Abstract Research on unmanned aerial vehicles is growing as they are becoming less expensive and more available than before. The applications span a large number of areas and include border security, search and rescue, wildlife surveying, firefighting, precision agriculture, structure inspection, surveying and mapping, aerial photography, and recreative applications. These applications can require autonomous behavior which is only possible with a precise and robust self-localization. Until recently, the favored approach to localization was based on inertial sensors and global navigation satellite systems. However, global navigation satellite systems have multiple shortcomings related to long-distance radio communications (e.g. non-line-of-sight reception, multipath, spoofing). This motivated the development of new approaches to supplement or supplant satellite navigation. Absolute visual localization is one of the two main approaches to vision-based localization. The goal is to locate the current view of the UAV in a reference satellite map or georeferenced imagery from previous flights. Various approaches were proposed in this area and this paper review most of the literature in this field since 2015. The problematic at hand is analyzed and defined. Existing approaches are reviewed in 4 categories: template matching, feature points matching, deep learning and visual odometry.

中文翻译：

---

无人机绝对视觉定位综述

摘要 随着无人驾驶飞行器变得比以前更便宜和更容易获得，对无人驾驶飞行器的研究正在增长。应用范围广泛，包括边境安全、搜索和救援、野生动物测量、消防、精准农业、结构检查、测绘、航空摄影和娱乐应用。这些应用程序可能需要自主行为，而这只有通过精确而强大的自我定位才能实现。直到最近，最受欢迎的定位方法还是基于惯性传感器和全球导航卫星系统。然而，全球导航卫星系统具有与长距离无线电通信相关的多个缺点（例如非视距接收、多径、欺骗）。这激发了补充或取代卫星导航的新方法的开发。绝对视觉定位是基于视觉的定位的两种主要方法之一。目标是在参考卫星地图或以前飞行的地理参考图像中定位无人机的当前视图。在该领域提出了各种方法，本文回顾了自 2015 年以来该领域的大部分文献。分析和定义了手头的问题。现有方法分为 4 类：模板匹配、特征点匹配、深度学习和视觉里程计。在该领域提出了各种方法，本文回顾了自 2015 年以来该领域的大部分文献。分析和定义了手头的问题。现有方法分为 4 类：模板匹配、特征点匹配、深度学习和视觉里程计。在该领域提出了各种方法，本文回顾了自 2015 年以来该领域的大部分文献。分析和定义了手头的问题。现有方法分为 4 类：模板匹配、特征点匹配、深度学习和视觉里程计。