Three-Dimensional Point Cloud Registration Algorithm Based on Factor Analysis

Zhirong Tang; Yue Jiang; Changwei Miao; Chengqiang Zhao

doi:10.3788/LOP56.191503

[1] Besl P J. McKay N D. A method for registration of 3-D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 239-256(1992).

[2] He[\s]{1}SJ,[\s]{1}Zhao[\s]{1}ST,[\s]{1}BaiF,[\s]{1}et[\s]{1}al.[\s]{1}A[\s]{1}method[\s]{1}for[\s]{1}spatial[\s]{1}data[\s]{1}registration[\s]{1}based[\s]{1}on[\s]{1}PCA-ICP[\s]{1}algorithm[J].[\s]{1}Advanced[\s]{1}Materials[\s]{1}Research,[\s]{1}2013,[\s]{1}718/719/720:[\s]{1}1033-[\s]{1}1036.[\s]{1}

[3] Ying S, Peng J, Du S et al. A scale stretch method based on ICP for 3D data registration[J]. IEEE Transactions on Automation Science & Engineering, 6, 559-565(2009). http://ieeexplore.ieee.org/document/4982554

[4] Sharp G C, Lee S W, Wehe D K. ICP registration using invariant features[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 90-102(2002). http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=982886

[5] Yang J L, Li H D, Campbell D et al. Go-ICP: a globally optimal solution to 3D ICP point-set registration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 2241-2254(2016). http://europepmc.org/abstract/MED/26731638

[6] Yang J L, Li H D, Jia Y D. Go-ICP: solving 3D registration efficiently and globally optimally. [C]∥2013 IEEE International Conference on Computer Vision, December 1-8, 2013, Sydney, Australia. New York: IEEE, 1457-1464(2013).

[7] Ji S, Ren Y, Ji Z et al. An improved method for registration of point cloud[J]. Optik, 140, 451-458(2017). http://www.sciencedirect.com/science/article/pii/S0030402617300517

[8] 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[J]. The International Journal of Advanced Manufacturing Technology, 23, 429-435(2004). http://link.springer.com/article/10.1007/s00170-003-1897-2

[9] Huang W, Sullivan J M, Kulkarni P et al. Automatic 3D image registration using voxel similarity measurements based on a genetic algorithm[J]. Proceedings of SPIE, 6144, 614430(2006). http://spie.org/x648.html?product_id=653979

[10] Yu C, Ju D. A maximum feasible subsystem for globally optimal 3D point cloud registration[J]. Sensors, 18, 544(2018). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5856135/

[11] Myronenko A, Song X B. Point set registration: coherent point drift[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 2262-2275(2010). http://dl.acm.org/citation.cfm?id=1907655.1908061

[12] Campbell D, Petersson L. GOGMA: globally-optimal Gaussian mixture alignment. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 5685-5694(2016).

[13] Li Q S, Xiong R, Vidal-Calleja T. A GMM based uncertainty model for point clouds registration[J]. Robotics and Autonomous Systems, 91, 349-362(2017). http://www.sciencedirect.com/science/article/pii/S0921889015303109

[14] Jian B, Vemuri B C. Robust point set registration using Gaussian mixture models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 1633-1645(2011). http://dl.acm.org/citation.cfm?id=2006992

[15] Zhang X T, Jian L B, Xu M F. Robust 3D point cloud registration based on bidirectional Maximum Correntropy Criterion[J]. PLoS One, 13, e0197542(2018). http://europepmc.org/abstract/MED/29799864

[16] Luo N, Wang Q. Effective outlier matches pruning algorithm for rigid pairwise point cloud registration using distance disparity matrix[J]. IET Computer Vision, 12, 220-232(2018). http://ieeexplore.ieee.org/document/8306410

[17] Prakhya S M, Liu B B, Lin W S. B-SHOT: a binary feature descriptor for fast and efficient keypoint matching on 3D point clouds. [C]∥2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 28-October 2, 2015, Hamburg, Germany. New York: IEEE, 1929-1934(2015).

[18] Ge X M. Automatic markerless registration of point clouds with semantic-keypoint-based 4-points congruent sets[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 344-357(2017). http://www.sciencedirect.com/science/article/pii/S0924271616304439

[19] Quan S W, Ma J, Hu F Y et al. Local voxelized structure for 3D binary feature representation and robust registration of point clouds from low-cost sensors[J]. Information Sciences, 444, 153-171(2018). http://www.sciencedirect.com/science/article/pii/S0020025518301646

[20] Persad R A, Armenakis C. Automatic co-registration of 3D multi-sensor point clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 162-186(2017). http://www.sciencedirect.com/science/article/pii/S0924271617300771

[21] Wang C, Shu Q, Yang Y X et al. Quick registration algorithm of point clouds using structure feature[J]. Acta Optica Sinica, 38, 0911005(2018).

[22] Zhao M, Shu Q, Chen W et al. Three-dimensional point cloud registration algorithm based on l p spatial mechanics model[J]. Acta Optica Sinica, 38, 1010005(2018).

[23] Zhang Z, Xu H L, Yin H. A fast point cloud registration algorithm based on key point selection[J]. Laser & Optoelectronics Progress, 54, 121001(2017).