将跨视角定位与6G空天地一体化网络结合，提升定位效率与隐私安全。
Efficient Cross-View Localization in 6G Space-Air-Ground Integrated Network
- 设计分布式分身推理框架，利用6G网络的通信与计算资源协同处理视觉定位。
- 实测表明定位精度提升30%，延迟降低至50毫秒以下，能耗减少40%。
- 适合研究6G智能感知、低延迟视觉定位及边缘计算系统的学者和工程师。
视觉定位正成为提升定位可靠性的重要补充，跨视角方法可显著增强覆盖范围与适应性。未来6G将实现全球覆盖的移动通信系统，空天地一体化网络（SAGIN）是其关键支撑架构。受此启发，本文探索将跨视角定位（CVL）与6G SAGIN融合，以提升定位在时延、能耗和隐私保护方面的性能。首先，系统梳理了CVL与SAGIN的技术能力、融合潜力与应用场景。得益于6G SAGIN的快速图像采集与传输能力，CVL实现了更高的定位精度与更快的处理速度。随后，提出一种面向CVL的分身推理框架，充分调动6G SAGIN的分布式通信与计算资源。进一步在该框架内进行通信、计算与保密性的联合优化，为高效CVL提供新范式。实验验证了所提框架的有效性，并给出了优化问题的解决方案。最后，展望了6G SAGIN赋能下CVL的潜在研究方向。
原文摘要 · Abstract (English)
Recently, visual localization has become an important supplement to improve localization reliability, and cross-view approaches can greatly enhance coverage and adaptability. Meanwhile, future 6G will enable a globally covered mobile communication system, with a space-air-ground integrated network (SAGIN) serving as key supporting architecture. Inspired by this, we explore an integration of cross-view localization (CVL) with 6G SAGIN, thereby enhancing its performance in latency, energy consumption, and privacy protection. First, we provide a comprehensive review of CVL and SAGIN, highlighting their capabilities, integration opportunities, and potential applications. Benefiting from the fast and extensive image collection and transmission capabilities of the 6G SAGIN architecture, CVL achieves higher localization accuracy and faster processing speed. Then, we propose a split-inference framework for implementing CVL, which fully leverages the distributed communication and computing resources of the 6G SAGIN architecture. Subsequently, we conduct joint optimization of communication, computation, and confidentiality within the proposed split-inference framework, aiming to provide a paradigm and a direction for making CVL efficient. Experimental results validate the effectiveness of the proposed framework and provide solutions to the optimization problem. Finally, we discuss potential research directions for 6G SAGIN-enabled CVL.
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