一种新的点云拼接算法

迭代最近点(ICP)算法广泛运用于三维点云数据的多视拼接,其精度和迭代收敛性严重依赖于待拼接数据的初始拼接位置,这就决定ICP只能是一个性能优越的精确拼接算法。粗拼接算法旨在为ICP提供一个良好的初始拼接位置。基于信息论中熵的概念,分析了点云的空间分布规律与所处位置的关系,在此基础上提出了一种新的粗拼接算法—迭代最小空间分布熵法。实验表明,该算法有效可行,可以提供很好的初始拼接位置,在误差允许范围内,该算法可以直接实现点云拼接。

A Novel Algorithm for Registration of Point Clouds

Iterative closest point (ICP) algorithm is widely used in multi-view fine registration of 3D point clouds, while its accuracy and convergence to global optimization depend on initial registration position. It fails when a great difference exists to initial position of the waited registered point clouds. Coarse registration aims to provide a good initial registration position for ICP. A new coarse registration algorithm—iterative least space distribution entropy is proposed based on the space distribution of point clouds, and the concept of entropy is used for describing this distribution law according to information theory. Experiments show that the proposed algorithm can offer a good initial registration position for ICP and it owns a high efficiency and can realize registration without using ICP under precision permission.