测试五种单目SLAM在高空俯视无人机视频上的表现，发现均无法可靠实现视觉导航。
Evaluation of Monocular SLAM Systems on High-Altitude Nadir UAV Footage
- 在无惯性/定位辅助下，测试五种单目SLAM在真实与合成航拍数据上的表现。
- 最优系统DROID-SLAM水平误差平均为参考路径长的2.88%，但垂直精度差且长程轨迹严重失真。
- 高空俯视场景因几何弱约束和感知混淆，现有方法仍难满足可靠视觉导航需求。
航空俯视视频结合了弱几何约束与严重的感知混淆，对单目SLAM构成严峻挑战。我们在局部无人机飞行、合成城市尺度图像及远距离航拍序列上评估了五种单目SLAM系统。为隔离视觉性能，未提供惯性或GNSS辅助。性能随环境与轨迹尺度变化显著：MASt3R-SLAM在五次DJI飞行中取得最低水平均方误差（0.53%参考路径长），但无系统能在长距离GES和ALTO序列上保持全局轨迹形状。总体而言，DROID-SLAM表现最佳，所有完成运行的平均误差为参考路径长的2.88%。垂直位置估计仍较差，大范围轨迹即使具备回环闭合能力仍高度扭曲。因此，当前单目SLAM方法自身不足以支持可靠的纯视觉空中导航。
原文摘要 · Abstract (English)
Aerial nadir video combines weak geometric constraints with severe perceptual aliasing, making it a difficult regime for monocular SLAM. We benchmark five monocular SLAM systems on local UAV flights, synthetic city-scale imagery, and long-range aerial sequences. To isolate visual performance, we provide no inertial or GNSS aiding. Performance varies strongly with environment and trajectory scale: MASt3R-SLAM achieves the lowest mean horizontal MAE on the five DJI flights (0.53% of reference path length), whereas no system consistently preserves global trajectory shape on the long GES and ALTO sequences. Overall, DROID-SLAM performs best, averaging 2.88% of reference path length across completed runs. Vertical position remains poor, and large-area trajectories remain highly distorted despite loop-closure capability. Current monocular SLAM methods are by themselves therefore insufficient for reliable visual-only aerial navigation.
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