Visual place recognition for aerial imagery: A survey
<|reference_start|>Visual place recognition for aerial imagery: A survey: Aerial imagery and its direct application to visual localization is an essential problem for many Robotics and Computer Vision tasks. While Global Navigation Satellite Systems (GNSS) are the standard default solution for solving the aerial localization problem, it is subject to a number of limitations, such as, signal instability or solution unreliability that make this option not so desirable. Consequently, visual geolocalization is emerging as a viable alternative. However, adapting Visual Place Recognition (VPR) task to aerial imagery presents significant challenges, including weather variations and repetitive patterns. Current VPR reviews largely neglect the specific context of aerial data. This paper introduces a methodology tailored for evaluating VPR techniques specifically in the domain of aerial imagery, providing a comprehensive assessment of various methods and their performance. However, we not only compare various VPR methods, but also demonstrate the importance of selecting appropriate zoom and overlap levels when constructing map tiles to achieve maximum efficiency of VPR algorithms in the case of aerial imagery. The code is available on our GitHub repository -- https://github.com/prime-slam/aero-vloc.<|reference_end|>
@article{moskalenko2024visual,
    title={Visual place recognition for aerial imagery: A survey},
    author={Ivan Moskalenko, Anastasiia Kornilova, Gonzalo Ferrer},
    journal={Robotics and Autonomous Systems 183 (2025) 104837},
    year={2024},
    doi={10.1016/j.robot.2024.104837},
    archivePrefix={arXiv},
    eprint={2406.00885},
    primaryClass={cs.CV cs.RO}
}