  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.
