To　solve　the　problem　of　decreased　positioning　accuracy　and　poor　robustness　of　simultaneous　localization　and　mapping　(SLAM)　systems　based　on　static　environment　assumptions　when　processing　dynamic　objects,　a　semantic　vision　SLAM　system　for　indoor　dynamic　environments　was　proposed,　which　was　based　on　ORB_SLAM2,　and　added　with　a　dynamic　target　detection　thread.　While　extracting　ORB　features　from　the　input　image,　YOLOv5s　network　was　used　for　dynamic　target　detection,　combining　optical　flow　method　and　epipolar　geometric　constraints　to　screen　dynamic　feature　points,　and　finally　static　feature　points　were　used　for　pose　estimation.　Using　the　data　set　of　Technical　University　of　Munich　(TUM)　to　compare　with　ORB_SLAM2,　results　show　that　the　system　significantly　reduces　the　trajectory　error.　Compared　with　the　system　in　the　dynamic　environment　such　as　DS-SLAM　and　DynaSLAM,　this　system　can　effectively　balance　the　accuracy,　robustness　and　rapidity　of　the　pose　estimation　of　the　semantic　visual　SLAM　system.　©　2023　Beijing　University　of　Technology.　All　rights　reserved.