The　Simultaneous　localization　and　mapping　(SLAM)　problem　have　become　a　focus　of　many　researches　on　robot　navigation.　Generally　the　most　widely　used　filter　in　SLAM　problems　are　centralized　filter.　It　is　well　known　that　SLAM　based　on　conventional　centralized　filter　must　reconfigure　the　entire　state　vectors　when　the　observation　dimension　changes,　which　cause　an　exponential　growth　in　computation　quantities　and　difficulties　in　isolate　potential　faults.　In　this　paper,　we　proposed　improved　DPF　distributed　particle　filter-SLAM　in　two　aspects,　in　DPF-SLAM　one　centralized　filter　is　divided　into　several　distributed　filters　which　reduce　the　computation　quantities　efficiently　and　avoid　the　necessary　to　reconfigure　the　entire　state　vectors　in　every　step.　First,　we　improved　the　important　function　of　the　local　filters　in　distributed　particle　filter.　By　changed　a　set　constant　in　the　important　function　to　an　adaptive　value,　we　improved　the　robustness　of　the　system.　Second,　we　propose　an　information　fusion　method　that　mixed　the　innovation　method　and　the　number　of　effective　particles　method,　which　combined　the　advantages　of　these　two　methods.　The　result　of　simulations　shows　that　the　algorithms　we　proposed　improved　the　virtue　of　the　DPF-SLAM　system　in　isolate　faults　and　enabled　the　system　has　a　better　tolerance　and　robustness.　©　2013　Universitas　Ahmad　Dahlan.