面向航拍图像目标导航的不确定性感知世界模型
Uncertainty-Aware World Model for Aerial Image-Goal Navigation
- Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对航拍图像目标导航中现有世界模型的未来状态预测不足问题，提出UA-NWM模型，将轨迹评分建模为条件分布外检测，仅用不可解释残差评分，实验验证其性能与实用性。
AI中文摘要：
航拍图像目标导航要求无人机（UAV）抵达由目标图像指定的目标位置。现有基于世界模型的方法通过预测未来状态对候选轨迹进行排序，但通常仅依赖1个或少数点预测，这对于存在大量未来状态不确定性的大规模户外环境而言并不适用。为解决该局限，我们提出不确定性感知导航世界模型（UA-NWM），这是一种用于航拍图像目标导航的高效潜在世界模型，其将轨迹评分建模为条件分布外检测问题。UA-NWM通过不确定性子空间表示合理的未来状态，并将预测-目标差异分解为可由不确定性解释与不可解释的两部分，仅使用不可解释残差进行评分，无需多个未来样本即可实现鲁棒选择。大量实验表明，UA-NWM在保持低推理延迟的同时，始终优于现有导航世界模型；真实无人机实验进一步验证了其实际适用性。项目页面：this https URL
英文摘要：
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
