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.　©　2016,　Chinese　Institute　of　Electronics.　All　right　reserved.