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.

翻译：航空影像及其在视觉定位中的直接应用是许多机器人学和计算机视觉任务的核心问题。虽然全球导航卫星系统（GNSS）是解决航空定位问题的标准方案，但其存在诸多局限性，如信号不稳定或解算不可靠，使得该方案并非理想选择。因此，视觉地理定位正逐渐成为一种可行的替代方案。然而，将视觉地点识别任务应用于航空影像面临着重大挑战，包括天气变化和重复性模式等。当前的VPR综述大多忽略了航空数据的具体背景。本文提出了一种专门用于评估航空影像领域VPR技术的方法，对各种方法及其性能进行了全面评估。此外，我们不仅比较了多种VPR方法，还论证了在构建地图瓦片时选择适当缩放级别和重叠率对于实现航空影像VPR算法效率最大化的重要性。相关代码已发布于GitHub仓库——https://github.com/prime-slam/aero-vloc。