Task-Oriented Communications for Edge-Assisted Multi-View Localization

2026-09-28

AI Summary

This study addresses the visual localization challenge for UAVs and UGVs operating under computational constraints in satellite-denied environments. To this end, it proposes a network-adaptive, task-oriented communication framework that jointly optimizes computation offloading timing, view selection, and semantic transmission rates. The core innovations include a scalable orthogonally regularized variational information bottleneck (O-VIB) encoder, a value-of-information (VOI)-guided request control mechanism, and Lyapunov optimization-based dynamic system scheduling. Experimental results demonstrate that, compared to baseline methods, the proposed approach reduces localization error by over 24% and decreases communication traffic by more than 98%. Furthermore, it achieves a 76.2% reduction in critical request latency under highly congested scenarios, highlighting its effectiveness in balancing computational efficiency with communication reliability for resource-constrained autonomous systems.

Abstract

Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) often lose satellite positioning in urban canyons, indoor facilities, and jammed or spoofed environments, making vision-based matching with geo-tagged databases important for absolute positioning. However, limited onboard computation and energy often require localization to be offloaded over wireless links with time-varying throughput. We present a network-adaptive task-oriented communication framework that jointly determines when to offload, which views and semantic rate to transmit, and which client to serve. The framework combines scalable orthogonality-regularized variational information bottleneck (O-VIB) encoding, value-of-information (VOI)-guided request control, and VOI-weighted Lyapunov scheduling. O-VIB supports importance-ordered latent prefixes and different view subsets, while edge assistance is requested only when its predicted reduction in localization risk exceeds the communication and service cost. Under a matched per-route traffic budget, VOI-guided control reduces mean and p95 route errors by 24.8% and 31.0% over budgeted periodic offloading on CARLA multi-view UAV data. In real-world indoor UAV and UGV experiments, the pipeline reduces mean position error by 28.0% and 14.4% over uncompressed all-view CLIP retrieval while cutting descriptor traffic by 98.6% and 98.2%, respectively. Under high congestion, value-aware shaping reduces edge-side p95 latency for the top-10% high-value requests by 76.2%, from 137.7 ms to 32.8 ms.

Problem

• task-oriented communication
• multi-view localization
• edge offloading
• UAV
• bandwidth-constrained

Innovation

• Task-Oriented Communication
• Variational Information Bottleneck
• Value-of-Information
• Edge-Assisted Localization
• Lyapunov Scheduling