999
摘要	Rigid registration of 3D point clouds is the key technology in robotics and computer vision. Most commonly, the iterative closest point (ICP) and its variants are employed for this task. These methods assume that the closest point is the corresponding point and lead to sensitivity to the outlier and initial pose, while they have poor computational efficiency due to the closest point computation. Most implementations of the ICP algorithm attempt to deal with this issue by modifying correspondence or adding coarse registration. However, this leads to sacrificing the accuracy rate or adding the algorithm complexity. This paper proposes a hierarchical optimization approach that includes improved voxel filter and Multi-Scale Voxelized Generalized-ICP (MVGICP) for 3D point cloud registration. By combining traditional voxel sampling with point density, the outlier filtering and downsample are successfully realized. Through multi-scale iteration and avoiding closest point computation, MVGICP solves the local minimum problem and optimizes the operation efficiency. The experimental results demonstrate that the proposed algorithm is superior to the current algorithms in terms of outlier filtering and registration performance.
关键词	3D point cloud registration improved voxel filter multi-scale voxelized GICP
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收录类别	SCI ; SCIE ; EI
语种	英语
资助项目	Innovation Project of Shanghai Institute of Technical Physics, Chinese Academy of Sciences[X-209]
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000597527800001
出版者	MDPI
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/124911
专题	信息科学与技术学院_硕士生信息科学与技术学院_博士生
通讯作者	Sun, Shengli
作者单位	1.Chinese Acad Sci, Shanghai Inst Tech Phys, Shanghai 200083, Peoples R China 2.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China 3.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China
推荐引用方式 GB/T 7714	Liu, Huikai,Zhang, Yue,Lei, Linjian,et al. Hierarchical Optimization of 3D Point Cloud Registration[J]. SENSORS,2020,20(23).
APA	Liu, Huikai,Zhang, Yue,Lei, Linjian,Xie, Hui,Li, Yan,&Sun, Shengli.(2020).Hierarchical Optimization of 3D Point Cloud Registration.SENSORS,20(23).
MLA	Liu, Huikai,et al."Hierarchical Optimization of 3D Point Cloud Registration".SENSORS 20.23(2020).