In　order　to　quickly　and　accurately　obtain　data　association　results　in　simultaneous　localization　and　mapping　(SLAM)　of　mobile　robot,　a　fast　joint　compatibility　data　association　algorithm　(DFJCBB)　for　SLAM　based　on　DBSCAN　(density-based　spatial　clustering　of　application　with　noise)　is　proposed.　Firstly,　the　local　association　strategy　is　used　to　limit　features　in　local　map.　Then,　a　density-based　clustering　method,　that　is　DBSCAN　method,　is　used　to　group　all　measurements　at　the　current　moment　and　get　a　number　of　measurement　groups　with　small　correlation,　because　the　measurements　appear　a　clear　distribution　in　most　environments.　Finally,　joint　compatibility　branch　and　bound　(JCBB)　algorithm　is　adopted　in　data　association　of　each　group　to　obtain　the　optimal　association　solution　between　each　group　of　measurements　and　local　map　features,　and　the　optimal　association　solutions　are　combined　to　obtain　the　final　association　result.　The　performance　of　the　proposed　algorithm　is　verified　by　simulation　based　on　the　simulator　and　benchmark　dataset.　The　results　show　that　the　proposed　algorithm　can　guarantee　high　association　accuracy,　reduce　the　computational　complexity　and　shorten　the　running　time.　It　is　suitable　for　solving　the　data　association　problem　of　SLAM　in　different　complex　environments.　©　2018,　Science　Press.　All　right　reserved.