Uncertainty-Aware World Model for Aerial Image-Goal Navigation

• URL: http://arxiv.org/abs/2608.05597v1
• Date: Thu, 06 Aug 2026 04:43:53 GMT
• Title: Uncertainty-Aware World Model for Aerial Image-Goal Navigation
• Abstract summary: Uncertainty-Aware Navigation World Model (UA-NWM) is an efficient latent world model for aerial image-goal navigation.<n>It decomposes the prediction--goal discrepancy into uncertainty-explainable and unexplainable components.<n>It consistently outperforms existing navigation world models while maintaining low inference latency.
• Score: 49.01078106635952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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