The　simultaneous　localization　and　mapping　(SLAM)　based　on　a　conventional　centralized　filter　reconfigures　the　entire　state　vectors　in　every　necessary　cycle　as　the　number　of　landmarks　changes,　which　is　result　in　an　exponential　growth　in　computation　quantities　and　hard　to　isolate　potential　faults.　For　that,　SLAM　system　using　distributed　particle　filter　was　presented　to　cope　with　these　problems.　In　this　paper,　the　distributed　strong　tracking　unscented　particle　filter　(DSTUPF)　is　presented　to　improve　the　idea　of　SLAM　system　based　on　distributed　particle　filter.　The　unscented　particle　filter　(UPF)　was　used　in　every　local　filter　to　increase　the　estimation　performance　and　the　configuration　of　proposed　system　was　introduced.　However,　UPF　lacks　ability　of　adaptive　adjustment　on-line.　To　deal　with　this　problem,　this　paper　proposes　an　improved　SLAM　algorithm　that　combines　the　strong　tracking　filter　(STF)　and　UPF,　STF　has　good　performance　for　adjusting　the　filter　gains　on-line,　it　satisfies　the　demand　of　algorithm　which　has　self-adapted　ability.　The　experiment　results　show　that　the　DSTUPF-SLAM　reduces　computation　quantities　compared　to　the　centralized　particle　filter　and　is　capable　of　improving　estimation　performance.　©　2014　TCCT,　CAA.