履歴強化型二段階Transformerによる空中視覚言語ナビゲーション
History-Enhanced Two-Stage Transformer for Aerial Vision-and-Language Navigation
大規模都市環境でのUAVナビゲーションにおいて、粗い目標位置予測と細かい視覚分析を組み合わせた二段階Transformerと履歴グリッドマップを提案し、CityNavデータセットで性能を向上させた。
詳しい要約
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著者: Xichen Ding, Jianzhe Gao, Cong Pan, Wenguan Wang, Jie Qin
分類: cs.CV, cs.RO
原文アブストラクト
Aerial Vision-and-Language Navigation (AVLN) requires Unmanned Aerial Vehicle (UAV) agents to localize targets in large-scale urban environments based on linguistic instructions. While successful navigation demands both global environmental reasoning and local scene comprehension, existing UAV agents typically adopt mono-granularity frameworks that struggle to balance these two aspects. To address this limitation, this work proposes a History-Enhanced Two-Stage Transformer (HETT) framework, which integrates the two aspects through a coarse-to-fine navigation pipeline. Specifically, HETT first predicts coarse-grained target positions by fusing spatial landmarks and historical context, then refines actions via fine-grained visual analysis. In addition, a historical grid map is designed to dynamically aggregate visual features into a structured spatial memory, enhancing comprehensive scene awareness. Additionally, the CityNav dataset annotations are manually refined to enhance data quality. Experiments on the refined CityNav dataset show that HETT delivers significant performance gains, while extensive ablation studies further verify the effectiveness of each component.
