PathPainter: 画像生成モデルの汎化能力を身体化ナビゲーションへ転移する
PathPainter: Transferring the Generalization Ability of Image Generation Models to Embodied Navigation
鳥瞰図画像を大域的な事前情報として用い、画像生成モデルで自然言語から目標地点と走行可能領域を推定し、クロスビュー位置合わせでドリフトを補正するナビゲーションシステムを提案。UAVで160mの長距離ナビゲーションを実証した。
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？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Yijin Wang, Yuru Tian, Xijie Huang, Weiqi Gai, Mo Zhu, Xin Zhou, Yuze Wu, Fei Gao
分類: cs.RO
原文アブストラクト
Bird's-eye-view (BEV) images have been widely demonstrated to provide valuable prior information for navigation. Given the global information provided by such views, two key challenges remain: how to fully exploit this information and how to reliably use it during execution. In this paper, we propose a navigation system that uses BEV images as global priors and is designed for ground and near-ground robotic platforms. The system employs an image generation model to interpret human intent from natural language, identify the target destination, and generate traversability masks. During execution, we introduce cross-view localization to align the robot's odometry with the BEV map and mitigate long-term drift in conventional odometry. We conduct extensive benchmark experiments to evaluate the proposed method and further validate it on a UAV platform. Using only a conventional local motion planner, the UAV successfully completes a 160-meter outdoor long-range navigation task. This work demonstrates how the world-understanding capabilities of foundation models can be transferred to embodied navigation, enabling robots to benefit from the strong generalization ability of existing image generation models.
