A　real-time　dense　method　to　address　the　problem　of　mobile　robot　simultaneous　localization　and　3D　mapping　(3D　SLAM)　in　complex　indoor　environment　is　proposed.　In　this　approach,　the　environmental　data　is　captured　by　using　a　RGB-D　camera　which　is　fixed　on　the　robot.　Combining　with　the　local　texture　association,　a　hybrid　algorithm　model　is　established　to　ensure　the　pose　estimation　accuracy　and　concurrently　decrease　the　failure　rate　during　mapping　by　using　the　point　cloud　and　image　texture.　By　taking　advantage　of　the　keyframe　selection　mechanism,　a　visual-based　loop　detection　algorithm　and　tree-based　network　optimizer　(TORO)　are　used　to　achieve　a　global　consistency　map.　Experimental　results　show　the　feasibility　and　effectiveness　of　the　proposed　algorithm　in　the　indoor　environment.　©,　2015,　Northeast　University.　All　right　reserved.