基于熵准则遗传算法的点云配准算法 postprint

Abstract: The iterative closest point (ICP) algorithm is usually used in the fine registration of point cloud because of its high registration accuracy, but its registration accuracy and iteration convergence depend on the initial registration position of the point cloud to be registered. This paper proposed an algorithm of spatial optimization transformation matrix combining genetic algorithm with spatial distribution entropy, in which used a new point cloud spatial position evaluation method as the objective function of genetic algorithm, used and genetic operator to guide the solution of search direction. The algorithm achieves the minimum spatial distribution entropy through the new population of iterations, after which the coarse registration of point cloud is achieved by decoding the optimal individuals. The experimental results show that the algorithm is effective and feasible, and it can overcome the problem that traditional method can’t provide a good initial position when the defects and noise exist in the point cloud. The algorithm can directly realize point cloud registration within the acceptable error range.