Abstract
With the continuous development of robotics and computer vision technology, mobile robots have been widely applied in various fields. In this process, semantic maps for robots have attracted considerable attention because they provide a comprehensive and anthropomorphic representation of the environment. On the one hand, semantic maps are a tool for robots to depict the environment, which can enhance the robot’s cognitive expression of space and build the communication bond between robots and humans. On the other hand, semantic maps contain spatial location and semantic properties of entities, which helps robots realize intelligent decision-making in human-centered indoor environments. In this paper, we review the primary approaches of semantic mapping proposed over the last few decades, and group them according to the type of information used to extract semantics. First, we give a formal definition of semantic map and describe the techniques of semantic extraction. Then, the characteristics of different solutions are comprehensively analyzed from different perspectives. Finally, the open issues and future trends regarding semantic maps are discussed in detail. We wish this review provides a comprehensive reference for researchers to drive future research in related field.
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