  We propose and demonstrate a fast, robust method for using satellite images
to localize an Unmanned Aerial Vehicle (UAV). Previous work using satellite
images has large storage and computation costs and is unable to run in real
time. In this work, we collect Google Earth (GE) images for a desired flight
path offline and an autoencoder is trained to compress these images to a
low-dimensional vector representation while retaining the key features. This
trained autoencoder is used to compress a real UAV image, which is then
compared to the precollected, nearby, autoencoded GE images using an
inner-product kernel. This results in a distribution of weights over the
corresponding GE image poses and is used to generate a single localization and
associated covariance to represent uncertainty. Our localization is computed in
1% of the time of the current standard and is able to achieve a comparable RMSE
of less than 3m in our experiments, where we robustly matched UAV images from
six runs spanning the lighting conditions of a single day to the same map of
satellite images.
