Point Cloud Registration Algorithm Based on Canonical Correlation Analysis

被引:0
作者
Tang Z. [1 ]
Liu M. [1 ]
Jiang Y. [2 ]
Zhao F. [1 ]
Zhao C. [1 ]
机构
[1] State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, Sichuan
[2] School of Electrical Engineering and Information, Sichuan University, Chengdu, 610065, Sichuan
来源
Zhongguo Jiguang/Chinese Journal of Lasers | 2019年 / 46卷 / 04期
关键词
Affine registration; Canonical correlation analysis; Correlation coefficient; Measurement; Point cloud registration;
D O I
10.3788/CJL201946.0404006
中图分类号
学科分类号
摘要
A point cloud registration algorithm based on canonical correlation analysis is proposed. We centralize the target point cloud and the point cloud to be registered, and rotate it around the coordinate origin. The two sets of point clouds can satisfy the maximum square of the correlation coefficient between the dimensions. The two sets of rotation matrices are solved by typical correlation analysis method. The rotation matrix and the translation vector of the rigid transformation between the two points of the clouds are solved by the rotation matrix, and the registration of the point cloud is realized. We use the proportional square value of the eigenvalues of the covariance matrix to scale the registration point cloud proportionally, and complete the affine registration. The simulation results show that, compared with several other algorithms, the proposed algorithm can be quickly and accurately registered with good stability, when point clouds are out of order, occluded, missing, size scaling and interrupted by noise. © 2019, Chinese Lasers Press. All right reserved.
引用
收藏
相关论文
共 18 条
[1]  
Besl P.J., McKay N.D., A method for registration of 3-D shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 2, pp. 239-256, (1992)
[2]  
Ying S.H., Peng J.G., Du S.Y., Et al., A scale stretch method based on ICP for 3D data registration, IEEE Transactions on Automation Science & Engineering, 6, 3, pp. 559-565, (2009)
[3]  
Hsu C.C., Chang H.E., Lu Y.Y., Map building of unknown environment using PSO-tuned enhanced iterative closest point algorithm, 2013 International Conference on System Science & Engineering(ICSSE), pp. 279-284, (2013)
[4]  
Ji S., Ren Y., Ji Z., Et al., An improved method for registration of point cloud, Optik, 140, pp. 451-458, (2017)
[5]  
Yan S.J., Zhou Y.F., Peng F.Y., Et al., Research on the localisation of the workpieces with large sculptured surfaces in NC machining, The International Journal of Advanced Manufacturing Technology, 23, 5-6, pp. 429-435, (2004)
[6]  
Huang A.W., Sullivan J.M., Kulkarni P., Et al., Automatic 3D image registration using voxel similarity measurements based on a genetic algorithm, Proceedings of SPIE, 6144, pp. 968-976, (2006)
[7]  
Wang C., Shu Q., Yang Y.X., Et al., Quick registration algorithm of point clouds using structure feature, Acta Optica Sinica, 38, 9, (2018)
[8]  
Zhao M., Shu Q., Chen W., Et al., Three-dimensional point cloud registration algorithm based on l<sup>p</sup> spatial mechanics model, Acta Optica Sinica, 38, 10, (2018)
[9]  
Myronenko A., Song X.B., Point set registration: coherent point drift, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 12, pp. 2262-2275, (2010)
[10]  
Yang J.L., Li H.D., Campbell D., Et al., Go-ICP: a globally optimal solution to 3D ICP point-set registration, IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 11, pp. 2241-2254, (2016)