In　this　paper,　a　semantic　OctoMap　SLAM　system　is　proposed　by　combining　image　semantic　segmentation　with　SLAM　technology.　The　system　is　divided　into　two　parts.　In　the　semantic　segmentation　part,　the　improved　HRNet　combining　the　Multi-Layer　Serial　Dilated　Convolution　(MLSDC)　and　the　Multi-Layer　DUpsampling　(MLDU)　is　used　for　semantic　segmentation　of　RGB　data.　In　the　visual　SLAM　part,　the　camera　pose　estimation　and　optimization　are　carried　out　on　the　input　RGBD　data,　and　then　the　point　cloud　is　preliminarily　generated.　Next,　the　Bayesian　semantic　fusion　algorithm　is　used　to　fuse　the　semantic　segmentation　results　and　project　them　into　the　generated　point　cloud.　Then,　the　point　cloud　is　filtered　to　remove　outliers　and　noise　points.　Finally,　the　octree　map　is　constructed.　Experiments　show　that　the　improved　HRNet　algorithm　can　retain　more　detailed　features　compared　with　the　traditional　algorithm,　and　has　a　better　segmentation　effect　on　the　fuzzy　edge,　and　the　overall　accuracy　is　improved.　The　final　octree　semantic　map　has　intuitive　mapping　effect.　©　2021　IEEE