计算机工程与科学2017，Vol.39Issue(10)：1877-1883,7.DOI:10.3969/j.issn.1007-130X.2017.10.015
一种改进的稀疏迭代最近点算法
An improved sparse iterative closest point algorithm
摘要
Abstract
The sparse iterative closest point algorithm for point cloud with noise points is sensitive to the outliers contained in the target point cloud,and is inefficient.To solve the problems,we find the corresponding point-pairs based on neighborhood information to improve the sparse iterative closest point algorithm.The improved sparse iterative closest point algorithm firstly uses the improved registration based on the PCA to adjust the position of the two point clouds,and then finds the corresponding point-pairs based on neighborhood information.Finally we use the alternating direction method of multipliers (ADMM) to get the optimal transformational matrix for corresponding point-pairs.Experiments on Stanford rabbit and potted model show that the improved algorithm can handle the outliers contained in the target point cloud,and the algorithm speed can be increased by 30％.关键词
点云配准/邻域信息/稀疏迭代最近点算法Key words
registration of point cloud/neighborhood information/sparse iterative closest point algorithm分类
信息技术与安全科学引用本文复制引用
周游,耿楠,张志毅..一种改进的稀疏迭代最近点算法[J].计算机工程与科学,2017,39(10):1877-1883,7.基金项目
国家高技术研究发展(863)计划(2013AA102304) （863）
基本科技创新一般项目(QN2013056) （QN2013056）
