An　improved　fast　simultaneous　localization　and　mapping　(FastSLAM)　algorithm　based　on　the　strong　tracking　square　root　central　difference　Kalman　filter　(STSRCDKF)　with　adaptive　partial　systematic　resampling　is　proposed　in　this　paper　to　solve　the　large-scale　simultaneous　localization　and　mapping　(SLAM)　problem　for　unmanned　intelligent　vehicle.　In　the　proposed　algorithm,　STSRCDKF　is　composed　of　a　strong　tracking　filter　and　a　square　root　central　difference　Kalman　filter.　STSRCDKF　is　used　to　design　an　adaptive　adjusting　proposal　distribution　of　the　particle　filter　and　to　estimate　the　Gaussian　densities　of　the　landmarks.　Moreover,　an　adaptive　partial　systematic　resampling　operation　is　carried　out　to　reduce　the　degree　of　particle　degeneracy　and　maintain　the　diversity　of　particles.　The　performance　of　the　proposed　algorithm　is　compared　with　that　of　central　difference　FastSLAM　and　FastSLAM2.0;　the　simulation　results　based　on　the　simulator　and　two　benchmark　data　sets　verify　that　the　proposed　algorithm　has　better　adaptability　and　robustness　to　respond　with　time-varying　measurement　noise.　In　addition,　it　reduces　computational　cost　and　improves　state　estimation　accuracy　and　consistency.　Furthermore,　the　validity　of　the　proposed　algorithm　is　verified　by　the　experimental　result　in　campus　test　site　of　Beijing　University　of　Technology.