Under　the　structure　of　robot　technology　middle　ware(RTM),　this　paper　presents　a　distributed　method　for　mobile　robot　simultaneous　localization　and　mapping(SLAM)　to　address　the　problem　of　3D　modeling　in　complex　indoor　environment.　We　integrate　the　image　feature　and　depth　information　to　establish　the　correspondence-based　iterative　closest　point　(ICP)　algorithm　for　localizing　the　robot　precisely.　With　the　introduction　of　keyframe　selection　mechanism,　a　vision-based　loop　closure　detect　algorithm　and　tree-based　network　optimizer(TORO)　are　used　to　efficiently　achieve　globally　consistent　and　accuracy　maps　during　the　map　building.　Experimental　results　verify　the　feasibility　and　effectiveness　of　the　proposed　algorithm　in　the　indoor　environment.