Vision-based Absolute Localization for Unmanned Aerial Vehicles

This paper presents a method for localizing an Unmanned Aerial Vehicle (UAV) using georeferenced aerial images. Easily maneuverable and more and more affordable, UAVs have become a real center of interest. In the last few years, their utilization has significantly increased. Today, they are used for multiple tasks such as navigation, transportation or vigilance. Nevertheless, the success of these tasks could not be possible without a highly accurate localization which can, unfortunately be often laborious. Here we provide a multiple usage localization algorithm based on vision only. However, a major drawback with vision-based algorithms is the lack of robustness. Most of the approaches are sensitive to scene variations (like season or environment changes) due to the fact that they use the Sum of Squared Differences (SSD). To prevent that, we choose to use the Mutual Information (MI) which is very robust toward local and global scene variations. However, dense approaches are often related to drift disadvantages. Here, we solve this problem by using georeferenced images. The localization algorithm has been implemented and experimental results are presented demonstrating the localization of a hexarotor UAV fitted with a downward looking camera during real flight tests.