Uncertainty-Aware World Model for Aerial Image-Goal Navigation
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
Community
We present UA-NWM, an uncertainty-aware world model for aerial image-goal navigation. UA-NWM formulates trajectory scoring as conditional OOD detection, models plausible future variations through an uncertainty subspace, and separates plausible uncertainty-induced deviations from unexplained residual errors. This enables efficient distribution-aware trajectory scoring without stochastic future sampling. Experiments show that UA-NWM improves navigation performance across diverse tasks, while preserving low inference latency, and real-world UAV deployment further supports its practical applicability.
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