To support the Low Altitude Economy (LAE), it is essential to achieve precise localization of unmanned aerial vehicles (UAVs) in urban areas where global positioning system (GPS) signals are unavailable. Vision-based methods offer a viable alternative but face severe bandwidth, memory and processing constraints on lightweight UAVs. Inspired by mammalian spatial cognition, we propose a task-oriented communication framework, where UAVs equipped with multi-camera systems extract compact multi-view features and offload localization tasks to edge servers. We introduce the Orthogonally-constrained Variational Information Bottleneck encoder (O-VIB), which incorporates automatic relevance determination (ARD) to prune non-informative features while enforcing orthogonality to minimize redundancy. This enables efficient and accurate localization with minimal transmission cost. Extensive evaluation on a dedicated LAE UAV dataset shows that O-VIB achieves high-precision localization under stringent bandwidth budgets. Code and dataset will be made publicly available at:this http URL.
View on arXiv@article{fang2025_2504.18317,
title={ Task-Oriented Communications for Visual Navigation with Edge-Aerial Collaboration in Low Altitude Economy },
author={ Zhengru Fang and Zhenghao Liu and Jingjing Wang and Senkang Hu and Yu Guo and Yiqin Deng and Yuguang Fang },
journal={arXiv preprint arXiv:2504.18317},
year={ 2025 }
}