Abstract: A particle swarm optimized Simultaneous Localization And Mapping(SLAM) approach is presented, which integrates Particle Swarm Optimization(PSO) into the SLAM. Through the PSO, the prediction of particles is updated, and the particle’s proposal distribution is adjusted, which makes the particles concentrate into the robot’s true pose. Because of the optimization of the particle set, the impoverishment of particle is overcomed effectively. At the same time, the particle number and the computational complexity are reduced. This method is proved correct and feasible in simulation experiments.

Key words: particle filtering, Particle Swarm Optimization(PSO), Simultaneous Localization And Mapping(SLAM)

摘要： 提出一种粒子群优化的同时定位与建图方法，该方法将粒子群优化思想引入到机器人同时定位与建图算法中。通过粒子群优化方法对预估粒子进行更新，调整粒子的提议分布，从而使得采样粒子集中于机器人的真实位置附近。通过对粒子集的优化，有效地克服粒子贫乏问题，并且减少所使用的粒子数以及计算的时间复杂度。经过仿真实验，验证该方法的正确性和可行性。

关键词: 粒子滤波, 粒子群优化, 同时定位与建图

CLC Number: 

• TP273