用AI自动生成多样逼真的无人机飞行数据集
FlyMirage: A Fully Automated Generation Pipeline for Diverse and Scalable UAV Flight Data via Generative World Model
- 用大模型设计场景，生成世界模型并转为3D高斯点云
- 自动生成可飞行的轨迹，支持大规模真实感数据生成
- 适合研究无人机导航、视觉语言导航的开发者
在视觉-语言导航（VLN）领域，空中数据集在规模、多样性与真实性方面仍显不足，常依赖昂贵的真实场景或视觉效果有限的模拟。为此，我们提出FlyMirage，一个高度可扩展且全自动的空中VLN数据生成管道。该方法利用大语言模型（LLM）作为环境设计师以提升场景多样性，并结合生成式世界模型将设计转化为高保真3D高斯泼溅（3DGS）场景。为大幅降低人工成本并确保飞行可行性，FlyMirage自动完成场景探索与语义信息获取，并集成动态可行路径规划器生成无人飞行器（UAV）飞行轨迹。借助该工具链，我们构建了一个大规模、多样化且逼真的空中VLN数据集，包含动态可行的飞行路径，旨在推动下一代具身导航模型的发展。
原文摘要 · Abstract (English)
In the field of Vision-Language Navigation (VLN), aerial datasets remain limited in their ability to combine scale, diversity, and realism, often relying on either costly real-world scenes or visually limited simulations. To address these challenges, we introduce FlyMirage, a highly scalable and fully automated data generation pipeline for aerial VLN. Our approach leverages large language models (LLM) as an environment designer to promote scene diversity, paired with a generative world model that instantiates these designs into high-fidelity 3D Gaussian Splatting (3DGS) scenes. To substantially reduce human labor and ensure the feasibility of flight data, FlyMirage automates scene exploration and semantic information acquisition, and further integrates a dynamically feasible planner for uncrewed aerial vehicle (UAV) trajectory generation. Utilizing this toolchain, we generate a large-scale, diverse, and photorealistic aerial VLN dataset, with dynamically feasible flying trajectories, designed to support the development of next-generation embodied navigation models.
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