中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 合肥物质科学研究院
  3. 中国科学院合肥物质科学研究院
A Coarse-to-Fine Registration Approach for Point Cloud Data with Bipartite Graph Structure

文献类型：期刊论文

作者Yuan, Munan1,2; Li, Xiru1; Cheng, Longle1,2; Li, Xiaofeng1; Tan, Haibo1
刊名ELECTRONICS
出版日期2022
卷号11
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)
DOI10.3390/electronics11020263
通讯作者Yuan, Munan(mnyuan@hfcas.ac.cn) ; Li, Xiaofeng(xrli@hfcas.ac.cn)
英文摘要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.
WOS关键词POSE ESTIMATION ; ALGORITHM ; ICP ; RECOGNITION ; SETS
资助项目National Natural Science Foundation of Anhui[1708085QF153]
WOS研究方向Computer Science ; Engineering ; Physics
语种英语
WOS记录号WOS:000757980500001
出版者MDPI
资助机构National Natural Science Foundation of Anhui
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/127470]  
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).

入库方式： OAI收割

来源：合肥物质科学研究院

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。