基于IINFO的粒子重组FastSLAM在SLAM中的研究

Research on IINFO Based Particles Recombination FastSLAM in SLAM

针对FastSLAM算法需要增加粒子数提高精度以及重采样导致的粒子多样性缺失的问题,提出一种改进INFO(向量加权平均算法)优化的粒子重组FastSLAM算法。首先,在预测粒子集时加入最新时刻的观测信息并通过INFO计算粒子适应度值,为增强INFO跳出局部最优的能力,对最优个体进行柯西变异;其次,通过改进INFO寻优能力强,收敛速度快的特性更新预测粒子集,使得在计算权重前粒子集的位姿就更接近期望值,以此提高估计精度;最后,重采样阶段将筛选后保留的粒子与剩余粒子重新组合成新的粒子,以此增加粒子多样性。仿真实验结果表明,IINFO-FastSLAM算法较FastSLAM、INFO-FastSLAM算法相比,其位姿与路标估计精度更高且鲁棒性更佳。

To solve the problem that FastSLAM algorithm needs to increase the number of particles to improve the accuracy and the lack of particle diversity caused by resampling,a particle recombination FastSLAM algorithm optimized by improved INFO(weighted mean of vectors algorithm)is proposed.First,the latest observation information is added into the prediction of particle set and the particle fitness value is calculated through INFO.In order to enhance the ability of INFO to jump out of the local optimum,Cauchy mutation is performed on the optimal individual;Secondly,the predicted particle set is updated by improving the characteristics of strong optimization ability and fast convergence speed of INFO,so that the pose of the particle set is closer to the expected value before calculating the weight,so as to improve the estimation accuracy;Finally,in the resampling stage,the particles retained after screening and the remaining particles are recombined into new particles to increase particle diversity.The simulation results show that the IINFO FastSLAM algorithm has higher accuracy and better robustness than FastSLAM and INFO FastSLAM algorithms.