Vision-based Absolute Localization for Unmanned Aerial Vehicles - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2014

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 experimen- tal results are presented demonstrating the localization of a hexarotor UAV fitted with a downward looking camera during real flight tests.
Fichier principal
Vignette du fichier
IROS14_vfinal_ayol.pdf (1011.1 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-01010140 , version 1 (19-06-2014)


  • HAL Id : hal-01010140 , version 1


Aurelien Yol, Bertrand Delabarre, Amaury Dame, Jean-Emile Dartois, Eric Marchand. Vision-based Absolute Localization for Unmanned Aerial Vehicles. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS'14, Sep 2014, Chicago, United States. ⟨hal-01010140⟩
434 View
2060 Download


Gmail Mastodon Facebook X LinkedIn More