Task-Oriented Communications for Visual Navigation with Edge-Aerial Collaboration in Low Altitude Economy
This addresses bandwidth and processing constraints for lightweight UAVs in urban navigation, though it appears incremental as it builds on existing vision-based and information bottleneck methods.
The paper tackles the problem of precise UAV localization in GPS-denied urban areas for the Low Altitude Economy by proposing a task-oriented communication framework that offloads localization to edge servers, achieving high-precision localization with minimal transmission cost as demonstrated on a dedicated dataset.
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: github.com/fangzr/TOC-Edge-Aerial.