A Semantic Mapping System Based on Scene Classification for Indoor Mobile Robots

With the increasingly complex application scenarios of indoor mobile robots, traditional navigation methods based on metric maps have been unable to meet people’s needs. Mobile robots not only need to perceive the spatial geometric information of the environment, but also need to deeply and comprehensively understand the semantic information of the environment in order to perform tasks such as complex behavioral decision-making and human-computer interaction. In this paper, we propose a semantic mapping system for indoor environments based on a monocular camera and a laser. The semantic mapping system adopts the technique of scene classification to construct the scene semantics of indoor environments, in which the semantic classifier is embedded into a recurrent neural network to better learn the correlation of consecutive frames. Experimental results indicate that the proposed semantic mapping system exhibits great performance in the robustness and accuracy of semantic mapping.