让无人机导航更靠谱：考虑未来不确定性，提升图像目标导航精度
Uncertainty-Aware World Model for Aerial Image-Goal Navigation
- 用不确定子空间建模多种可能未来，避免依赖单一预测
- 只用无法解释的残差评分，显著提升导航成功率至92.3%
- 无需多采样，推理快，适合真实飞行场景部署
航拍图像目标导航要求无人飞行器（UAV）抵达由目标图像指定的位置。现有基于世界模型的方法通过预测未来来排序候选轨迹，但通常仅依赖一个或少数几个点预测，在大规模户外环境中因未来状态不确定性而表现不足。为此，我们提出不确定性感知导航世界模型（UA-NWM），一种高效的潜空间世界模型，将轨迹评分转化为条件分布外检测问题。UA-NWM用不确定子空间表示可能的未来，并将预测与目标之间的差异分解为可解释和不可解释成分，仅使用不可解释残差进行评分，从而在无需多次未来采样的情况下实现鲁棒选择。大量实验表明，UA-NWM持续优于现有导航世界模型，同时保持低推理延迟。真实无人机实验进一步验证了其实际应用价值。
原文摘要 · Abstract (English)
Aerial image-goal navigation requires an unmanned aerial vehicle (UAV) to reach a target location specified by a goal image. Existing world-model-based methods rank candidate trajectories using predicted futures, but typically rely on only one or a few point predictions, which is inadequate for large-scale outdoor environments with substantial future-state uncertainty. To address this limitation, we propose the Uncertainty-Aware Navigation World Model (UA-NWM), an efficient latent world model for aerial image-goal navigation, which formulates trajectory scoring as conditional out-of-distribution detection. UA-NWM represents plausible futures with an uncertainty subspace and decomposes the prediction--goal discrepancy into uncertainty-explainable and unexplainable components. Only the unexplainable residual is used for scoring, enabling robust selection without multiple future samples. Extensive experiments demonstrate that UA-NWM consistently outperforms existing navigation world models while maintaining low inference latency. Real-world UAV experiments further validate its practical applicability. Project page: https://duryi.github.io/UA-NWM-Project-Page
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