迭代最近点算法的改进策略

doi:10.3969/j.issn.1006-2475.2019.01.004

计算机与现代化

迭代最近点算法的改进策略

ZHAO Fu-qun, FANG Rong

收稿日期:2018-06-15 出版日期:2019-01-30 发布日期:2019-01-30
作者简介:赵夫群(1982-),女,山东临沂人,副教授,博士,研究方向:计算机视觉,图形图像处理,E-mail: fuqunzhao@126.com。
基金资助:
国家自然科学基金资助项目(61731015); 咸阳发展研究院服务地方经济社会发展项目(2018XFY007); 咸阳师范学院青年骨干教师培养项目(XSYGG201621)

Improved Strategy of Iterative Closest Point Algorithm

(College of Education Science, Xianyang Normal University, Xianyang 712000, China)

Received:2018-06-15 Online:2019-01-30 Published:2019-01-30

摘要/Abstract

摘要： 迭代最近点（Iterative Closest Point, ICP）算法是一种最为常见的点云配准方法，虽然配准精度高，但收敛速度慢，对含噪声、覆盖率较低点云的配准效果不佳。鉴于此，本文提出3种ICP算法的改进方法。针对含噪声的点云，采用概率ICP算法来抑制噪声点对配准结果的影响，提高配准精度；为了提高点云配准速度，采用坐标ICP算法实现点云的快速配准；针对低覆盖率点云，采用盒子ICP算法实现配准，可以大大提高配准精度和速度。通过兔子点云配准实验表明，3种改进的ICP算法在点云配准精度和速度方面都有很大程度的提高，均为有效的点云配准方法。

关键词: 点云配准, 迭代最近点, 高斯概率, 坐标轴, 盒子结构

Abstract: Iterative Closest Point (ICP) algorithm is the most common method of point cloud registration. Although its registration accuracy is high, the convergence speed is slow, and the registration effect of cloud with noise and low overlapping is not good. In view of this, three improved ICP algorithms are proposed in this paper. Aiming at the point cloud with noise, the probability ICP algorithm is used to suppress the influence of the noise points to the registration results and improve the registration accuracy. In order to improve the registration speed of the point cloud, the coordinate ICP algorithm is used to realize the rapid registration of the point cloud. Aiming at the low overlapping point clouds, the box ICP algorithm is used to improve the registration accuracy and speed. The registration experiment of rabbit point cloud shows that the three improved ICP algorithms have greatly improved the accuracy and speed of point cloud registration, and all are effective point cloud registration methods.

Key words: point cloud registration, iterative closest point, Gauss probability, coordinate axis, box structure

中图分类号:

TP391

赵夫群,方荣. 迭代最近点算法的改进策略[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2019.01.004.

(咸阳师范学院教育科学学院,陕西咸阳712000). Improved Strategy of Iterative Closest Point Algorithm[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2019.01.004.

参考文献

［1］马大贺,刘国柱. 改进的基于FPFH特征配准点云的方法［J］. 计算机与现代化, 2017(11):46-50.
［2］ SUN J H, ZHANG J, ZHANG G J. An automatic 3D point cloud registration method based on regional curvature maps［J］. Image and Vision Computing, 2016,56:49-58.
［3］ GAO Y, WANG M, TAO D C, et al. 3-D object retrieval and recognition with hypergraph analysis［J］. IEEE Transactions on Image Processing, 2012,21(9):4290-4303.
［4］ GAO Y, WANG M, JI R R. 3-D object retrieval with hausdorff distance learning［J］. IEEE Transactions on Industrial Electronics, 2014,61(4):2088-2098.
［5］韦羽棉,尚赵伟. 基于Kinect的旋转刚体三维重建方法［J］. 计算机与现代化, 2014(5):89-93,98.
［6］ ALTANTSETSEG E, MATSUYAMA K, KONNO K. Pairwise matching of 3D fragments using fast Fourier transform［J］. Visual Computer, 2014,30(6-8):929-938.
［7］税午阳,周明全,武仲科,等. 数据配准的颅骨面貌复原方法［J］. 计算机辅助设计与图形学学报, 2011,23(4):607-614.
［8］赵夫群,周明全. 颅骨点云模型的优化配准［J］. 光学精密工程, 2017,25(7):1927-1933.
［9］ 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.
［10］ZHU J, DU S, YUAN Z, et al. Robust affine iterative closest point algorithm with bidirectional distance［J］. IET Computer Vision, 2012,6(3):252-261.
［11］LI W, SONG P. 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.
［12］CHOI W S, KIM Y S, OH S Y, et al. Fast iterative closest point framework for 3D LIDAR data in intelligent vehicle［C］// Proceedings of 2012 IEEE Intelligent Vehicles Symposium. 2012:1029-1034.
［13］BAE K, LICHTI D D. A method for automated registration of unorganized point clouds［J］. ISPRS Journal of Photogrammetry and Remote Sensing, 2008,63(1):36-54.
［14］HAN J, YIN P, HE Y. Enhanced ICP for the registration of large scale 3D environment models: An experimental study［J］. Sensors, 2016,16(2):1-15.
［15］DU S Y, LIU J, ZHANG C J. Probability iterative closest point algorithm for m-D point set registration with noise［J］. Neurocomputing, 2015,157（1）:187-198.
［16］ZHAO L, SHEN X K, LONG X. Robust wrinkle-aware non-rigid registration for triangle meshes of hand with rich and dynamic details［J］. Computers & Graphics, 2012,36(5):577-583.
［17］BOUAZIZ S, TAGLIASACCHI A, PAULY M. Sparse iterative closest point［J］. Computer Graphics Forum, 2013,32(5):113-123.
［18］AGUS M, GOBBETTI E, VILLANUEVA A J, et al. SOAR: Stochastic optimization for affine global point set registration［C］// Proceedings of the 19th International Workshop on Vision, Modeling and Visualization. 2014:103-110.
［19］陶海跻,达飞鹏．一种基于法向量的点云自动配准方法［J］. 中国激光, 2013,4(8):179-184.
［20］RUSINKIEWICZ S, LEVOY M. Efficient variants of the ICP algorithm［C］// Proceedings of the 3rd International Conference on 3-D Digital Imaging and Modeling. 2001:145-152.
［21］JOST T, HUGLI H. A multi-resolution ICP with heuristic closest point search for fast and robust 3D registration of range images［C］// Proceedings of the 4th International Conference on 3-D Digital Imaging and Modeling. 2003:427-433.
［22］陈金广,郭秋梦,马丽丽,等. 用于多视点云拼接的改进ICP算法［J］. 计算机系统应用, 2018,27(1):180-184.
［23］杨小青,杨秋翔,杨剑,等. 应用改进ICP算法的点云配准［J］. 计算机工程与设计, 2015,36(9):2457-2461.

[1]	张亚文, 林文忠, 韩晓东. 基于组合特征点和主成分分析的点云配准算法[J]. 计算机与现代化, 2023, 0(09): 59-63.
[2]	梁正友, 王璐, 李轩昂, 杨锋, . 基于改进PSO-TrICP算法的点云配准[J]. 计算机与现代化, 2022, 0(05): 90-95.
[3]	梁正友, 王璐, 李轩昂, 杨锋, . 基于K-means++的多视图点云配准技术[J]. 计算机与现代化, 2022, 0(02): 97-101.
[4]	李运川, 王晓红, 陈思吉, 葛义攀, 李闯. 基于多核并行和动态阈值的点云配准算法[J]. 计算机与现代化, 2020, 0(09): 77-82.
[5]	李宇萌，段欣伯，马蓬勃. 基于点云的转子表面缺陷检测方法[J]. 计算机与现代化, 2019, 0(10): 101-.
[6]	马大贺，刘国柱. 改进的基于FPFH特征配准点云的方法[J]. 计算机与现代化, 2017, 0(11): 46-50.

迭代最近点算法的改进策略

Improved Strategy of Iterative Closest Point Algorithm

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

编辑推荐

Metrics

本文评价