CosFlyTrack provides 12,000 expert UAV trajectories with aligned RGB, depth, segmentation, pose, target state, and bilingual instructions to train visual tracking agents, yielding 53-69 point gains in success rate after fine-tuning.
Towards realistic UA V vision-language navigation: Platform, benchmark, and methodology
7 Pith papers cite this work. Polarity classification is still indexing.
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LookasideVLN improves aerial vision-and-language navigation by encoding directional cues from instructions into an egocentric graph and lightweight knowledge base, outperforming prior methods like CityNavAgent even with single-step lookahead.
Large multimodal models display emerging but limited spatial action capabilities in goal-oriented urban 3D navigation, remaining far from human-level performance with errors diverging rapidly after critical decision points.
NeuroKalman mitigates state drift in vision-language UAV navigation by using memory-augmented Kalman filtering where attention retrieves historical anchors to correct predictions without gradient updates.
FineCog-Nav uses fine-grained cognitive modules driven by foundation models to outperform zero-shot baselines in UAV navigation and introduces the AerialVLN-Fine benchmark with refined instructions.
FlyMirage automates generation of large-scale, diverse, photorealistic aerial VLN datasets with dynamically feasible UAV trajectories by combining LLM scene design, 3D Gaussian Splatting world models, automated exploration, and trajectory planning.
A survey of UAV vision-and-language navigation that establishes a methodological taxonomy, reviews resources and challenges, and proposes a forward-looking research roadmap.
citing papers explorer
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LookasideVLN: Direction-Aware Aerial Vision-and-Language Navigation
LookasideVLN improves aerial vision-and-language navigation by encoding directional cues from instructions into an egocentric graph and lightweight knowledge base, outperforming prior methods like CityNavAgent even with single-step lookahead.
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How Far Are Large Multimodal Models from Human-Level Spatial Action? A Benchmark for Goal-Oriented Embodied Navigation in Urban Airspace
Large multimodal models display emerging but limited spatial action capabilities in goal-oriented urban 3D navigation, remaining far from human-level performance with errors diverging rapidly after critical decision points.
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Mitigating Error Accumulation in Continuous Navigation via Memory-Augmented Kalman Filtering
NeuroKalman mitigates state drift in vision-language UAV navigation by using memory-augmented Kalman filtering where attention retrieves historical anchors to correct predictions without gradient updates.
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FineCog-Nav: Integrating Fine-grained Cognitive Modules for Zero-shot Multimodal UAV Navigation
FineCog-Nav uses fine-grained cognitive modules driven by foundation models to outperform zero-shot baselines in UAV navigation and introduces the AerialVLN-Fine benchmark with refined instructions.
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FlyMirage: A Fully Automated Generation Pipeline for Diverse and Scalable UAV Flight Data via Generative World Model
FlyMirage automates generation of large-scale, diverse, photorealistic aerial VLN datasets with dynamically feasible UAV trajectories by combining LLM scene design, 3D Gaussian Splatting world models, automated exploration, and trajectory planning.
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Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap
A survey of UAV vision-and-language navigation that establishes a methodological taxonomy, reviews resources and challenges, and proposes a forward-looking research roadmap.