Fast　simultaneous　localization　and　mapping　(FastSLAM)　is　an　efficient　algorithm　for　autonomous　navigation　of　mobile　vehicle.　However,　FastSLAM　must　reconfigure　the　entire　vehicle　state　equation　when　the　feature　points　change,　which　causes　an　exponential　growth　in　quantities　of　computation　and　difficulties　in　isolating　potential　faults.　In　order　to　overcome　these　limitations,　an　improved　FastSLAM,　based　on　the　distributed　structure,　is　developed　in　this　paper.　There　are　two　state　estimation　parts　designed　in　this　improved　FastSLAM.　Firstly,　a　distributed　unscented　particle　filter　is　used　to　avoid　reconfiguring　the　entire　system　equation　in　the　vehicle　state　estimation　part.　Secondly,　in　the　landmarks　estimation　part,　the　observation　model　is　designed　as　a　linear　one　to　update　the　landmarks　states　by　using　the　linear　observation　errors.　Then,　the　convergence　of　the　proposed　and　improved　FastSLAM　algorithm　is　given　in　the　sense　of　mean　square.　Finally,　the　simulation　results　show　that　the　proposed　distributed　algorithm　could　reduce　the　computational　complexity　with　high　accuracy　and　high　fault-tolerance　performance.