Combination of the ICP and the PSO for 3D-SLAM

Abstract

There are many methods for 3D simultaneous localization and mapping(SLAM) such like ORB-SLAM, LSD-SLAM and so on when we use camera as sensor. However for laser range finder, there are few algorithms for SLAM, especially for 3DSLAM. Besides, the accuracy and robustness of 3D SLAM is still not enough by using conventional methods for laser range finder. A very famous algorithm for using laser range finder and grid map, Iterative Closest Point (ICP), has been usually used in 3DSLAM. In our previous research, a method of using the Particle Swarm Optimization(PSO) algorithm is proposed to increase the accuracy of 3D-SLAM. And the proposed method that using PSO algorithm in grid map makes a better performance than using the ICP algorithm in our previous research. However, we find a way to combine the ICP algorithm with the PSO algorithm for the 3D-SLAM by using 3D laser range finder and grid map instead of comparing them. By combining these two algorithms, we can reduce the computation consumption and improve the performance. In this paper we show and analyze the result of experiments of SLAM.