A Coarse-to-Fine Registration Approach for Point Cloud Data with Bipartite Graph Structure

doi:10.3390/electronics11020263

HFCAS OpenIR

	A Coarse-to-Fine Registration Approach for Point Cloud Data with Bipartite Graph Structure
	Yuan, Munan1,2 ; Li, Xiru1 ; Cheng, Longle 1,2; Li, Xiaofeng1 ; Tan, Haibo1
	2022
发表期刊	ELECTRONICS
通讯作者	Yuan, Munan(mnyuan@hfcas.ac.cn) ; Li, Xiaofeng(xrli@hfcas.ac.cn)
摘要	Alignment is a critical aspect of point cloud data (PCD) processing, and we propose a coarse-to-fine registration method based on bipartite graph matching in this paper. After data pre-processing, the registration progress can be detailed as follows: Firstly, a top-tail (TT) strategy is designed to normalize and estimate the scale factor of two given PCD sets, which can combine with the coarse alignment process flexibly. Secondly, we utilize the 3D scale-invariant feature transform (3D SIFT) method to extract point features and adopt fast point feature histograms (FPFH) to describe corresponding feature points simultaneously. Thirdly, we construct a similarity weight matrix of the source and target point data sets with bipartite graph structure. Moreover, the similarity weight threshold is used to reject some bipartite graph matching error-point pairs, which determines the dependencies of two data sets and completes the coarse alignment process. Finally, we introduce the trimmed iterative closest point (TrICP) algorithm to perform fine registration. A series of extensive experiments have been conducted to validate that, compared with other algorithms based on ICP and several representative coarse-to-fine alignment methods, the registration accuracy and efficiency of our method are more stable and robust in various scenes and are especially more applicable with scale factors.
关键词	point cloud coarse-to-fine registration top-tail (TT) strategy bipartite graph matching 3D scale-invariant feature transform (3D SIFT) fast point feature histograms (FPFH) trimmed iterative closest point (TrICP)
DOI	10.3390/electronics11020263
关键词[WOS]	POSE ESTIMATION ; ALGORITHM ; ICP ; RECOGNITION ; SETS
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of Anhui[1708085QF153]
项目资助者	National Natural Science Foundation of Anhui
WOS研究方向	Computer Science ; Engineering ; Physics
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Physics, Applied
WOS记录号	WOS:000757980500001
出版者	MDPI
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/127471
专题	中国科学院合肥物质科学研究院
通讯作者	Yuan, Munan; Li, Xiaofeng
作者单位	1.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China 2.Grad Sch USTC, Sci Isl Branch, Hefei 230026, Peoples R China
推荐引用方式 GB/T 7714	Yuan, Munan,Li, Xiru,Cheng, Longle,et al. A Coarse-to-Fine Registration Approach for Point Cloud Data with Bipartite Graph Structure[J]. ELECTRONICS,2022,11.
APA	Yuan, Munan,Li, Xiru,Cheng, Longle,Li, Xiaofeng,&Tan, Haibo.(2022).A Coarse-to-Fine Registration Approach for Point Cloud Data with Bipartite Graph Structure.ELECTRONICS,11.
MLA	Yuan, Munan,et al."A Coarse-to-Fine Registration Approach for Point Cloud Data with Bipartite Graph Structure".ELECTRONICS 11(2022).