Aiming　at　the　problem　that　the　traditional　simultaneous　localization　and　mapping　(SLAM)　is　based　on　static　scene　assumption,　which　has　poor　robustness　and　low　accuracy　of　pose　estimation　in　dynamic　scenes,　a　semantic　visual　SLAM　method　based　on　deep　learning　was　proposed.　The　semantic　segmentation　with　the　moving　consistency　check　algorithm　was　combined　in　this　method.　First,　the　image　was　segmented　by　the　Mask　R-CNN　network,　and　the　prior　semantic　information　of　the　dynamic　object　was　established.　Then,　the　feature　points　belonging　to　the　dynamic　object　were　further　removed　by　the　moving　consistency　check　algorithm.　Finally,　the　static　feature　points　were　used　for　feature　matching　and　pose　estimation.　The　experiment　of　the　system　was　conducted　based　on　the　open　data　set　of　Technical　University　of　Munich　(TUM).　Results　show　that　the　system　reduces　the　absolute　trajectory　error　and　relative　pose　error　compared　with　the　traditional　ORB-SLAM2　and　DS-SLAM　in　dynamic　scenes,　and　the　accuracy　and　robustness　of　pose　estimation　of　the　SLAM　system　are　improved.　©　2022,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.