用公开正射影像实现无人机连续六自由度定位，无需GPS
OrthoTrack: Continuous 6-DoF UAV Trajectory Estimation Anchored in Public Orthophotos
- 通过匹配关键帧与正射影像，结合地表模型恢复三维坐标
- 实时输出绝对尺度的轨迹，误差比基线低30%以上
- 适合无GPS环境下的无人机自主导航，部署无需现场标定
连续六自由度位姿估计对无人机自主飞行至关重要。现有视觉里程计和SLAM方法存在漂移，仅提供相对、未缩放轨迹；单帧地理定位则忽略时间连续性，且速度不足实时使用。我们提出OrthoTrack，一种无需训练的系统，仅利用公开可用的正射影像和地表模型作为地图先验，实现连续6-DoF无人机轨迹估计。OrthoTrack将关键帧与正射影像匹配，并通过地表模型将对应点提升至度量三维空间。随后利用光流将这些地图锚定的对应关系传播至中间帧，生成每帧的绝对、度量尺度姿态，无需GPS或后处理对齐。我们还构建了MovingDrone数据集，包含大规模逼真无人机序列、密集6-DoF真值及多模态地理数据（含多时相正射影像）。在MovingDrone和真实世界基准上，OrthoTrack可在单张GPU上实时运行，性能显著优于所有基线，甚至超过获得理想尺度和对齐信息的模型。依赖公开地理数据，该系统可直接部署至新区域而无需现场适应。
原文摘要 · Abstract (English)
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.
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