Planarcityloc improves drone navigation using 3d building planes
PlanarCityLoc: Breaking Silhouette Ambiguity in LoD Aerial Localization via 3D Planes
Computer Vision and Pattern Recognition
Summary
Locating drones accurately over cities is hard because GPS signals often don’t work well there. The authors designed PlanarCityLoc, which helps drones find their exact position by matching flat surfaces on buildings seen in drone images to 3D building shapes in simple city maps. This method goes beyond just matching building outlines, which can be ambiguous or error-prone. It uses 3D geometry to better figure out where the drone is, without needing special training on specific city data.
What this means in practice
- •For drone navigation engineers: Improve drone self-positioning accuracy in urban areas using lightweight 3D city models without relying on GPS.
- •For urban mapping teams: Enhance aerial map alignment by leveraging planar building surfaces for better geometric matches with city models.
Authors
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
Abstract
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.