It　is　well　known　that　the　traditional　visual　SLAM　systems　have　performed　well　in　a　relatively　ideal　environment,　but　for　the　dynamic　environment,　there　are　some　problems　in　the　instance　segmentation　algorithm　such　as　incomplete　segmentation　or　wrong　segmentation　which　will　result　in　outliers.　Aiming　at　this　problem,　this　paper　proposed　a　dynamic　SLAM　system　using　Histogram-based　Outlier　Score　(HBOS)　to　improve　the　reprojection　residual　constraint　to　increase　the　robustness　in　removing　outliers.　In　this　method,　HBOS　method　is　used　to　process　the　reprojection　residual,　and　the　feature　association　data　obtained　after　the　segmentation　mask　processing　is　further　filtered　to　improve　the　robustness.　This　method　can　combine　the　semantic　segmentation　network　with　traditional　geometric　methods　to　solve　the　data　association　problem　caused　by　the　wrong　information　generated　from　the　dynamic　objects.　To　verify　the　effectiveness　of　the　proposed　method,　the　experiment　is　carried　out　on　the　public　TUM　data　set　and　the　results　show　that　the　proposed　method　improves　the　accuracy　of　SLAM　positioning　in　the　dynamic　environment.　©　2021　IEEE