基于层次聚类的三维点云特征点检测算法  

Feature Point Detection Algorithm of 3DPoint Cloud Based on Hierarchical Clustering

作　　者：马学磊[1] 薛河儒[1] 周艳青[1] Ma Xuelei;Xue Heru;Zhou Yanqing(College of Computer and Information Engineering,Inner Mongolia Agricultural University,Huhhot 010018,Inner Mongolia,China)

机构地区：[1]内蒙古农业大学计算机与信息工程学院,内蒙古呼和浩特010018

出　　处：《应用激光》2024年第4期164-170,共7页Applied Laser

基　　金：国家自然科学基金资助项目(61461041,31960494);内蒙古自然科学基金项目(2020BS06003)。

摘　　要：针对传统方法对点云中的细节特征不能准确检测,无法反映物体的真实信息的问题,提出了一种基于层次聚类算法的点云特征点检测方法。使用最小生成树结合深度优先遍历算法,对点云中各点与其邻域点所形成三角形的法向量方向进行调整;使用法向量的高斯隐射检测出点云模型中的非特征点和候选特征点;对于候选特征点使用层次聚类算法判断其是否为特征点。试验结果表明,基于层次聚类的点云特征点检测算法可准确地检测出散乱点云数据中位于特征区域内的特征点,对细节不明显的特征点也可进行有效检测。研究方法对Sheep、Fandisk、Bunny和Dragon 4种点云模型检测的特征点数量分别为810、933、2 955、3 941个,多于其他特征点检测方法。This paper introduces a point cloud feature point detection method based on a hierarchical clustering algorithm to address the limitations of traditional methods in accurately detecting detailed features and reflecting the true object information.The minimum spanning tree and depth first search algorithm are used to adjust the direction of the normal vector of each triangle formed by each point and its neighborhood points.Non feature points and candidate feature points in the point cloud model are detected by Gaussian mapping of normal vector.For candidate feature points,hierarchical clustering algorithm is used to judge whether they are feature points.Experimental results demonstrate the effectiveness of the proposed algorithm in accurately detecting feature points within scattered point cloud data,including those with unclear details.Specifically,the method detected 810,933,2955,and 3941feature points for the Sheep,Fandisk,Bunny,and Dragon point cloud models,respectively,surpassing the performance of other feature point detection methods.

关 键 词：点云 特征检测 高斯隐射 层次聚类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]