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
In REF , searching in the Google Street View database is performed to provide the UAVs with a drift-free localization.
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