The use of Unmanned Aerial Vehicles (UAVs) has become quite popular in a number of applications during the last few years. Such spread use is motivated by the UAV flexibility of usage and by their ability to automatically execute several tasks, mostly thanks to the availability of Global Navigation Satellite Systems (GNSS), which usually allow reliable outdoor localization of aerial vehicles. However, the extension of task automatic execution also indoors, and in other challenging working conditions for the GNSS, requires an alternative positioning system, able to compensate for the unreliability or unavailability of GNSS in those cases.

To this aim, additional sensors are usually considered. Among them, cameras are probably the most popular ones. The most common case of vision-based positioning system is given by a camera mounted on the moving platform, used to determine its ego-motion in a dead-reckoning approach, i.e. visual odometry. Despite this solution is affordable and not requiring the installation of any infrastructure, it allows to obtain absolute positioning of the camera, i.e. of the UAV, only if certain landmarks, with known position, are visible in the flying area.

Differently, this work considers the use of external cameras, installed in the flying area, to track the UAV movements. This approach is similar to the one implemented in motion capture systems as well, where a set of static cameras are used to triangulate some target positions, using calibrated cameras. Instead, this work investigates the use of vision and machine learning tools to (i) extract the UAV position from each video frame, (ii) estimate its 3D position. Estimation is performed with single and multiple cameras. Performance analysis is provided on a dataset collected at the Agripolis campus of the University of Padua.Performance analysis is provided on a dataset collected at the Agripolis campus of the University of Padua.