Abstract:

Sampling process often causes particle degradation in fast simultaneous localization and mapping （FastSLAM）. From the point view of the proposal distribution function, a method named the FastSLAM based on adaptive fading extended Kalman filter is proposed to improve the performance of the algorithm and increase estimation accuracy. It uses the adaptive fading extended Kalman filter (AFEKF) to compute proposal distribution based on the basic framework of FastSLAM, then this proposal distribution is more close to the posterior position of the mobile robot and the degree of particle degradation is reduced. In the case of the same number of particles, the algorithm can effectively improve the accuracy of SLAM. Hence it can reduce the number of particles used in the algorithm and the complexity of the algorithm. The validity of the proposed algorithm is verified by the experimental simulation results based on the simulator and the standard data set.