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This paper benchmarks five visual SLAM algorithms (ORB-SLAM3, DPVO, DROID-SLAM, DUSt3R, and MASt3R) across a range of visual degradation conditions relevant to UAV navigation in GPS-denied environments. The evaluation uses multiple public datasets and a custom dataset with controlled degradations, assessing tracking success rate (TSR) and absolute trajectory error (ATE) against Vicon ground truth. Results indicate that learning-based methods, particularly MASt3R and DUSt3R, exhibit superior robustness compared to ORB-SLAM3 under severe degradation, while DPVO offers a favorable efficiency-robustness trade-off for embedded deployment.
Classical SLAM algorithms crumble under visual degradation, but deep learning approaches like MASt3R and DUSt3R maintain impressive localization accuracy, suggesting a path to robust UAV autonomy in challenging environments.
Reliable localization in GPS-denied, visually degraded environments is critical for autonomous UAV opera- tions. This paper presents a systematic comparative evaluation of five V-SLAM systems ORB-SLAM3, DPVO, DROID-SLAM, DUSt3R, and MASt3R spanning classical, deep learning, recurrent, and Vision Transformer (ViT) paradigms. Experiments are conducted on curated sequences from four public benchmarks (TUM RGB-D, EuRoC MAV, UMA-VI, SubT-MRS) and a custom monocular indoor dataset under five controlled degradation conditions (normal, low light, dust haze, motion blur, and combined), with sub-millimeter Vicon ground truth. Results show that ORB-SLAM3 fails critically under severe degradation (62.4% overall TSR; 0% under dense haze), while learning-based methods remain robust: MASt3R achieves the lowest degraded ATE (0.027 m) and DUSt3R the highest tracking success (96.5%). DPVO offers the best efficiency robustness trade-off (18.6 FPS, 3.1 GB GPU memory, 86.1% TSR), making it the preferred choice for memory-constrained embedded platforms. Embedded deployment analysis across NVIDIA Jetson platforms provides actionable guidelines for SLAM selection under SWaP-constrained UAV scenarios.
