This　paper　proposes　a　fine　semantic　mapping　method　using　dense　segmentation　network　(DS-Net)　to　obtain　good　performance　of　semantic　mapping　fusion.　First,　the　RGB　image　and　the　depth　image　are　used　to　generate　a　dense　indoor　scene　map　via　the　state-of-the-art　dense　SLAM　(ElasticFusion).　Then,　the　DS-Net　is　constructed　based　on　DenseNet＇s　dense　connection　to　perform　precise　semantic　segmentation　on　the　input　RGB　image.　Finally,　the　long-term　correspondence　is　established　between　the　indoor　scene　map　and　the　landmarks　using　continuous　frames　both　in　the　visual　odometer　and　in　loop　detection,　and　the　final　semantic　map　is　obtained　by　fusing　the　indoor　scene　map　with　the　semantic　predictions　of　the　RGB-D　video　frames　of　multiple　angles.　Experiments　were　performed　on　the　NYUv2,　PASCAL　VOC　2012,　CIFAR10　datasets　and　our　laboratory　environments.　Results　show　that　our　method　can　reduce　the　error　in　dense　map　construction　and　obtain　good　semantic　segmentation　performance.