AI 中文总结
该研究评估5种单目SLAM系统在高空天底视角无人机影像的性能，发现DROID-SLAM表现最优，MASt3R-SLAM水平误差最低，但所有系统垂直位置估计差、大面积轨迹失真，纯视觉单目SLAM不足以可靠应用于航空导航。
AI 中文摘要
航空天底视角视频兼具较弱的几何约束与严重的感知混叠，是单目SLAM的困难场景。我们在本地无人机飞行、合成城市规模影像及远程航空序列上对5个单目SLAM系统进行基准测试，为隔离视觉性能，未提供惯性或GNSS辅助。性能随环境与轨迹规模差异显著：MASt3R-SLAM在5次DJI飞行中实现最低平均水平绝对误差（为参考路径长度的0.53%），但无系统能在长距离GES与ALTO序列上始终保持全局轨迹形状。总体而言，DROID-SLAM表现最佳，完成运行的平均误差为参考路径长度的2.88%。垂直位置估计仍较差，即便具备回环检测能力，大面积轨迹仍高度失真。因此，当前单目SLAM方法本身不足以实现可靠的纯视觉航空导航。
英文摘要
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.
Comments6 pages, accepted to ERK 2026
