关键词: 点云配准, 粒子群算法, 迭代最近点算法, 裁剪迭代最近点算法, 刚性配准

Abstract: 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.

Key words: point cloud registration, particle swarm optimization algorithm, iterative closest point algorithm, trimmed iterative closest point algorithm, rigid registration