Fast　simultaneous　localization　and　mapping　(FastSLAM),　a　popular　algorithm　based　on　the　Rao-Blackwellized　Particle　Filter,　has　been　used　to　solve　the　large-scale　simultaneous　localization　and　mapping　(SLAM)　problem　for　autonomous　vehicle,　but　it　suffers　from　two　serious　shortcomings:　one　is　the　calculation　of　Jacobian　matrices　and　the　linear　approximations　of　the　nonlinear　vehicle　kinematics　model　and　the　nonlinear　environment　measurement　model;　the　other　is　particle　set　degeneracy　due　to　inaccurate　proposal　distribution　of　particle　filter.　Hence　an　improved　FastSLAM　algorithm　based　on　the　strong　tracking　square　root　central　difference　Kalman　filter　(STSRCDKF)　is　proposed　in　this　paper　to　overcome　these　problems.　In　the　proposed　algorithm,　STSRCDKF　is　based　on　the　combination　of　a　strong　tracking　filter　(STF)　and　a　square　root　central　difference　Kalman　filter　(SRCDKF),　STSRCDKF　is　used　to　design　an　adaptive　adjustment　proposal　distribution　of　the　particle　filter　and　to　estimate　the　Gaussian　densities　of　the　feature　landmarks.　The　performance　of　the　proposed　algorithm　is　compared　with　that　of　UFastSLAM　and　FastSLAM2.0　in　simulations　and　experimental　tests,　the　results　verify　that　the　proposed　algorithm　has　better　adaptability　and　robustness.　Furthermore,　it　reduces　computational　cost　and　improves　state　estimation　accuracy　and　consistency.