  Visual Place Recognition and Visual Localization are essential components in
navigation and mapping for autonomous vehicles especially in GNSS-denied
navigation scenarios. Recent work has focused on ground or close to ground
applications such as self-driving cars or indoor-scenarios and low-altitude
drone flights. However, applications such as Urban Air Mobility require
operations in large-scale outdoor environments at medium to high altitudes. We
present a new dataset named VPAIR. The dataset was recorded on board a light
aircraft flying at an altitude of more than 300 meters above ground capturing
images with a downwardfacing camera. Each image is paired with a high
resolution reference render including dense depth information and 6-DoF
reference poses. The dataset covers a more than one hundred kilometers long
trajectory over various types of challenging landscapes, e.g. urban, farmland
and forests. Experiments on this dataset illustrate the challenges introduced
by the change in perspective to a bird's eye view such as in-plane rotations.

Computer Vision and Pattern Recognition