Simultaneous　localization　and　mapping　(SLAM)　is　an　important　technology　in　the　field　of　robotics.　Semantic　SLAM　can　provide　a　more　accurate　localization　and　satisfy　the　needs　of　complex　applications,　which　has　become　a　research　hot　spot.　In　this　paper,　we　propose　a　real-time　semantic　SLAM　system　that　uses　semantic　information　in　loop　closure　detection　and　mapping.　In　the　system,　we　use　object　detection　network　to　get　semantic　information　including　bounding　box　and　category.　In　loop　closure　detection,　we　only　use　semantic　information　to　construct　feature　structure,　and　implement　feature　comparison.　Compared　with　the　Bag　of　Visual　Words　(BoVW),　the　proposed　approach　does　not　need　to　generate　vocabulary,　holds　very　less　amount　of　memory.　Besides,　we　propose　a　fast　semantic　segmentation　method　combining　bounding　box　and　RGB-D　image　to　create　semantic　OctoMap.　Finally,　we　evaluate　our　semantic　SLAM.　Experimental　results　show　that　compared　with　BoVW,　the　proposed　loop　closure　detection　algorithm　is　about　20%　higher　in　accuracy　under　the　same　recall　rate.　The　semantic　segmentation　results　reveal　that　the　mean　pixel　accuracy　of　our　method　on　NYUv2　dataset　is　0.67.And　our　system　takes　150ms　to　infer　an　image　with　the　size　of　640480　on　MX150　GPU,i7-8550U　CPU,　which　can　be　used　in　real-time　visual　SLAM.　©　2020　ACM.