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.
翻译：视觉同步本地化和绘图系统(VSLAM)在计算机视觉和机器人界取得了巨大进步,并在自主机器人导航和AR/VR等许多领域得到成功使用。然而,VSLAM无法在动态和复杂环境中实现良好的本地化。许多出版物报告说,通过与VSLAM的语义信息相结合,语义学和SLAM系统近年来有能力解决上述问题。然而,没有关于语义学和机器人界的全面调查。为填补空白,本文件首先回顾了语义学VSLAM的发展,明确侧重于其优势和差异。第二,我们探讨了语义学和语义学信息的提取和关联、语义学信息的应用以及语义学和语义学的优点等三个主要问题。然后,我们收集和分析了目前在语义学和VSLAM系统中广泛使用的目前最先进的SLM数据集。最后,我们讨论了未来方向,这将为语义学VSLAM的未来发展提供蓝图。
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