北京测绘2025，Vol.39Issue(2)：153-157,5.DOI:10.19580/j.cnki.1007-3000.2025.02.005
基于ISS特征点和改进的ICP点云配准方法
Point cloud registration method based on ISS feature points and improved ICP
摘要
Abstract
This paper addressed the strong initial position dependence and slow iterative speed of the iterative closest point(ICP)algorithm by proposing a point cloud registration method that integrated intrinsic shape signature(ISS)key points with improved ICP.The paper utilized open-source Stanford point cloud data and scene point clouds as data sources.Key features were extracted via the ISS algorithm,and initial transformation matrices were calculated by using the fast point feature histograms to achieve preliminary registration.This ensured a good initial pose for the two point clouds.Finally,the registration was refined by using an ICP algorithm based on neighborhood curvature optimization.The experimental results show that this method significantly enhances registration accuracy and efficiency compared to the traditional ICP algorithm and sample consensus initial alignment(SAC-IA)+ICP algorithm.关键词
点云配准/内部形态描述子/迭代最近点算法/邻域曲率Key words
point cloud registration/intrinsic shape signature/iterative closest point algorithm/neighborhood curvature分类
天文与地球科学引用本文复制引用
赵永卿..基于ISS特征点和改进的ICP点云配准方法[J].北京测绘,2025,39(2):153-157,5.基金项目
科技部创新工作方法专项(2020IM020500) （2020IM020500）
