Abstract
In the future, the goal of service robots is to operate in human-centric indoor environments, requiring close cooperation with humans. In order to enable the robot to perform various interactive tasks, it is necessary for robots to perceive and understand environments from a human perspective. Semantic map is an augmented representation of the environment, containing both geometric information and high-level qualitative features. It can help the robot to comprehensively understand the environment and bridge the gap in human-robot interaction. In this paper, we propose a unified semantic mapping system for indoor mobile robots. This system utilizes the techniques of scene classification and object detection to construct semantic representations of indoor environments by fusing the data of a camera and a laser. In order to improve the accuracy of semantic mapping, the temporal-spatial correlation of semantics is leveraged to realize data association of semantic maps. Also, the proposed semantic mapping system is scalable and portable, which can be applied to different indoor scenarios. The proposed system was evaluated with collected datasets captured in indoor environments. Extensive experimental results indicate that the proposed semantic mapping system exhibits great performance in the robustness and accuracy of semantic mapping.