Indexed by:Journal paper
Document Code:10.1016/j.measurement.2021.109274
Date of Publication:2021-03-22
Journal:Measurement
Journal: SCI 
Affiliation of Author(s):Jilin University
Discipline:工学
Document Type:J
Volume:177
Page Number:1-12
ISSN No.:0263-2241
Key Words:Unstructured environmentPoint cloud registrationIterative closest pointK-D treePrincipal component a
Abstract:In this paper, an improved iterative closest point (ICP) algorithm based on the curvature feature similarity of the point cloud is proposed to improve the performance of classic ICP algorithm in an unstructured environment, such as alignment accuracy, robustness and stability. A K-D tree is introduced to segment the 3D point cloud for speeding up the search of nearest neighbor points using principal component analysis for coarse alignment based on the classical ICP algorithm. In the curvature calculation process, discrete index mapping and templates are taken for sampling to simplify the point
First-Level Discipline:机械工程
Translation or Not:no