改进的概率迭代最近点配准算法

摘要

Aiming at low convergence rate and failure registration brought by noise of iterative closest point (ICP) algorithm, a point cloud registration algorithm based on expectation maximum estimation is proposed in the paper, which is named improved probability iterative closest point (PICP) algorithm. Firstly, a point-to-point correspondence is built between two point cloud sets, and thus the registration accuracy is improved greatly. Then, Gaussian model is introduced into ICP algorithm, and the singular value decomposition method is used to calculate the rigid transformation. In the process of rigid transformation, the dynamic iteration coefficient is introduced to search the closest point rapidly in order to decrease iteration number and convergence rate without affecting the registration accuracy and convergence trend, and the accurate registration of two point cloud sets with noise is completed finally. The experimental results show that the improved PICP algorithm proposed is an accurate and fast algorithm which can effectively avoid noise and external interference.%针对迭代最近点算法中存在的收敛速度较慢和噪声引起的配准效果不佳的问题,提出一种基于期望最大化估计的噪声点云配准算法,即改进的概率迭代最近点算法.首先,建立两个点云集合之间的一一对应关系,以提高算法的配准精度;然后,将高斯模型引入到ICP算法中,采用奇异值分解的方法解决刚体变换问题,并在刚体变换过程中加入动态迭代系数,在不影响配准精度和迭代方向的情况下,在下次迭代中更快速地寻找到最近点,以此减少迭代次数、提高收敛速度,实现两个带有噪声点云的精确配准.实验表明,该算法是一种精度高、速度快的点云配准算法,能有效地避免噪声和外点的干扰.