Semantic Visual Simultaneous Localization and Mapping: A Survey

Visual Simultaneous Localization and Mapping (vSLAM) is a cornerstone technology in computer vision and robotics, underpinning applications such as autonomous vehicles and robot navigation. While traditional vSLAM systems have shown significant progress in indoor or outdoor environments, their performance often degrades in complex scenes, limiting their adaptability and robustness. Semantic vSLAM, which integrates high-level semantic information into vSLAM systems, has emerged as a promising solution to address these limitations by enabling a richer understanding of the environment. In this paper, we provide a comprehensive review of semantic vSLAM, offering a critical analysis of its evolution, methods, and challenges. We begin by revisiting the development of traditional vSLAM, emphasizing its limitations and the motivation for incorporating semantic information. Subsequently, we delve into the core modules of semantic vSLAM, including semantic extraction, object association, semantic loop closing, back-end optimization, and semantic mapping. Then, we present a performance comparison of semantic vSLAM systems under two different datasets, indoor and outdoor, respectively. Furthermore, we also provide a comparative analysis of widely used SLAM datasets to provide guidance for performance testing and validation. To further enrich the discussion, we identify unresolved challenges in semantic vSLAM, such as long-term semantic perception and association, open and unstructured environments. We propose future research directions, including balancing computational resources and quantifying system risk, large model-based navigation and mapping, and embodied AI SLAM. By providing key insights and forward-looking perspectives, this work aims to stimulate future research and improve the capabilities of semantic vSLAM in real-world applications.