基于自适应粒子群优化FastSLAM算法的改进

An improved FastSLAM algorithm based on self-adapt scatter particle swarm optimization

快速同时定位与建图(FastSLAM)算法中的重采样过程会带来粒子退化和粒子多样性减弱问题,为了改进算法的性能、提高估计精度,针对FastSLAM算法的特点,设计了一种改进的FastSLAM算法,将FastSLAM算法中的粒子滤波部分用自适应粒子群优化算法来代替,并且引入了粒子的筛选区间,通过改善算法初期的粒子分布情况,以及采用交叉变异操作这种自适应优化策略来对粒子种群进行调整.最后在MATLAB仿真平台针对三种算法进行了对比并验证改进后算法的优越性,实验结果表明基于自适应粒子群优化的FastSLAM算法在估计精度和计算效率方面都具有较好的性能.

The resampling process in the Fast Simultaneous Localization and Mappinng(FastSLAM)algorithm will bring about particle degradation and particle diversity reduction.An improved FastSLAM algorithm is designed based on the characteristics of the FastSLAM algorithm in order to improve the performance of the algorithm and improve the estimation accuracy.The algorithm replaces the particle filtering part of the FastSLAM algorithm with an adaptive particle swarm optimization algorithm,and introduces a particle screening interval.By improving the particle distribution at the beginning of the algorithm and using an adaptive optimization strategy such as cross mutation operation,which adjusts particle population.Finally,the MATLAB simulation platform is compared with the three algorithms and the superiority of the improved algorithm is verified.The experimental results show that the FastSLAM algorithm based on adaptive particle swarm optimization has better performance in terms of estimation accuracy and calculation efficiency.