In the realm of robotics, the quest for achieving real-world autonomy, capable of executing large-scale and long-term operations, has positioned place recognition (PR) as a cornerstone technology. Despite the PR community's remarkable strides over the past two decades, garnering attention from fields like computer vision and robotics, the development of PR methods that sufficiently support real-world robotic systems remains a challenge. This paper aims to bridge this gap by highlighting the crucial role of PR within the framework of Simultaneous Localization and Mapping (SLAM) 2.0. This new phase in robotic navigation calls for scalable, adaptable, and efficient PR solutions by integrating advanced artificial intelligence (AI) technologies. For this goal, we provide a comprehensive review of the current state-of-the-art (SOTA) advancements in PR, alongside the remaining challenges, and underscore its broad applications in robotics. This paper begins with an exploration of PR's formulation and key research challenges. We extensively review literature, focusing on related methods on place representation and solutions to various PR challenges. Applications showcasing PR's potential in robotics, key PR datasets, and open-source libraries are discussed. We also emphasizes our open-source package, aimed at new development and benchmark for general PR. We conclude with a discussion on PR's future directions, accompanied by a summary of the literature covered and access to our open-source library, available to the robotics community at: https://github.com/MetaSLAM/GPRS.

翻译：在机器人学领域，实现大规模长期运行的现实世界自主性已成为核心追求，而地点识别作为基石技术备受关注。尽管过去二十年地点识别领域取得了显著进展，并吸引了计算机视觉和机器人学等领域的广泛关注，但开发能充分支撑现实世界机器人系统的方法仍面临挑战。本文旨在通过阐明地点识别在同步定位与建图2.0框架中的关键作用来弥合这一鸿沟。机器人导航的这一新阶段要求通过集成先进人工智能技术，构建可扩展、自适应且高效的地点识别解决方案。为此，我们全面综述了地点识别技术的最新进展与现存挑战，并强调其在机器人领域的广泛应用。本文首先探讨地点识别的形式化定义与核心研究挑战，随后系统梳理了关于地点表征方法及应对各类挑战的解决方案的相关文献。我们讨论了彰显地点识别潜力的机器人应用场景、关键数据集及开源工具库，并重点介绍了面向通用地点识别新开发与基准测试的开源工具包。最后，我们展望了地点识别的未来发展方向，附上文献综述总结及面向机器人社区的开源库访问链接：https://github.com/MetaSLAM/GPRS。