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：摘要：理解现实世界中附着超宽带（UWB）标签的物体与人的位置，对于实现流畅的虚实融合至关重要。然而，当前大多数UWB定位系统需要在环境中部署多个锚节点，这带来了极大的部署负担。本文开发了XRLoc系统，在多种真实场景下可实现厘米级定位精度。本文将阐述突破单锚节点定位设备时误差仅达厘米级这一根本性限制的关键技术。我们通过日常物品搭建VR国际象棋游戏进行演示，结果显示：在动态场景中，系统定位中位误差为2.4厘米，90百分位误差为5.3厘米，性能较现有最优定位系统提升至少8倍。此外，我们实现了支持100Hz更新率下为超过10个标签提供定位服务的MAC协议，其定位延迟约为1毫秒。