Semantic Visual Simultaneous Localization and Mapping: A Survey
This is an incremental contribution that provides a structured overview for researchers and practitioners in computer vision and robotics working on semantic vSLAM.
This survey paper addresses the lack of comprehensive reviews on semantic visual Simultaneous Localization and Mapping (vSLAM), which combines semantic information with vSLAM to improve localization in dynamic and complex environments. It reviews the development, key issues, datasets, and future directions of semantic vSLAM systems.
Visual Simultaneous Localization and Mapping (vSLAM) has achieved great progress in the computer vision and robotics communities, and has been successfully used in many fields such as autonomous robot navigation and AR/VR. However, vSLAM cannot achieve good localization in dynamic and complex environments. Numerous publications have reported that, by combining with the semantic information with vSLAM, the semantic vSLAM systems have the capability of solving the above problems in recent years. Nevertheless, there is no comprehensive survey about semantic vSLAM. To fill the gap, this paper first reviews the development of semantic vSLAM, explicitly focusing on its strengths and differences. Secondly, we explore three main issues of semantic vSLAM: the extraction and association of semantic information, the application of semantic information, and the advantages of semantic vSLAM. Then, we collect and analyze the current state-of-the-art SLAM datasets which have been widely used in semantic vSLAM systems. Finally, we discuss future directions that will provide a blueprint for the future development of semantic vSLAM.