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Alternative TitlePoint Cloud Registration Based on Neighborhood Characteristic Point Extraction and Matching
李新春1; 闫振宇1; 林森1,2,3; 贾迪1
Source Publication光子学报
Indexed ByEI ; CSCD
EI Accession number20202008654242
Contribution Rank2
Funding Organization国家自然科学基金(No.61601213) ; 辽宁省教育厅科学研究一般项目(No.L2014132) ; 辽宁省自然科学基金面上项目(No.2015020100)
Keyword机器视觉 点云配准 邻域特征 曲率 迭代最近点


Other Abstract

In order to solve the problem of poor robustness and low registration accuracy of the iterative closest point algorithm under noise interference and data loss, a point cloud registration method based on neighborhood characteristic point extraction and matching is proposed. Firstly, a neighborhood characteristic parameter is defined, which is composed of three parts: the k-neighborhood curvature of the point, the normal vector inner product’ mean value of the point and the neighborhood points, and the distance variance between the neighborhood points and the neighborhood fitted plane. Neighborhood characteristic parameters and curvature characteristic parameters constructed on moving least square surface are used to extract feature points twice. Secondly, three matching conditions are defined according to the histogram features, and the correct matching point pairs are obtained by double constraints. Finally, in the registration stage, the iterative closest point algorithm of bi-directional k-dimension tree is used to achieve accurate registration. The experimental results show that the registration accuracy of the proposed algorithm is more than 90%higher than that of the iterative closest point algorithm, and it can effectively complete the registration of missing point clouds in noisy environment, which has obvious advantages in robustness and precise registration.

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Document Type期刊论文
Corresponding Author闫振宇
Recommended Citation
GB/T 7714
李新春,闫振宇,林森,等. 基于邻域特征点提取和匹配的点云配准[J]. 光子学报,2020,49(4):1-11.
APA 李新春,闫振宇,林森,&贾迪.(2020).基于邻域特征点提取和匹配的点云配准.光子学报,49(4),1-11.
MLA 李新春,et al."基于邻域特征点提取和匹配的点云配准".光子学报 49.4(2020):1-11.
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