Lab meeting Nov.7, (ChihChung) MAV Urban Localization from Google Street View Data (IROS2013)

Authors: Andr´as L. Majdik, Yves Albers-Schoenberg, Davide Scaramuzza

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.

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