一种全局最优的多条件约束点云配准算法  

A Globally Optimal Multi-Constrained Point Cloud Registration Algorithm

作　　者：魏子怡 任小玲[1] 陈逍遥 WEI Ziyi;REN Xiaoling;CHEN Xiaoyao(College of Computer Science,Xi'an Polytechnic University,Xi'an 710048)

机构地区：[1]西安工程大学计算机科学学院,西安710048

出　　处：《计算机与数字工程》2024年第9期2594-2597,2609,共5页Computer & Digital Engineering

摘　　要：针对目前点云配准过程易陷入局部最优解而导致配准时间长、精度低等问题,提出一种基于全局最优的多条件约束点云配准算法。利用曲率相似度误差和欧氏距离作为约束条件,实现迭代最近点云精确配准。实验结果表明,论文提出的配准算法在配准效率及配准精度均有提升,均优于原ICP迭代最近点算法。In order to solve the problems of long registration time and low accuracy caused by local optimal solution,a multi constraint point cloud registration algorithm based on global optimal is proposed.The curvature similarity error and Euclidean distance are used as constraints to achieve the closest point cloud registration.The experimental results show that the proposed registration algorithm improves the efficiency and accuracy of the registration,and is superior to the original ICP iterative closest point algorithm.

关 键 词：点云配准 欧氏距离 迭代最近点算法 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]