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基于改进粒子滤波器,提出了一种应用于未知环境下的移动机器人的同步定位与地图创建方法.针对传统粒子滤波器经过多次迭代后粒子退化从而需要大量粒子才能提高定位精度的问题,设计了一种基于人工鱼群算法的粒子滤波算法,该方法主要利用人工鱼群算法对预估粒子进行二次更新,从而调整了粒子的分布使其更加接近真实位姿,提高机器人的SLAM性能.经过Matlab仿真实验,证明了该方法能够准确快速地对机器人定位,并且构建的地图精度也很高.
Based on the improved particle filter, a new method for simultaneous positioning and mapping of mobile robots used in unknown environment is proposed. In order to solve the problem that traditional particle filters need large numbers of particles to degrade particles after many iterations, A particle swarm optimization algorithm based on artificial fish swarm algorithm is proposed. This method uses artificial fish swarm algorithm to update the estimated particle twice, so the particle distribution is adjusted to be closer to the true pose and the SLAM performance of the robot is improved. After Matlab simulation experiments, it is proved that the method can locate the robot accurately and quickly, and the constructed map has high precision.