Leveraging edge detection and neural networks for better UAV localization
<|reference_start|>Leveraging edge detection and neural networks for better UAV localization: We propose a novel method for geolocalizing Unmanned Aerial Vehicles (UAVs) in environments lacking Global Navigation Satellite Systems (GNSS). Current state-of-the-art techniques employ an offline-trained encoder to generate a vector representation (embedding) of the UAV's current view, which is then compared with pre-computed embeddings of geo-referenced images to determine the UAV's position. Here, we demonstrate that the performance of these methods can be significantly enhanced by preprocessing the images to extract their edges, which exhibit robustness to seasonal and illumination variations. Furthermore, we establish that utilizing edges enhances resilience to orientation and altitude inaccuracies. Additionally, we introduce a confidence criterion for localization. Our findings are substantiated through synthetic experiments.<|reference_end|>
@article{di piazza2024leveraging,
    title={Leveraging edge detection and neural networks for better UAV
  localization},
    author={Theo Di Piazza, Enric Meinhardt-Llopis, Gabriele Facciolo, Benedicte
  Bascle, Corentin Abgrall, Jean-Clement Devaux},
    journal={arXiv preprint arXiv:2404.06207},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06207},
    primaryClass={cs.CV}
}