基于主成分特征向量的点云配准方法  

Point Cloud Registration Method Based on Principal Component Eigenvectors

作　　者：赵夫群 黄鹤 耿国华[2] ZHAO Fuqun;HUANG He;GENG Guohua(School of Information,Xi’an University of Finance and Economics,Shaanxi 710100,Xi’an,China;School of Information Science and Technology,Northwest University,Shaanxi 710127,Xi’an,China)

机构地区：[1]西安财经大学信息学院,陕西西安710100 [2]西北大学信息科学与技术学院,陕西西安710127

出　　处：《应用科学学报》2024年第6期962-976,共15页Journal of Applied Sciences

基　　金：国家自然科学基金(No.62271393);陕西省哲学社会科学研究专项(No.2023QN0101);西安财经大学“青年英才发展支持计划”资助。

摘　　要：已有点云配准算法对杂乱点云的配准精度较低,耗时较长,为此提出一种基于主成分特征向量的点云配准方法。首先,通过描述点云曲率变化情况提取点云特征点集,并利用重心法使参考点云与待配准点云的特征点集的重心重合,实现初始位姿确定,达到点云粗配准的目的;然后,在迭代最近点算法进行迭代时,利用主成分分析算法对特征点集进行主成分分析,选取前三个主成分特征向量,通过刚体变换进行对应匹配,再利用欧氏距离寻找最近点,实现点云精配准。采用公共点云和文物点云数据模型对所提的配准方法进行验证,结果表明该方法比已有方法的配准精度平均提高了约12%,配准耗时平均降低了约10%,具有良好的配准结果。表明该基于主成分特征向量的配准方法是一种有效的点云配准方法。To address the issues of low accuracy and long time consumption of the existing point cloud registration algorithms for cluttered point clouds,a point cloud registration method based on principal component eigenvectors is proposed.Firstly,feature point set is extracted by describing the curvature change of the point cloud,and the center of gravity method is applied to align the center of gravity of the reference point cloud with that of the feature point set,achieving an initial rough registration.Then,during the iterative closest point(ICP)algorithm,principal component analysis(PCA)is used to select the first three principal component feature vectors and perform corresponding matching through rigid body transformation.Lastly,the Euclidean distance is used to find the nearest points for fine registration. The proposed method was validated using both public point cloudand cultural relic point cloud. Experimental results show that the registration accuracyof the proposed method is improved by approximately 12% on average, while the registrationtime is reduced by about 10% on average. These results indicate that the proposedmethod based on principal component eigenvectors is an effective approach for point cloudregistration.

关 键 词：点云配准 曲率 迭代最近点 主成分分析 特征向量 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]