Edge-assisted multi-view localization cuts errors and communication load
Task-Oriented Communications for Edge-Assisted Multi-View Localization
Networking and Internet ArchitectureRobotics
Summary
Locating drones and robots precisely is hard when GPS signals are weak or blocked, like in cities or indoors. The authors developed a system that smartly decides when and what visual data to send from these devices to nearby servers to improve location accuracy. Their method reduces position errors significantly and cuts down the data sent by over 98%, making the process faster and less demanding on device power. It also prioritizes important data during network congestion, ensuring timely help when it matters most.
What this means in practice
- •For drone operators: Improve drone positioning in GPS-denied environments by selectively offloading visual data to edge servers for better accuracy and lower communication cost.
- •For indoor robotics teams: Enhance indoor robot localization by compressing and prioritizing multi-view image data transmitted to edge assistance, reducing latency and improving location reliability.
Authors
Zhengru Fang, Huanhuan Lou, Senkang Hu, Yihang Tao, Zongdian Li, Yiqin Deng, Jingjing Wang, Yuguang Fang
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.