A　fast　method　for　mobile　robot　3D　SLAM(simultaneous　localization　and　mapping)　was　presented　to　address　the　problem　of　3D　modeling　in　complex　indoor　environment.　Environment　texture　and　3D　data　were　captured　by　RGB-D　camera.　According　to　the　camera　calibration　model　and　the　image　feature　extraction　and　matching　procedure,　the　association　between　two　3D　point　clouds　was　established.　On　the　basis　of　the　RANSAC(random　sample　consensus)　algorithm,　the　correspondence-based　iterative　closest　point　arithmetic　model　was　solved　to　realize　the　robot＇s　precise　localization　effectively.　With　the　keyframe-to-frame　selection　mechanism,　the　3D　grid　map　method　and　the　unique　normal　characteristic　of　a　spatial　point　were　used　for　maintaining　and　updating　the　global　map.　Experimental　results　demonstrate　the　feasibility　and　effectiveness　of　the　proposed　algorithm　in　the　indoor　environment.