通过仿生认知机制，实现边缘端低带宽下的高精度视觉定位。
Task-Oriented Semantic Compression for Localization at the Network Edge
- 基于生物空间认知设计任务导向通信框架，压缩多视角特征。
- 在严苛带宽下定位误差低于0.5米，优于现有方法。
- 适合移动设备与边缘计算协同的智能定位场景。
在GPS受限的城市环境中，资源受限的移动平台面临精准视觉定位的挑战，尤其在带宽、内存和计算能力严格受限的情况下。受哺乳动物空间认知启发，我们提出一种任务导向的通信框架：配备多摄像头的带宽受限终端提取紧凑的多视角特征，并将定位任务卸载至协同边缘服务器。我们引入正交约束变分信息瓶颈编码器（O-VIB），结合自动相关性确定（ARD）机制，剔除非信息特征并强制正交性以减少冗余，从而实现低传输开销下的高效准确定位。在真实城市定位数据集上的大量实验表明，O-VIB在严苛带宽预算下实现了高精度定位，优于现有方法，在多种通信约束条件下均表现优异。
原文摘要 · Abstract (English)
Achieving precise visual localization in GPS-limited urban environments poses significant challenges for resource-constrained mobile platforms, particularly under strict bandwidth, memory, and processing limitations. Inspired by mammalian spatial cognition, we propose a task-oriented communication framework in which bandwidth-limited endpoints equipped with multi-camera systems extract compact multi-view features and offload localization tasks to collaborative 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 overhead. Extensive evaluation on a real-world urban localization dataset demonstrates that O-VIB achieves high-precision localization under stringent bandwidth budgets, outperforming existing methods across diverse communication constraints.
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