基于改进PSO-TrICP算法的点云配准

针对传统迭代最近点（Iterative Closest Point,ICP）算法在初始空间位置偏差大时，容易陷入局部最优的问题，提出一种基于改进PSO-TrICP算法的点云配准方法。首先，对传统粒子群（Particle Swarm Optimization,PSO）算法进行改进，引入适应度的相似度测量准则调整粒子的更新方式，然后加入历次迭代的全局最优解的均值作为新的学习因子避免求解过程中出现“早熟”现象；其次用刚性变换参数和点云间的重叠率组成粒子，利用改进PSO算法为配准提供良好的初始相对位置；最后，通过裁剪迭代最近点（Trimmed Iterative Closest Point,TrICP）算法估计点云间的空间变换。实验结果表明，改进PSO-TrICP算法的配准精度与运行效率优于近年提出的同类配准算法，且具有较好的鲁棒性。

A Point Cloud Registration Algorithm Combining Improved PSO Algorithm and TrICP Algorithm

Aiming at the problem that the traditional iterative closest point (ICP) algorithm is easy to fall into the problem of local optimality when the initial spatial position deviation is large, a point cloud registration method combining improved PSO-TrICP algorithm is proposed. Firstly, the traditional particle swarm optimization (PSO) algorithm is improved by introducing similarity measurement criterion of fitness to adjust the updating mode of particles. Then, the mean value of the historical global optimal solution of each iteration is added as a new learning factor to avoid the phenomenon of “precocity”; Secondly, the rigid transformation parameters and the overlap rate between the point clouds are used to form the particles, and the improved PSO algorithm is used to provide a good initial relative position; Finally, the space transformation between point clouds is estimated with trimmed iterative closest point (TrICP) algorithm. Experimental results show that the improved PSO-TRICP algorithm has better registration accuracy and operation efficiency than the similar registration algorithms proposed in recent years, and has better robustness.