Understanding the location of ultra-wideband (UWB) tag-attached objects and people in the real world is vital to enabling a smooth cyber-physical transition. However, most UWB localization systems today require multiple anchors in the environment, which can be very cumbersome to set up. In this work, we develop XRLoc, providing an accuracy of a few centimeters in many real-world scenarios. This paper will delineate the key ideas which allow us to overcome the fundamental restrictions that plague a single anchor point from localization of a device to within an error of a few centimeters. We deploy a VR chess game using everyday objects as a demo and find that our system achieves $2.4$ cm median accuracy and $5.3$ cm $90^\mathrm{th}$ percentile accuracy in dynamic scenarios, performing at least $8\times$ better than state-of-art localization systems. Additionally, we implement a MAC protocol to furnish these locations for over $10$ tags at update rates of $100$ Hz, with a localization latency of $\sim 1$ ms.

翻译：理解现实世界中附着超宽带（UWB）标签的物体与人的位置，对于实现顺畅的虚实融合过渡至关重要。然而，当前大多数UWB定位系统需要环境中部署多个锚点，这往往导致部署过程极为繁琐。本研究开发了XRLoc系统，可在多种实际场景中实现厘米级精度。本文阐述了突破单锚点设备定位误差限制的核心技术——通过创新方法将定位误差控制在厘米范围内。我们以日常物品为交互媒介部署了VR国际象棋游戏作为演示，实验表明在动态场景中系统定位中位精度达2.4厘米，90百分位精度达5.3厘米，性能较现有最佳定位系统提升至少8倍。此外，我们实现了支持100Hz更新速率下10个以上标签位置同步的MAC协议，定位延迟约为1毫秒。