General Place Recognition Survey: Towards Real-World Autonomy
<|reference_start|>General Place Recognition Survey: Towards Real-World Autonomy: 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.<|reference_end|>
@article{yin2024general,
    title={General Place Recognition Survey: Towards Real-World Autonomy},
    author={Peng Yin, Jianhao Jiao, Shiqi Zhao, Lingyun Xu, Guoquan Huang, Howie
  Choset, Sebastian Scherer, Jianda Han},
    journal={arXiv preprint arXiv:2405.04812},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04812},
    primaryClass={cs.RO cs.CV}
}