Abstract: Simultaneous localization and mapping(SLAM) is the main method to realize autonomous navigation of robots in unknown environments and FastSLAM algorithm is a popular solution to SLAM problem.Due to the sequential importance sampling method used in FastSLAM,a few of particles have a larger weight while the weight of most particles becomes very small throughout the iterative process,which leads to particle degradation.In order to make the particle distribution more accurate and reduce the particle degradation,a FastSLAM algorithm based on adaptive fading unscented Kalman filter (AFUKF) is proposed to improve the estimation accuracy of FastSLAM algorithm.To overcome the problem of particle degradation in FastSLAM,starting from the study of particle’s proposal distribution function,this paper uses adaptive fading unscented Kalman filter (AFUKF) instead of EKF to estimate the proposed distribution function of robot’s position to avoid the linearization error of EKF.With using the idea of adaptive fading filter,the proposal distribution is closer to the posterior position of the mobile robot and the particle set degradation is relieved.The simulation results on MATLAB platform show that the mean square error of position estimation of the proposed method is 28.7% lower than that of standard FastSLAM,i.e.the estimation accuracy is improved by 28.7%.And the proposed method achieves high estimation accuracy compared with the related algorithms in recent years.When increasing the increase of the number of particles,the estimation accuracy of each algorithm is improved,and the proposed algorithm still achieves the highest estimation accuracy.The experimental results fully show that the proposed algorithm can calculate the proposed distribution function more accurately and effectively alleviate the particle degradation problem in FastSLAM algorithm,which significantly improve the estimation accuracy of FastSLAM algorithm.

Key words: Adaptive fading unscented kalman filter, Particle degradation, Proposal distribution function, Robot, Simultaneous localization and mapping