The　Simultaneous　Localization　and　Mapping　(SLAM)　algorithm　is　robust　enough　when　used　on　static　scenes.　However,　it　is　less　robust　when　used　in　dynamic　scenes　where　objects　move　for　an　extended　period　in　indoor　scenes.　In　order　to　address　the　mentioned　issues,　this　paper　proposes　an　indoor　dynamic　scene　localization　and　map　construction　algorithm　that　combines　environmental　semantic　with　depth　information.　Based　on　the　ORB-SLAM2　framework:　First,　we　perform　semantic　segmentation　on　keyframes　to　obtain　the　mask,　then　we　combine　the　mask　and　optical　flow　algorithm　to　estimate　the　motion　properties　of　environmental　objects　and　remove　dynamic　features.　Second,　keyframe　pose,　semantic　label,　and　depth　information　are　combined　to　identify　scene　loop.　Finally,　we　filter　the　keyframes　twice　and　construct　a　static　semantic　map.　We　conducted　experiments　in　a　dynamic　simulation　environment　and　TUM　dataset.　The　result　shows　that　the　proposed　algorithm　can　maintain　high　localization　and　loop　detection　accuracy　in　dynamic　scenes,　and　the　static　semantic　map　can　reliably　describe　the　scene,　reflecting　better　lifelong　localization　robustness.　©　2023　IEEE.