Open AccessPoint Cloud Registration Method for Pipeline Workpieces Based on PCA and Improved ICP Algorithms
Author(s) -
SheSheng Xue,
Zhen Zhang,
Qin Lv,
Xiangchao Meng,
Xin Tu
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/612/3/032188
Subject(s) - iterative closest point , point cloud , computer science , principal component analysis , algorithm , feature (linguistics) , histogram , point (geometry) , artificial intelligence , matching (statistics) , laser scanning , point set registration , computer vision , pattern recognition (psychology) , mathematics , image (mathematics) , laser , philosophy , linguistics , statistics , geometry , physics , optics
Aiming at the registration problem of point cloud data obtained by laser scanning workpiece, a new automatic registration algorithm of point cloud based on PCA algorithm and improved ICP algorithm is proposed. Firstly, the feature points are selected according to the change rule of normal vectors in the original point cloud data, and the initial matching point set is obtained by establishing the histogram of feature points (FPFH); then the principal component analysis (PCA) is used to match the initial data; Finally using the k-d tree to accelerate the improved iterative method is closest point (ICP) precise matching, and using quaternion method of registration parameters are obtained. Experiments are carried out on the proposed new algorithm and PCA+ICP algorithm, and the experimental results are compared. The results show that the new algorithm can achieve registration, and improve the speed and accuracy of registration, which provide a reference for similar problems.