Semantic　information　can　help　the　robot　to　better　understand　unknown　environment　and　lay　the　foundation　for　more　advanced　human-computer　interaction　and　more　complicated　task.　To　enable　mobile　robot　to　build　semantic　map　in　real　time,　a　light　deep　learning　model　is　developed　for　object　detection　on　embedded　computer　Jetson　TX1.　The　inter-frame　optical　flow　information　in　the　video　stream　is　used　to　reduce　the　missing　rate　of　object　detection　algorithm,　which　is　called　motion　guided　propagation　(MGP)　algorithm.　A　real-time　depth　map　restoration　algorithm　based　on　CUDA　is　utilized　because　the　depth　map　generated　by　Kinect　has　black　hole　and　black　border.　SLAM　technology　is　employed　in　this　paper　for　robot　location,　navigation　and　mapping.　On　this　basis,　Bayesian　inference　framework　is　integrated　with　measurement　information　of　environment　and　object　detection　information　to　complete　the　building　of　semantic　map.　Experiments　show　that　the　proposed　method　can　enable　the　mobile　robot　to　build　the　semantic　map　in　real　time　in　the　real,　complicated　indoor　environment.　©　2017,　Science　Press.　All　right　reserved.