arxiv:2608.05597

Uncertainty-Aware World Model for Aerial Image-Goal Navigation

Published on Aug 6

· Submitted by

Zhu Deyi on Aug 10

Tsinghua University

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Authors:

Deyi Zhu ,

Haoyu Fan ,

Abstract

A latent world model for aerial navigation improves trajectory selection by separating explainable future uncertainty from residual discrepancies, reducing inference cost while handling large-scale outdoor environments.

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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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DurYi

Paper author Paper submitter 4 days ago

🌐 Project page: https://duryi.github.io/UA-NWM-Project-Page/
💻 Code: https://github.com/DurYi/UA-NWM
🤗 Data: https://huggingface.co/datasets/DurYi/AirGoal-10k
📦 Models: https://huggingface.co/DurYi/UA-NWM-Checkpoints

DurYi

Paper author Paper submitter 1 day ago

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.