[1] Wang J H, Lindenbergh R, Menenti M. SigVox: a 3D feature matching algorithm for automatic street object recognition in mobile laser scanning point clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 128: 111-129.

[2] Yang P, Zhou Y H, Yao J, et al. Three-dimensional shape reconstruction via an objective function optimization-based point cloud registration method[J]. Optical Engineering, 2017, 56(11): 113108.

[3] Yue H S, Chen W H, Wu X M, et al. Extension of an iterative closest point algorithm for simultaneous localization and mapping in corridor environments[J]. Journal of Electronic Imaging, 2016, 25(2): 023015.

[4] Chen C S, Chen P C, Hsu C M. Three-dimensional object recognition and registration for robotic grasping systems using a modified viewpoint feature histogram[J]. Sensors, 2016, 16(11): 1969.

[5] 蒋悦, 黄宏光, 舒勤, 等. 高维正交子空间映射的尺度点云配准算法[J]. 光学学报, 2019, 39(3): 0315007.

    Jiang Y, Huang H G, Shu Q, et al. Scale point cloud registration algorithm in high-dimensional orthogonal subspace mapping[J]. Acta Optica Sinica, 2019, 39(3): 0315007.

[6] Ji S J, Ren Y C, Ji Z, et al. An improved method for registration of point cloud[J]. Optik, 2017, 140: 451-458.

[7] 刘剑, 白迪. 基于特征匹配的三维点云配准算法[J]. 光学学报, 2018, 38(12): 1215005.

    Liu J, Bai D. 3D point cloud registration algorithm based on feature matching[J]. Acta Optica Sinica, 2018, 38(12): 1215005.

[8] 李仁忠, 杨曼, 田瑜, 等. 基于ISS特征点结合改进ICP的点云配准算法[J]. 激光与光电子学进展, 2017, 54(11): 111503.

    Li R Z, Yang M, Tian Y, et al. Point cloud registration algorithm based on the ISS feature points combined with improved ICP algorithm[J]. Laser & Optoelectronics Progress, 2017, 54(11): 111503.

[9] ZhongY. Intrinsic shape signatures: a shape descriptor for 3D object recognition[C]∥2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, September 27-October 4, 2009, Kyoto, Japan. New York: IEEE, 2009: 689- 696.

[10] 贾勇杰, 熊风光, 韩燮, 等. 基于SHOT的多尺度关键点检测技术研究[J]. 激光与光电子学进展, 2018, 55(7): 071013.

    Jia Y J, Xiong F G, Han X, et al. Multi-scale keypoint detection based on SHOT[J]. Laser & Optoelectronics Progress, 2018, 55(7): 071013.

[11] 张哲, 许宏丽, 尹辉. 一种基于关键点选择的快速点云配准算法[J]. 激光与光电子学进展, 2017, 54(12): 121002.

    Zhang Z, Xu H L, Yin H. A fast point cloud registration algorithm based on key point selection[J]. Laser & Optoelectronics Progress, 2017, 54(12): 121002.

[12] He B W, Lin Z M, Li Y F. An automatic registration algorithm for the scattered point clouds based on the curvature feature[J]. Optics & Laser Technology, 2013, 46: 53-60.

[13] Theiler P W, Wegner J D, Schindler K. Keypoint-based 4-points congruent sets: automated marker-less registration of laser scans[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 96: 149-163.

[14] Kleppe A L, Tingelstad L, Egeland O. Coarse alignment for model fitting of point clouds using a curvature-based descriptor[J]. IEEE Transactions on Automation Science and Engineering, 2019, 16(2): 811-824.

[15] Rusu RB, BlodowN, BeetzM. Fast point feature histograms (FPFH) for 3D registration[C]∥2009 IEEE International Conference on Robotics and Automation, May 12-17, 2009, Kobe, Japan. New York: IEEE, 2009: 3212- 3217.

[16] StederB, Rusu RB, KonoligeK, et al. Point feature extraction on 3D range scans taking into account object boundaries[C]∥2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai, China. New York: IEEE, 2011: 2601- 2608.

[17] Yang JQ, Cao ZG, Zhang Q. Afast and robust local descriptor for 3D point cloud registration[J]. Information Sciences, 2016, 346/347: 163- 179.

[18] 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.

[19] Li W M, Song P F. A modified ICP algorithm based on dynamic adjustment factor for registration of point cloud and CAD model[J]. Pattern Recognition Letters, 2015, 65: 88-94.

[20] 陈旭, 何炳蔚. 一种基于校正点云主成分坐标系的快速全局配准算法[J]. 激光与光电子学进展, 2018, 55(6): 061003.

    Chen X, He B W. A fast global registration algorithm based on correcting point cloud principal component coordinate system[J]. Laser & Optoelectronics Progress, 2018, 55(6): 061003.

[21] 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, 2016, 38(11): 2241-2254.

[22] MavridisP, AndreadisA, Papaioannou G. Efficient sparse ICP[J]. Computer Aided Geometric Design, 2015, 35/36: 16- 26.

[23] SegalA, HaehnelD, ThrunS. Generalized-ICP[C]∥Robotics: Science and Systems V, June 28-July 1, 2009, Seattle, USA. USA: MIT Press, 2009.

[24] 伍梦琦, 李中伟, 钟凯, 等. 基于几何特征和图像特征的点云自适应拼接方法[J]. 光学学报, 2015, 35(2): 0215002.

    Wu M Q, Li Z W, Zhong K, et al. Adaptive point cloud registration method based on geometric features and photometric features[J]. Acta Optica Sinica, 2015, 35(2): 0215002.

[25] Zhao Y, Hong R C, Jiang J G. Visual summarization of image collections by fast RANSAC[J]. Neurocomputing, 2016, 172: 48-52.

[26] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.

[27] 郭琰, 张会林, 王金伟, 等. 基于改进距离阈值约束的ICP三维配准方法[J]. 中国电子科学研究院学报, 2011, 6(6): 643-647.

    Guo Y, Zhang H L, Wang J W, et al. An improved distance threshold constrainted ICP algorithm for 3D registration[J]. Journal of China Academy of Electronics and Information Technology, 2011, 6(6): 643-647.

[28] Stanford 3D scanningrepository[EB/OL]. ( 2014-08-09) [2019-06-16]. http:∥graphics.stanford.edu/data/3Dscanrep/.

[29] Robotic 3D scanrepository[EB/OL]. ( 2016-10-15)[2019-06-16]. http:∥kos.informatik.uni-osnabrueck.de/3Dscans/.

[30] 胡修祥, 张良. 结合NARF特征的改进3D-NDT多视角点云配准[J]. 信号处理, 2015, 31(12): 1675-1679.

    Hu X X, Zhang L. Improved 3D-NDT multi-viewpoint cloud registration combined with NARF features[J]. Signal Processing, 2015, 31(12): 1675-1679.

[31] Theiler PW, Wegner JD, Schindler K. Markerless point cloud registration with keypoint-based 4-points congruent sets[J]. ISPRS Annals of Photogrammetry, RemoteSensing and Spatial InformationSciences, 2013, II-5/W2: 283- 288.

[32] Fitzgibbon A W. Robust registration of 2D and 3D point sets[J]. Image and Vision Computing, 2003, 21(13/14): 1145-1153.

[33] MagnussonM. The three-dimensional normal-distributions transform: an efficient representation for registration, surface analysis, and loop detection[D]. Örebro: Örebro Universitet, 2009: 1- 5.

[34] Lei H, Jiang G, Quan L. Fast descriptors and correspondence propagation for robust global point cloud registration[J]. IEEE Transactions on Image Processing, 2017, 26(8): 3614-3623.