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Aerial Vision-and-Language Navigation with Grid-based View Selection and Map Construction
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Aerial Vision-and-Language Navigation with Grid-based View Selection and Map Construction
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Aerial Vision-and-Language Navigation (Aerial VLN) aims to obtain an unmanned aerial vehicle agent to navigate aerial 3D environments following human instruction. Compared to ground-based VLN, aerial VLN requires the agent to decide the next action in both horizontal and vertical directions based on the first-person view observations. Previous methods struggle to perform well due to the longer navigation path, more complicated 3D scenes, and the neglect of the interplay between vertical and horizontal actions. In this paper, we propose a novel grid-based view selection framework that formulates aerial VLN action prediction as a grid-based view selection task, incorporating vertical action prediction in a manner that accounts for the coupling with horizontal actions, thereby enabling effective altitude adjustments. We further introduce a grid-based bird's eye view map for aerial space to fuse the visual information in the navigation history, provide contextual scene information, and mitigate the impact of obstacles. Finally, a cross-modal transformer is adopted to explicitly align the long navigation history with the instruction. We demonstrate the superiority of our method in extensive experiments.
Forward citations
Cited by 5 Pith papers
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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 wi...
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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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FSD-VLN: Fast-Slow Dual-System Modeling for Aerial Long-Horizon Vision-Language Navigation
An asynchronous fast-slow dual-system with DiT action modeling and time-weighted loss doubles unseen aerial VLN success rates and halves decision latency in simulation.
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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.
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