With　the　rapid　development　of　computer　vision　and　deep　learning,　researchers　have　begun　to　focus　on　the　semantic　characteristics　of　traditional　Simultaneous　Localization　And　Mapping　in　three-Dimensional　scenes.　The　point　cloud　map　generated　by　the　traditional　simultaneous　localization　and　mapping　method　takes　up　considerable　storage　space　and　cannot　extract　semantic　information　from　the　scene,　which　cannot　meet　the　requirements　of　intelligent　robot　navigation　and　high-level　semantic　understanding.　To　solve　this　problem,　this　paper　proposes　a　semantic　information　fusion　OctoMap　method.　First,　the　color　and　depth　images　obtained　from　RGB-D　by　ORB-SLAM2　are　used　to　locate　the　camera.　Second,　the　Convolutional　Block　Attention　Module-Pyramid　Scene　Parsing　Network　is　introduced　to　segment　the　input　RGB　image　semantically　to　improve　the　segmentation　accuracy　and　obtain　high-level　semantic　information　in　the　environment.　Then,　a　semantic　fusion　algorithm　based　on　Bayesian　fusion　is　introduced　to　fuse　multiview　semantic　information.　Finally,　the　generated　semantic　point　cloud　is　inserted　into　OctoMap,　and　its　octree　data　structure　is　used　to　compress　the　storage　space.　Experimental　results　based　on　the　ADE20K　dataset　show　that,　compared　with　Pyramid　Scene　Parsing　Network,　Convolutional　Block　Attention　Module-Pyramid　Scene　Parsing　Network　improves　Mean　Pixel　Accuracy　by　2.55%,　and　Mean　Intersection　over　Union　by　1.88%.　Experimental　results　based　on　the　TUM　dataset　show　that　the　proposed　method　greatly　reduces　storage　space　and　achieves　the　effect　of　voxels　by　voxel　dense　semantic　mapping　compared　with　point　clouds　and　a　traditional　OctoMap.