计算机与数字工程2017，Vol.45Issue(3)：419-422,522,5.DOI:10.3969/j.issn.1672-9722.2017.03.002
基于概率迭代最近点的点云配准算法
Point Cloud Registration Algorithm Based on Probability Iterative Closest Point
摘要
Abstract
Aiming at the failure registration of iterative closest point (ICP) algorithm brought by noise, the paper proposes a point cloud registration algorithm with noise based on Expectation Maximum (EM) estimation, which is named probability iterative closest point (PICP) algorithm.Firstly, a point-to-point correspondence is built between two point clouds, thus the registration accuracy is improved greatly.Then, Gaussian model is introduced into ICP algorithm, the singular value decomposition (SVD) method is used to solve the problem of rigid body transformation, thus the accurate registration of two point clouds is completed.The experimental results show that PICP algorithm not only can complete point clouds registration with noise of the same object from different angles rapidly and accurately, but also can achieve complete matching and partial matching of fracture surfaces between rigid body blocks effectively.It is an accurate and fast algorithm which can effectively avoid noise and external interference.It has more extensive application scopes.关键词
点云配准/迭代最近点/高斯模型/概率/噪声Key words
point cloud registration/iterative closest point/Gaussian model/probability/noise分类
信息技术与安全科学引用本文复制引用
赵夫群,周明全,王静..基于概率迭代最近点的点云配准算法[J].计算机与数字工程,2017,45(3):419-422,522,5.基金项目
国家自然科学基金项目(编号:61373117)资助. （编号:61373117）
