Abstract-We tackle the problem of globally localizing a camera-equipped micro aerial vehicle flying within urban environments for which a Google Street View image database exists. To avoid the caveats of current image-search algorithms in case of severe viewpoint changes between the query and the database images, we propose to generate virtual views of the scene, which exploit the air-ground geometry of the system. To limit the computational complexity of the algorithm, we rely on a histogram-voting scheme to select the best putative image correspondences. The proposed approach is tested on a 2km image dataset captured with a small quadroctopter flying in the streets of Zurich. The success of our approach shows that our new air-ground matching algorithm can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over-season variations, thus, outperforming conventional visual place-recognition approaches. Please note that this paper is accompanied by a video demonstration available on our webpage along with the dataset used in this work: rpg.ifi.uzh.ch
REF presented a similar approach for the localization of flying vehicles in the Google Street View image database, where the difference in viewpoint between the images taken from the street level and the images from flying height constitutes a challenge.
MAV urban localization from Google street view data
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
Computer Science