FlyMirage：生成的世界モデルによる多様でスケーラブルなUAV飛行データの完全自動生成パイプライン
FlyMirage: A Fully Automated Generation Pipeline for Diverse and Scalable UAV Flight Data via Generative World Model
大規模言語モデルと生成的世界モデルを組み合わせ、高忠実度の3DGSシーンを自動生成し、UAVの飛行軌道を計画することで、多様で現実的な空中VLNデータセットを自動構築するパイプラインを提案した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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著者: Jinhan Li, Xijie Huang, Zhaoqi Wang, Yijin Wang, Weiqi Ge, Qiyi He, Mo Zhu, Fei Gao, Yuze Wu, Xin Zhou
分類: cs.RO
原文アブストラクト
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.
