Scene　parsing　and　robotic　manipulation　using　visual　sensing　are　of　significant　importance　for　mobile　and　robotics　related　applications.　And　increasing　efforts　are　being　made　to　associate　semantic　concepts　to　geometric　entities　in　robot＇s　surroundings.　However,　they　are　not　well　suitable　for　resource-constrained　and　mobile　platforms.　Toward　an　efficient　semantic　mapping　solution,　we　propose　a　specifically　light-weight　segmentation　network　for　building　a　dense　3D　semantic　map　in　real-time.　The　propose　architecture　is　based　on　depth-wise　convolution　and　channel　shuffle　to　reduce　computation　cost.　An　improved　Atrous　Spatial　Pyramid　Pooling　(ASPP)　and　encoder-decoder　structures　are　presented　and　multi-scale　features　are　merged　for　better　prediction.　Experiments　are　compared　with　other　methods　to　prove　the　effectiveness　and　superiority.　©　2019　Published　under　licence　by　IOP　Publishing　Ltd.