In the normal fast simultaneous localization and mapping (FastSLAM) algorithm, the particle degradation
phenomena are quite obvious, which leads to the consistency of the robot pose estimation comparatively worse. Therefore,a improved FastSLAM algorithm is proposed, in which the particle weights’ covariance and every particle’s residual consistency are considered to check whether it is the time to do re-sample, and the new particles are produced by using exponential ranking selection method. Simulation results show that the improved FastSLAM algorithm can improve the consistency of the robot pose estimation obviously and keep the diversity of the particle well.