This　paper　proposes　a　fine　semantic　mapping　method　using　dense　segmentation　network　(DS-Net)　to　obtain　good　performance　of　semantic　mapping　fusion,　in　which　the　semantic　segmentation　network　(DS-Net)　is　constructed　based　on　the　idea　of　DenseNet＇s　dense　connection.　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,　semantic　segmentation　are　precisely　performed　on　the　input　RGB　image　via　DS-Net.　Finally,　the　long-term　correspondence　between　the　landmarks　and　the　indoor　scene　map　is　established　using　the　continuous　frames　both　in　the　visual　odometer　and　loop　detection,　and　the　final　fused　semantic　map　is　obtained　by　integrating　semantic　predictions　of　the　RGB-D　video　frames　of　multiple　angles　with　the　indoor　scene　map.　Experiments　were　performed　on　the　NYUv2　and　CIFAR10　datasets　and　our　laboratory　environments.　Results　show　shows　that　our　method　can　reduce　the　error　of　dense　map　construction　and　obtain　good　semantic　segmentation　performance.　©　2019　IEEE.