Semantic Simultaneous Localization and Mapping (SLAM) is a critical area of research within robotics and computer vision, focusing on the simultaneous localization of robotic systems and associating semantic information to construct the most accurate and complete comprehensive model of the surrounding environment. Since the first foundational work in Semantic SLAM appeared more than two decades ago, this field has received increasing attention across various scientific communities. Despite its significance, the field lacks comprehensive surveys encompassing recent advances and persistent challenges. In response, this study provides a thorough examination of the state-of-the-art of Semantic SLAM techniques, with the aim of illuminating current trends and key obstacles. Beginning with an in-depth exploration of the evolution of visual SLAM, this study outlines its strengths and unique characteristics, while also critically assessing previous survey literature. Subsequently, a unified problem formulation and evaluation of the modular solution framework is proposed, which divides the problem into discrete stages, including visual localization, semantic feature extraction, mapping, data association, and loop closure optimization. Moreover, this study investigates alternative methodologies such as deep learning and the utilization of large language models, alongside a review of relevant research about contemporary SLAM datasets. Concluding with a discussion on potential future research directions, this study serves as a comprehensive resource for researchers seeking to navigate the complex landscape of Semantic SLAM.

翻译：语义同步定位与建图（SLAM）是机器人与计算机视觉领域的关键研究方向，其核心在于实现机器人系统的实时定位，并通过关联语义信息构建对周围环境最精确且完整的综合模型。自二十余年前首项语义SLAM基础性工作问世以来，该领域持续受到各科学界的广泛关注。尽管其重要性日益凸显，当前仍缺乏涵盖最新进展与持续挑战的全面综述。为此，本研究对语义SLAM技术的前沿进展进行系统梳理，旨在阐明当前发展趋势与关键瓶颈。本文首先深入探讨视觉SLAM的技术演进脉络，阐述其优势与特性，并对既有综述文献进行批判性评估。随后提出模块化解题框架的统一问题表述与评估体系，将语义SLAM问题分解为视觉定位、语义特征提取、地图构建、数据关联及闭环优化等离散阶段。此外，本研究还探讨了深度学习与大语言模型应用等替代方法，并综述了当代SLAM相关数据集的研究进展。最后通过对未来潜在研究方向的展望，本研究为探索语义SLAM复杂研究版图的研究者提供了全面的学术参考。