基于粒子群优化的移动机器人SLAM方法

Mobile Robot SLAM Method Based on Particle Swarm Optimization

针对移动机器人的粒子滤波SLAM(同时定位与建图)方法中需要大量粒子来提高精度的问题,将粒子群优化思想引入到FastSLAM中,提出了一种基于粒子群优化的同时定位与建图方法.通过粒子群优化方法对FastSLAM中预估粒子进行更新,调整粒子的提议分布,使得预测采样粒子集中于机器人的真实位姿附近.该方法能有效提高SALM精度,并减少所使用的粒子数以及计算的时间复杂度.仿真实验结果表明该方法有效、可行.

A large number of particles are needed to improve the precision in particle filtering SLAM （simultaneous localization and mapping） of mobile robots. To solve this problem, a SLAM method based on particle swarm optimization （PSO） is presented by introducing PSO＇s idea into the FastSLAM. Through the particle swarm optimization, the particle＇s prediction is updated, the particle＇s proposal distribution is adjusted in FastSLAM, and then the particles are concentrated around the robot＇s true pose. The method can enhance the SLAM precision effectively, and reduce the particle number and the computational time complexity. The simulation experiment results prove its effectiveness and feasibility.