OrthoTrack: Continuous 6-DoF UAV Trajectory Estimation Anchored in Public Orthophotos
For autonomous UAV operations, OrthoTrack solves the problem of drift and scale ambiguity in pose estimation using only publicly available geodata, enabling deployment without site-specific adaptation.
OrthoTrack estimates continuous 6-DoF UAV trajectories without drift or scale ambiguity by matching keyframes to public orthophotos and propagating correspondences via optical flow, achieving real-time performance and outperforming baselines by a large margin on the MovingDrone and real-world benchmarks.
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.