OrthoTrack: Continuous 6-DoF UAV Trajectory Estimation Anchored in Public Orthophotos
MCML Authors
Oussema Dhaouadi
Daniel Cremers
Prof. Dr.
Director
Abstract
Oussema Dhaouadi
Daniel Cremers
Prof. Dr.
Director
Abstract
Continuous 6-DoF pose estimation is essential for autonomous UAV operations. Yet, existing visual odometry and SLAM methods accumulate drift and yield only relative, up-to-scale trajectories. Single-frame geo-localization, in turn, discards temporal continuity and remains too slow for real-time use. We present OrthoTrack, a training-free system that estimates continuous 6-DoF UAV trajectories using only publicly available orthophotos and surface models as a map prior. OrthoTrack matches keyframes against the orthophoto and lifts correspondences to metric 3D via the surface model. It then propagates these map-anchored correspondences to intermediate frames with optical flow, producing absolute, metrically scaled poses at every frame without GPS or post-hoc alignment. We also introduce the MovingDrone Dataset, a large-scale benchmark pairing photorealistic UAV sequences with dense 6-DoF ground truth and co-registered multi-modal geodata including multi-temporal orthophotos. On MovingDrone and real-world benchmarks, OrthoTrack runs in real time on a single GPU. It outperforms all baselines by a large margin, even those receiving oracle scale and alignment. By relying on publicly available geodata, OrthoTrack enables deployment to new regions without site-specific adaptation.
inproceedings DBM+26
ECCV 2026
19th European Conference on Computer Vision. Malmö, Sweden, Sep 08-12, 2026. To be published. Preprint available.
Authors
O. Dhaouadi • Z. Bauer • J. M. Meier • O. Wysocki • M. Pollefeys • D. CremersLinks
arXiv URLResearch Area
BibTeXKey: DBM+26