XRLoc: Accurate UWB Localization to Realize XR Deployments

TL;DR

XRLoc addresses the challenge of cm-scale UWB localization from a single 1 m module to enable XR deployments. It fuses time-difference and phase-difference measurements with a bias-calibrated model and a particle-filter-based estimator to overcome geometric dilution of precision, while a LoRa-based MAC supports high-rate, multi-tag localization. The system achieves median static/dynamic localization errors of approximately $1.5$ cm/$2.4$ cm and 90th percentile errors around $5.5$ cm/$5.3$ cm, with sub-millisecond latency, and demonstrates >99.5% MAC success for tens of tags at 100 Hz. These results enable practical, low-deployment, real-time XR localization suitable for live VR/AR experiences in everyday spaces.

Abstract

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.

Figures (11)

• Figure 1: XRLoc enables users to play a life-size chess game with everyday objects. XRLoc localizes mugs retrofitted with off-the-shelf UWB tags from a single vantage point with a few cm of location accuracy, which are then translated to chess pieces in the virtual world.
• Figure 2: (a) Spatially-diverse placement of UWB anchors (red diamonds) near the walls provides median accuracy with TWR of $2.9$ cm (b) when receivers are constrained near the bottom wall, median accuracy degrades by $~8\times$ when using TWR (c) fusion of TDoA, TWR, and AoA does not help in these scenarios either, providing median accuracy of $23.3$ cm. (d) XRLoc solves the challenges associated with dilution of precision, achieving median accuracy of $3.3$ cm (e) Summary of errors when leveraging various UWB measurements and XRLoc.
• Figure 3: Log-likelihood heat map of PDoA and TDoA when changing the number of antennas $N$.
• Figure 4: (a) Localization error vs. PDoA error standard deviation, with TDoA error standard deviations as each line in the legend. For few-cm level localization, the threshold, per the red line, is $\sigma_t = 5^\circ$ and $\sigma_t = 150$ ps. (b) Phase measurements (green) deviate from ideal (black) measurements. Performing appropriate calibration fixes these deviations (red).
• Figure 5: Implementation: (a) Block diagram showcasing interconnections between the $6$ UWB receivers evb1000, the clock synchronization scheme (blue), "SYNC" implementation (red), and data back-haul via USB (black). (b) real-world implementation of block diagram; inset: external modification to UWB receiver. (c) block diagram for Tag showcasing the UWB and LoRa radios, the interrupt line (blue) to schedule UWB transmission and LoRa clock-sync broadcasts (dotted green) (d) real-world implementation of Tag. (e) UWB/LoRa radio parameters.