基于法向量权重改进的ICP算法

doi:10.3969/j.issn.1000-1158.2023.06.03

Abstract
Figure/Table
References (20)
Related Citation (15)

Download: PDF (498 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Aiming at the problem that the accuracy and speed of point cloud registration in the process of 3D reconstruction are not ideal, an iterative nearest point (ICP) algorithm based on normal vector weight improvement is proposed. By projecting the normal vector of the point cloud onto the Gaussian sphere, the distribution of normal vectors in different directions is counted, the corresponding weight is assigned by combining the geometric structure information of the object, and the normal vector weight combined with the error measurement method from point to plane is used to calculate the optimal rigid body transformation matrix. Experimental results show that taking spherical point cloud data as an example, compared with the iterative closest point (ICP) algorithm before improvement, the registration error is reduced to about 30% without reducing the registration speed, and the algorithm is suitable for various point cloud models with significant effects.

Key words： metrology point cloud registration ICP algorithm normal vector weight

Received: 11 January 2022 Published: 25 June 2023

PACS:

TB92

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	ZHU Yu-mei
	XING Ming-yi
	CAI Jing

Cite this article:

ZHU Yu-mei,XING Ming-yi,CAI Jing. Improved ICP Algorithm Based on Normal Vector Weight[J]. Acta Metrologica Sinica, 2023, 44(6): 852-857.

URL:

http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2023.06.03 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2023/V44/I6/852

［4］	Tamaki T, Abe M, Raytchev B. Softassign and EM-ICP on GPU ［C］// 2010 First International Conference on Networking and Computing. IEEE. 2010.
［6］	刘哲, 周天, 彭东东, 等. 一种改进的基于PCA的ICP点云配准算法研究［J］. 黑龙江大学自然科学学报, 2019, 36(4): 473-478,505.
［7］	Granger S, Pennec X. Multi-scale EM-ICP: a fast and robust approach for surface registration［C］//European Conference on Computer Vision. 2002.
［8］	Chen Y, Medioni G. Object modelling by registration of multiple range images, Image Vis［J］. Image and Vision Computing, 1992, 10(3): 145-155.
［12］	戴静兰, 陈志杨, 叶修梓. ICP 算法在点云配准中的应用［J］. 中国图象图形学报, 2007, 129(3): 517-521.
［13］	Jiang J, Cheng J, Chen X. Registration for 3-D point cloud using angular-invariant feature［J］. Neurocomputing, 2009, 72(16): 3839-3844.
	Wu M Q, Li Z W, Zhong K. Adaptive splicing method of point cloud based on geometric features and image features［J］. Acta Optica Sinica, 2015, 35(2): 229-236.
［15］	Meinzer H, Fangerau M, Schmidt M. Convergent iterative closest-point algorithm to accomodate anisotropic and inhomogenous localization error［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(8) : 1520-1532.
［18］	张玉存, 李亚彬, 付献斌. 基于曲率约束的点云分割去噪方法［J］. 计量学报, 2020, 41(10): 1218-1225.
［19］	张玉燕, 渠文涵, 郭全丽,等. 基于3D点云平均曲率估算的损伤参数计算［J］. 计量学报, 2018, 39(5): 609-614.
［20］	孔德明, 张娜,王书涛,等. 基于多视角区域生长的复杂点云模型分割［J］. 计量学报, 2021, 42(6): 704-709.
	Kong D M, Zhang N, Wang S T, et al. Complicated Point Cloud Model Segmentation Based on Multi-view Region Growing［J］. Acta Metrologica Sinica, 2021, 42(6): 704-709.
［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.
	Liu Z, Zhou T, Peng D D, et al. Research on an Improved PCA-based ICP Point Cloud Registration Algorithm［J］. Journal of Natural Sciences of Heilongjiang University, 2019, 36(4): 473-478, 505.
	Zhu Y M, Jiang H Z. Point Cloud Registration Algorithm Based on DNSS Combined with Point-to-Plane ICP［J］. Measurement Technology, 2020, 40(6): 21-25.
［14］	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): 229-236.
	Yang Q X, Wang C Y, Yang J,et al. Improved ICP algorithm based on angle of law arrow［J］. Computer Engineering and Design, 2016, 37(8): 2082-2086.
	Zhang Y Y, Qü W H, Guo Q L, et al. Damage Parameter Calculation Based on 3D Point Cloud Mean Curvature Estimation［J］. Acta Metrologica Sinica. 2018, 39(5): 609-614.
［2］	Rusinkiewicz S, Levoy M. Effcient variants of the ICP algorithm［C］// International Conference on 3-D Digital Imaging and Modeling. Quebec City, Canada,2001.
［5］	Yang J, Li H, Jia Y. GO-ICP: Solving 3D Registration Effciently and Globally Optimally ［C］//Proceedings of the 2013 IEEE International Conference on Computer Vision. IEEE. 2013.
［10］	Makovetskii A , Voronin S , Kober V , et al. Affine Registration of Point Clouds Based on Point-to-plane Approach［J］. Procedia Engineering, 2017, 201:322-330.
	Dai J L, Chen Z Y, Ye X Z. Application of ICP algorithm in point cloud registration［J］. Chinese Journal of Image and Graphics, 2007, 129(3): 517-521.
［17］	杨小青, 杨秋翔, 杨剑. 基于法向量改进的ICP算法［J］. 计算机工程与设计, 2016, 37(1): 169-173.
	Zhang Y C, Li Y B, Fu X B. Point Cloud Segmentation De-noising Method Based on Curvature Constraint［J］. Acta Metrologica Sinica, 2020, 41(10): 1218-1225.
［3］	Yang J, Li H, Campbell D. Go-ICP: A globally optimal solution to 3D ICP point-set registration［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11): 2241-2254, 2016.
［11］	Kwok T H. DNSS: Dual-Normal-Space Sampling for 3-D ICP Registration［J］. IEEE Transactions on Automation Science and Engineering, 2019, 16(1) : 241-252.
	Yang X Q, Yang Q X, Yang J. ICP Algorithm Based on Normal Vector Improvement［J］. Computer Engineering and Design, 2016, 37(1): 169-173.
［9］	朱玉梅, 姜宏志. 基于DNSS与点到平面的ICP结合的点云配准算法［J］. 计测技术, 2020, 40(6): 21-25.
［16］	杨秋翔, 王程远, 杨剑, 等. 基于法矢夹角的改进ICP算法［J］. 计算机工程与设计, 2016, 37(8): 2082-2086.