Yin, P;
Jiao, J;
Zhao, S;
Xu, L;
Huang, G;
Choset, H;
Scherer, S;
(2025)
General Place Recognition Survey: Towards Real-World Autonomy.
IEEE Transactions on Robotics
10.1109/TRO.2025.3550771.
(In press).
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Abstract
In the realm of robotics, the quest for achieving realworld 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 conclude with a discussion on PR's future directions and provide a summary of the literature covered at: https://github.com/MetaSLAM/GPRS.
| Type: | Article |
|---|---|
| Title: | General Place Recognition Survey: Towards Real-World Autonomy |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TRO.2025.3550771 |
| Publisher version: | https://doi.org/10.1109/tro.2025.3550771 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Place Recognition, Multi-sensor modalities, Long-term Navigation, Datasets |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10207205 |
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