Open AccessPoint Cloud Registration Method for Pipeline Workpieces Based on RANSAC and Improved ICP Algorithms
Author(s) -
SheSheng Xue,
Zhen Zhang,
Xiangchao Meng,
Qin Lv,
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/612/3/032190
Subject(s) - ransac , iterative closest point , point cloud , computer science , algorithm , matching (statistics) , feature (linguistics) , artificial intelligence , k d tree , point (geometry) , histogram , point set registration , computer vision , mathematics , image (mathematics) , linguistics , statistics , philosophy , geometry , tree traversal
Aiming at the registration problem of laser-scanned workpiece point cloud data, a point cloud registration method based on RANSAC algorithm and improved ICP algorithm is proposed. Firstly, feature points are selected according to the variation law of the normal vector in the original point cloud data, and the initial matching point set is obtained by establishing the histogram (FPFH) of feature points. Then a random sampling consensus (RANSAC) algorithm is applied to the initial data matching. At last, the nearest point iterative algorithm (ICP) accelerated by k-d tree is used for accurate matching, and the quaternion method is used to obtain the registration parameters. The new algorithm and PCA+ICP algorithm are tested 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.