VPAIR -- Aerial Visual Place Recognition and Localization in Large-scale Outdoor Environments
This addresses the need for navigation in large-scale outdoor environments for applications like Urban Air Mobility, but it is incremental as it primarily provides a new dataset without major methodological advances.
The authors tackled the problem of visual place recognition and localization for aerial vehicles at medium to high altitudes by introducing a new dataset called VPAIR, which includes images from over 300 meters altitude covering over 100 kilometers of diverse landscapes, and experiments showed challenges like in-plane rotations.
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.