A review on absolute visual localization for UAV

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. (c) 2020 Published by Elsevier B.V.

Automated summary

Research on unmanned aerial vehicles is increasing due to their affordability and availability. Absolute visual localization has emerged as a main approach for vision-based localization, which aims to locate the UAV in a reference map or imagery. Existing approaches in this field include template matching, feature points matching, deep learning, and visual odometry.