Autonomous　mapping　for　mobile　robot　is　the　premise　of　completing　intelligent　behavior.　To　improve　the　intelligence　and　intuitive　user　interaction　of　robot,　maps　are　needed　to　achieve　the　semantics　beyond　geometry　and　appearance.　This　paper　studies　the　3D　semantic　map　construction　method,　which　fuses　the　pixel-level　image　semantic　segmentation　based　on　Deep　Residual　Networks(DRN)　and　Simultaneous　Localization　And　Mapping(SLAM).　Firstly,　the　combined　median　filter　algorithmis　used　to　restore　the　depth　of　the　map.　The　improved　Iterator　Closest　Point　(ICP)　algorithm　is　employed　to　estimate　camera　pose　and　loopback　detection　based　on　random　ferns　is　proposed　for　3D　scene　reconstruction.　Then,　the　optimized　DRN　is　utilized　to　achieve　more　accurate　semantic　prediction　and　segmentation.　Finally,　the　predicted　semantic　classification　labels　are　migrated　to　the　3D　model　by　Bayesian　based　incremental　transfer　strategy　to　generate　a　globally　consistent　3D　semantic　map.　Experimental　results　show　that　the　proposed　method　can　build　the　real-time　3D　semantic　map　in　the　real　and　complicated　environment.　©　2019,　Science　Press.　All　right　reserved.