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.

翻译：近年来，视觉定位已成为提升定位可靠性的重要补充手段，而跨视角方法能显著增强覆盖范围与适应性。与此同时，未来6G将构建全球覆盖的移动通信系统，空天地一体化网络（SAGIN）将成为其关键支撑架构。受此启发，本研究探索将跨视角定位（CVL）与6G SAGIN相融合，从而在时延、能耗和隐私保护方面提升其性能。首先，我们对CVL与SAGIN进行全面综述，着重分析其技术特性、融合机遇与潜在应用场景。得益于6G SAGIN架构快速广泛的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，我们提出一种用于实现CVL的拆分推理框架，该框架充分利用6G SAGIN架构的分布式通信与计算资源。进而，我们在所提出的拆分推理框架内对通信、计算与保密性进行联合优化，旨在为CVL的高效化提供范式指引。实验结果验证了所提框架的有效性，并为该优化问题提供了解决方案。最后，我们探讨了6G SAGIN使能CVL的潜在研究方向。