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MAV Urban Localization from Google Street View Data

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This approach tackles 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, the authors proposed to generate virtual views of the scene, which exploit the air-ground geometry of the system. To limit the computational complexity of the algorithm, they 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 the approach shows
that the 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.

For more info, http://rpg.ifi.uzh.ch/docs/IROS13_Maj
A. Majdik, Y. Albers-Schoenberg, D. Scaramuzza MAV Urban Localization from Google Street View Data, IROS’13, IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS’13, 2013.
More info at: http://rpg.ifi.uzh.ch


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