Intelligent　vehicle　plays　an　important　role　in　intelligent　transportation　systems.　Developing　a　high-performance　SLAM　algorithm　can　provide　the　key　technology　for　intelligent　vehicle　achieving　autonomous　navigation　in　an　unknown　environment.　In　simple　terms,　SLAM　problem　includes　two　issues　of　state　estimation　and　data　association.　Hence,　a　SLAM　algorithm　based　on　the　square　root　central　difference　particle　filter　(SRCDPF)　and　per-particle　ICNN-ML　(individual　compatibility　nearest　neighbor-　maximum　likelihood)　data　association　method　is　proposed　in　this　paper　to　solve　the　SLAM　problem.　In　the　proposed　algorithm,　the　per-particle　ICNN-ML　data　association　method　provides　the　key　information　for　the　state　estimation.　The　state　of　each　feature　in　the　map　is　estimated　by　using　central　difference　Kalman　filter　(CDKF).　The　square　root　central　difference　particle　filter　(SRCDPF)　is　used　to　compute　the　mean　and　covariance　of　the　vehicle　state.　The　simulations　result　in　a　large-scale　environment　which　is　designed　to　emulate　a　real　campus　scene　and　the　experimental　result　with　standard　dataset　have　verified　the　feasibility　and　effectiveness　of　the　proposed　SLAM　algorithm.