Point cloud registration of rock mass is the basis of rock mass engineering. Although the classic point cloud registration method can be well applied to ordinary point clouds, it can not be used for rock point cloud registration. Due to the complex surface structure of the point cloud of the rock mass, most of the area is flat, based on the characteristics of point cloud of rock mass, this paper proposes a rock mass point cloud registration algorithm that filters matching points layer by layer through geometric features, by introducing the eigenvalues and eigenvector matrices of the covariance matrix of matching point pairs, and geometric features such as curvature and principal direction, the matching point pairs can be accurately found. The experimental test and analysis results on different rock mass point clouds show that the algorithm in this paper has obvious advantages in accuracy.
ZHANG Tao
,
XIAO Jun
,
WANG Ying
. Point cloud registration method with layer-by-layer filtering of matching points[J]. Journal of University of Chinese Academy of Sciences, 2022
, 39(3)
: 352
-359
.
DOI: 10.7523/j.ucas.2020.0020
[1] Besl P J, McKay N D. A method for registration of 3-D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2):239-256. DOI:10.1109/34.121791.
[2] 王飞鹏, 肖俊, 王颖, 等. 一种基于高斯曲率的ICP改进算法[J]. 中国科学院大学学报, 2019, 36(5):702-708. DOI:10.7523/j.issn.2095-6134.2019.05.016.
[3] Aiger D, Mitra N J, Cohen-Or D. 4-points congruent sets for robust pairwise surface registration[J]. ACM Transactions on Graphics, 2008, 27(3): 1-10. DOI:10.1145/1360612.1360684.
[4] Yang B S, Zang Y F. Automated registration of dense terrestrial laser-scanning point clouds using curves[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 95:109-121. DOI:10.1016/j.isprsjprs.2014.05.012.
[5] Xian Y R, Xiao J, Wang Y.A fast registration algorithm of rock point cloud based on spherical projection and feature extraction[J]. Frontiers of Computer Science, 2019, 13(1): 170-182. DOI:10.1007/s11704-016-6191-1.
[6] Wang F P, Xiao J, Wang Y. Efficient rock-mass point cloud registration using n-point complete graphs[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019,57(11): 9332-9343. DOI:10.1109/TGRS.2019.2926201.
[7] Hu L, Xiao J, Wang Y. An automatic 3D registration method for rock mass point clouds based on plane detection and polygon matching[J]. The Visual Computer, 2020, 36(4): 669-681. DOI:10.1007/s00371-019-01648-z.
[8] Biber P, Straßer W. The normal distributions transform: a new approach to laser scan matching[ C]// Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat.No.03CH37453). October 27-31, 2003, Las Vegas, NV, USA. IEEE, 2003: 2743-2748. DOI:10.1109/IROS.2003.1249285.
[9] Vongkulbhisal J, de La Torre F, Costeira J P. Discriminative optimization: theory and applications to point cloud registratio [C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). July 21-26, 2017, Honolulu, HI, USA. IEEE, 2017: 3975-3983. DOI:10.1109/CVPR.2017.423.
[10] Elbaz G, Avraham T, Fischer A. 3D point cloud registration for localization using a deep neural network auto-encoder [C]// 2017 IEEE Conference on Computer Vision and Pattern Recongition(CVPR). July 21-26, 2017, Honolulu, HI, USA. IEEE, 2017: 2472-2481. DOI:10.1109/CVPR.2017.265.
[11] Lu W X, Wan G W, Zhou Y, et al. DeepVCP: an end-to-end deep neural network for point cloud registration [C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). October 27-November 2, 2019, Seoul, Korea (South). IEEE, 2019:12-21. DOI:10.1109/ICCV.2019.00010.
[12] 舒程珣,何云涛,孙庆科. 基于卷积神经网络的点云配准方法[J]. 激光与光电子学进展,2017, 54(3): 129-137. DOI:10.3788/LOP54.031001.
[13] 刘鸣, 舒勤, 杨赟秀, 等. 基于独立成分分析的三维点云配准算法[J]. 激光与光电子学进展, 2019, 56(1):181-189. DOI:10.3788/LOP56.011203.
[14] Zhang X P, Li H J, Cheng Z L, et al. Robust curvature estimation and geometry analysis of 3D point cloud surfaces[J]. Journal of Information & Computational Science, 2009, 6(5) : 1983-1990.
[15] Holz D, Ichim A E, Tombari F, et al. Registration with the Point Cloud Library: a modular framework for aligning in 3-D[J]. IEEE Robotics & Automation Magazine, 2015, 22(4):110-124. DOI:10.1109/MRA.2015.2432331.
[16] Lato M, Kemeny J, Harrap R M, et al. Rock bench: establishing a common repository and standards for assessing rockmass characteristics using LiDAR and photogrammetry[J]. Computers & Geosciences, 2013, 50:106-114. DOI:10.1016/j.cageo.2012.06.014.