面向边缘辅助多视角定位的任务导向通信
Edge-Assisted Multi-View Localization for Low-Altitude Economy under GPS-Challenged Environments
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中文总结 AI 辅助
提出网络自适应任务导向通信框架，联合决策卸载时机、视角与语义速率，结合O-VIB编码和VOI调度，显著降低多视角定位误差与流量，并优化高价值请求延迟。
中文摘要 AI 辅助
无人机（UAV）和无人地面车辆（UGV）在城市峡谷、室内设施以及受干扰或欺骗的环境中经常失去卫星定位能力，这使得基于视觉的、与地理标记数据库匹配的方法对于绝对定位变得重要。然而，机载计算和能量有限，通常需要将定位任务通过无线链路卸载，而无线链路的吞吐量随时间变化。我们提出了一种网络自适应的任务导向通信框架，该框架联合决定何时卸载、传输哪些视角和语义速率，以及服务哪个客户端。该框架结合了可扩展的正交正则化变分信息瓶颈（O-VIB）编码、基于信息价值（VOI）的请求控制，以及VOI加权的李雅普诺夫调度。O-VIB支持按重要性排序的潜在前缀和不同的视角子集，而仅在预测的定位风险降低超过通信和服务成本时，才请求边缘辅助。在匹配的每路线流量预算下，与预算周期卸载相比，VOI引导控制在CARLA多视角无人机数据上将平均和p95路线误差分别降低了24.8%和31.0%。在真实世界的室内无人机和UGV实验中，该流程相比未压缩的全视角CLIP检索，将平均位置误差分别降低了28.0%和14.4%，同时将描述符流量分别削减了98.6%和98.2%。在高拥塞情况下，价值感知整形将边缘侧前10%高价值请求的p95延迟降低了76.2%，从137.7毫秒降至32.8毫秒。
英文摘要
Unmanned aerial vehicles (UAVs) serving the low-altitude economy require reliable localization in urban canyons, indoor facilities, and other GPS-challenged environments. Visual matching with a geo-tagged database provides an alternative source for absolute positioning, but onboard computation and energy limits motivate offloading the database and matching pipeline to an edge server. The resulting localization quality depends on what visual information can reach the edge in time under varying wireless-communication and edge-computing resources. In this paper, we propose a network-adaptive edge-assisted multi-view localization framework that combines scalable orthogonality-regularized variational information bottleneck (O-VIB) encoding, value-of-information (VOI)-guided request control, and value-aware edge scheduling. We design an O-VIB model that supports nested latent prefixes from 8 to 128 dimensions and four UAV view modes. Each UAV requests edge assistance when the predicted localization-risk reduction exceeds the communication and service costs. On CARLA multi-view UAV data, VOI-guided control can lower the mean and 95th-percentile (p95) route errors by 24.8% and 31.0%, respectively, relative to budgeted periodic offloading under a matched per-route traffic budget. In indoor UAV experiments with motion-capture ground truth, our design can lower the mean position error by 28.0% relative to uncompressed all-view CLIP retrieval while cutting the descriptor traffic by 98.6%, using a 0.145 KB semantic representation. Under high congestion, a VOI-weighted scheduler with waiting-age and deadline shaping can lower the edge-side p95 latency of the top-10% high-value requests from 137.7 ms to 32.8 ms.
发表机构
- The Hong Kong University of Science and Technology（香港科技大学）
- City University of Hong Kong（香港城市大学）
- Zhejiang University（浙江大学）
- Lingnan University（岭南大学）
- Beihang University（北京航空航天大学）
机构由 AI 辅助整理，请以论文原文为准。
