  The capabilities of autonomous flight with unmanned aerial vehicles (UAVs)
have significantly increased in recent times. However, basic problems such as
fast and robust geo-localization in GPS-denied environments still remain
unsolved. Existing research has primarily concentrated on improving the
accuracy of localization at the cost of long and varying computation time in
various situations, which often necessitates the use of powerful ground station
machines. In order to make image-based geo-localization online and pragmatic
for lightweight embedded systems on UAVs, we propose a framework that is
reliable in changing scenes, flexible about computing resource allocation and
adaptable to common camera placements. The framework is comprised of two
stages: offline database preparation and online inference. At the first stage,
color images and depth maps are rendered as seen from potential vehicle poses
quantized over the satellite and topography maps of anticipated flying areas. A
database is then populated with the global and local descriptors of the
rendered images. At the second stage, for each captured real-world query image,
top global matches are retrieved from the database and the vehicle pose is
further refined via local descriptor matching. We present field experiments of
image-based localization on two different UAV platforms to validate our
results.
