现代信息科技2024，Vol.8Issue(7)：146-150,5.DOI:10.19850/j.cnki.2096-4706.2024.07.030
一种针对大规模场景的点云匹配算法
A Point Cloud Matching Algorithm for Large-scale Scenarios
摘要
Abstract
A new point cloud matching method is proposed to address the issues of slow speed and inconsistent matching results in traditional algorithms in large-scale point cloud matching.This method first uses the KD tree to find the point with the minimum depth in point cloud and uses it as a seed point.Then,an improved region growth segmentation algorithm improving in depth information and curvature is used to extract the upper surface area of point cloud,and the point cloud boundary is extracted in this area.Finally,the point cloud matching algorithm is validated using improved point pair features.The experimental results show that compared with traditional algorithms,the proposed method has significantly improved matching speed and consistency of matching results,and has practical application value in handling large-scale point cloud matching.关键词
大规模点云/KD树/改进的区域生长分割算法/点对特征Key words
large-scale point cloud/KD tree/improved region growth segmentation algorithm/point pair feature分类
信息技术与安全科学引用本文复制引用
刘芊伟,张朝霞,谢怡婷,张成龙..一种针对大规模场景的点云匹配算法[J].现代信息科技,2024,8(7):146-150,5.基金项目
广东省自然科学基金(2014A030313739) （2014A030313739）
