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.
Aerial vision- and-language navigation via semantic-topo-metric representation guided llm reasoning
8 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 8representative citing papers
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.
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.
HTNav combines imitation and reinforcement learning in a staged, tiered structure with map learning to reach state-of-the-art performance on the CityNav benchmark for urban aerial navigation.
A monocular RGB-only aerial VLN framework outperforms baselines via prompt-guided multi-task learning, keyframe selection, and label reweighting on AerialVLN and OpenFly benchmarks.
CLOSER-VLN combines hierarchical reasoning, multidimensional verification, and triggered retrieval to reach 32.01% SR and 21.28% SPL on CityNav test-unseen without task-specific training.
A survey of UAV vision-and-language navigation that establishes a methodological taxonomy, reviews resources and challenges, and proposes a forward-looking research roadmap.
This survey organizes aerial vision-language navigation methods into five architectural categories, critically reviews evaluation infrastructure, and synthesizes seven open problems for LLM/VLM integration.
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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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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HTNav: A Hybrid Navigation Framework with Tiered Structure for Urban Aerial Vision-and-Language Navigation
HTNav combines imitation and reinforcement learning in a staged, tiered structure with map learning to reach state-of-the-art performance on the CityNav benchmark for urban aerial navigation.
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Aerial Vision-Language Navigation with a Unified Framework for Spatial, Temporal and Embodied Reasoning
A monocular RGB-only aerial VLN framework outperforms baselines via prompt-guided multi-task learning, keyframe selection, and label reweighting on AerialVLN and OpenFly benchmarks.
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CLOSER-VLN: Closed-Loop Self-Verified Retrieval-Augmented Reasoning for Aerial Vision-Language Navigation
CLOSER-VLN combines hierarchical reasoning, multidimensional verification, and triggered retrieval to reach 32.01% SR and 21.28% SPL on CityNav test-unseen without task-specific training.
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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.
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Vision-Language Navigation for Aerial Robots: Towards the Era of Large Language Models
This survey organizes aerial vision-language navigation methods into five architectural categories, critically reviews evaluation infrastructure, and synthesizes seven open problems for LLM/VLM integration.