Oussema Dhaouadi, Shao Jie Hu-Chen, Jakob Gollreiter, Samir Jusufi, Nikolas Rieger, Caroline Wendlandt, Christian Wender, Jingfeng Mao, Guangming Wang, Zuria Bauer, Marc Pollefeys, Daniel Cremers, Olaf Wysocki · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.26729 · cited by 0 works (OpenAlex) · open-access PDF
La localisation visuelle aérienne est essentielle pour la navigation autonome des drones dans les environnements urbains où les signaux GNSS sont souvent dégradés. Les modèles urbains légèrement texturés de niveau de détail (LoD) fournissent une alternative géométrique aux actifs texturés. Les méthodes existantes basées sur la silhouette reposent sur l'alignement image-espace, ce qui peut laisser les poses différentes de la caméra indistinguibles et favoriser les poses incorrectes lorsque les erreurs de segmentation déforment les contours du bâtiment. Nous présentons PlanarCityLoc, qui va au-delà de la superposition de la silhouette en tirant parti de la géométrie plane des façades et des toits du bâtiment. Après l'
Machine translation (Meta M2M100); the English original follows.
Aerial visual localization is crucial for autonomous drone navigation in urban environments where GNSS signals are often degraded. Lightweight textureless Level-of-Detail (LoD) city models provide a geometric map alternative to textured assets. Existing silhouette-based methods rely on image-space alignment, which can leave different camera poses indistinguishable and favor incorrect poses when segmentation errors distort the building outlines. We present PlanarCityLoc, which moves beyond silhouette overlap by leveraging the planar geometry of building facades and roofs. Following geometry-aware initialization, we match image-side planar patches to LoD model planes and use the resulting 3D-to-2D correspondences to refine pose with Perspective-n-Point (PnP). The framework uses off-the-shelf geometry and segmentation models without dataset-specific training or fine-tuning. Experiments on UAVD4L-LoDv2 and Swiss-EPFLv2 achieve state-of-the-art localization recall among the evaluated off-the-shelf LoD-based methods. By grounding image-space alignment in 3D planes, PlanarCityLoc enables more accurate aerial localization with lightweight, textureless city maps.
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Topics: Silhouette, Computer vision, Artificial intelligence, Segmentation, Computer science, Drone
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