Open AccessPoint Cloud Registration Method for Pipeline Workpieces Based On NDT and Improved ICP Algorithms
Author(s) -
SheSheng Xue,
Guangqing Li,
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/677/2/022131
Subject(s) - point cloud , iterative closest point , algorithm , nondestructive testing , position (finance) , point (geometry) , feature (linguistics) , transformation (genetics) , computer science , pipeline (software) , computer vision , artificial intelligence , k d tree , mathematics , geometry , medicine , linguistics , philosophy , biochemistry , chemistry , finance , tree traversal , gene , economics , radiology , programming language
A new point cloud registration method based on NDT and improved ICP algorithm is proposed to solve the problem of point cloud registration of laser scanning workpiece position and pose data on industrial pipeline. Firstly, according to the FPFH algorithm, the feature points of point clouds are extracted to reduce the number of point clouds. Then, the normal distribution transformation (NDT) is used to achieve rough registration of point clouds, so that the two point clouds have relatively good initial position and posture. Finally, based on the traditional ICP algorithm, the k-d tree is used to accelerate the search speed of the corresponding point pairs and complete the accurate registration of the point clouds. The proposed algorithm and the NDT+ICP algorithm are tested respectively, and the results show that the proposed algorithm is faster, with lower error and fewer iterations, which provides a reference for similar problems.