  Localization without Global Navigation Satellite Systems (GNSS) is a critical
functionality in autonomous operations of unmanned aerial vehicles (UAVs).
Vision-based localization on a known map can be an effective solution, but it
is burdened by two main problems: places have different appearance depending on
weather and season, and the perspective discrepancy between the UAV camera
image and the map make matching hard. In this work, we propose a localization
solution relying on matching of UAV camera images to georeferenced orthophotos
with a trained convolutional neural network model that is invariant to
significant seasonal appearance difference (winter-summer) between the camera
image and map. We compare the convergence speed and localization accuracy of
our solution to six reference methods. The results show major improvements with
respect to reference methods, especially under high seasonal variation. We
finally demonstrate the ability of the method to successfully localize a real
UAV, showing that the proposed method is robust to perspective changes.
