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This study benchmarks five monocular SLAM systems on challenging high-altitude UAV footage, revealing significant limitations in vertical positioning and global trajectory preservation when relying solely on visual data without inertial or GNSS aiding. While DROID-SLAM shows the best overall performance across completed runs, no current method consistently maintains accurate shape over long-range sequences like GES and ALTO. The findings underscore that existing monocular approaches are insufficient for reliable autonomous aerial navigation in complex environments.
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Evaluation of Monocular SLAM Systems on High-Altitude Nadir UAV Footage

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2608.18632v1 Announce Type: new Abstract: 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.

Abstract: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.

Robotics (cs.RO)

arXiv:2608.18632 [cs.RO]

(or arXiv:2608.18632v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.18632 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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