“863”国家高科技计划资助项目(2007AA041501)
传统的基于粒子滤波器的移动机器人同步定位与地图构建(SLAM)方法往往随着迭代次数的增加会产生粒子退化的问题，提出了一种基于人工鱼群算法与定向重采样思想的改进的粒子滤波器用于移动机器人SLAM问题。方法首先将人工鱼群算法引入到粒子滤波器中，从而使得粒子分布在重采样之前就更加接近真实情况，然后利用定向重采样的方法，使得新产生的粒子更加接近于真实的运动情况，从而提高了机器人的位置估计精度与地图创建精度。仿真实验结果证明了该方法能够得到更多的有效粒子，而且能够提高粒子的多样性，并且提高SLAM性能。
Traditional methods based on particle filter for mobile robot SLAM(simultaneous localization and mapping)always induce particles degradation. Focusing on the particles degradation of the traditional particle filter and the need of a large number of particles to improve the precision of robot location，the AFAS(artificial fishing-swarm algorithm) is introduced into the particle filter method. This method updates the particle’s prediction again basing on the AFSA which adjusts the particle distribution concentrate around the robot’s true pose and improve the accuracy of SLAM. Through the MATLAB simulation，the results show that the method can locate the robot quickly and accurately，and improve the mapping precision.
朱磊,樊继壮,赵杰,吴晓光.改进粒子滤波器的移动机器人同步定位与地图构建方法[J].重庆大学学报,2014,37(4):39-45.
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