Registration Method of Partial Point Cloud and Whole Point Cloud of Large Workpiece

被引：0

作者：

Fan L. ^{[1
,2
,3
]}

Wang J. ^{[1
,2
]}

Xu Z. ^{[1
,2
]}

Yang X. ^{[1
,2
]}

Zhu X. ^{[1
,2
,3
]}

Dong Q. ^{[1
,2
,4
]}

Wu X. ^{[5
]}

机构：

[1] State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang

[2] Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang

[3] Shenyang Institute of Automation, University of Chinese Academy of Sciences, Beijing

[4] School of Mechanical and Electrical Engineering, Shenyang Aerospace University, Shenyang

[5] Xi’an North Huian Chemical Industries Co.,Ltd, Xi’an

来源：

Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2023年 / 35卷 / 09期

关键词：

dimensional evaluation; local feature description; partial point cloud; point cloud registration;

D O I：

10.3724/SP.J.1089.2023.19688

中图分类号：

学科分类号：

摘要：

The high-efficiency and high-precision registration of the partial point cloud and the whole point cloud is the basis for the rapid evaluation of the size of large workpieces. However, due to the difference between the global features of the partial point cloud and the whole point cloud, using the existing local feature descriptors for point pair matching search requires a lot of computation, and point cloud registration takes a long time. To solve this problem, in view of the geometric features of partial point cloud and whole point cloud, a registration method of partial point cloud and whole point cloud based on regional mean feature descriptor is proposed. Firstly, a regional mean feature descriptor is proposed, which can effectively describe the neighborhood geometric features of key points in the point cloud. Secondly, the data points are selected as the key points to be registered by evaluating the feature degree of the regional mean feature descriptors, search the matching descriptor to complete the key point matching between the partial point cloud and the whole point cloud. Finally, use the singular value decomposition method to calculate the transformation matrix between the point clouds, and register the partial point cloud and the whole point cloud based on the iterative closest point algorithm. The registration accuracy and registration speed are tested by using the point cloud set of the Stanford public database and the 3D scanning point cloud data of a large engine compartment. Compared with the point cloud registration methods of PFH, HoPPF, PPFH, and FPFH, the registration accuracy of the proposed method is increased by 56.75% on average, and the registration speed is increased by 45.57% on average. The effectiveness of the method is verified. © 2023 Institute of Computing Technology. All rights reserved.

引用

页码：1323 / 1332

页数：9

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