Leveraging Edge Detection and Neural Networks for Better UAV Localization

Théo Di Piazza, Enric Meinhardt-Llopis, Gabriele Facciolo, Bénédicte Bascle, Corentin Abgrall, Jean-Clément Devaux

Abstract: We propose a new method for the geolocalization of Un-maned Aerial Vehicles (UAV) in environments without Global Navigation Stallite Systems (GNSS). Current state-of-the-art methods use an offline-trained encoder to compute a vector representation (embedding) of the current UAV’s view, and compare it with the pre-computed embeddings of geo-referenced images in order to deduce the UAV’s position. Here, we show that the performance of these methods can be greatly improved by pre-processing the images by extracting their edges, which are robust to seasonal and illumination changes. Moreover, we also show that using edges improves the robustness to orientation and altitude errors. Finally, we present a confidence criterion for localization. Our findings are validated using synthetic experiments.