Learning-based Bias Correction for Ultra-wideband Localization of Resource-constrained Mobile Robots
This work addresses indoor localization challenges for resource-constrained robots like nano-quadcopters, offering a scalable and frugal solution, though it is incremental as it builds on prior bias correction methods by extending to new modes and deployment scenarios.
The paper tackled the problem of inaccurate and biased range measurements in ultra-wideband (UWB) localization for resource-constrained mobile robots by proposing a learning-based bias correction framework, resulting in a reduction of localization error by ~18.5% for two-way ranging and 48% for time difference of arrival, enabling accurate trajectory tracking with UWB-only position information.
Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks. Ultra-wideband (UWB) ranging is a promising solution which is low-cost, lightweight, and computationally inexpensive compared to alternative state-of-the-art approaches such as simultaneous localization and mapping, making it especially suited for resource-constrained aerial robots. Many commercially-available ultra-wideband radios, however, provide inaccurate, biased range measurements. In this article, we propose a bias correction framework compatible with both two-way ranging and time difference of arrival ultra-wideband localization. Our method comprises of two steps: (i) statistical outlier rejection and (ii) a learning-based bias correction. This approach is scalable and frugal enough to be deployed on-board a nano-quadcopter's microcontroller. Previous research mostly focused on two-way ranging bias correction and has not been implemented in closed-loop nor using resource-constrained robots. Experimental results show that, using our approach, the localization error is reduced by ~18.5% and 48% (for TWR and TDoA, respectively), and a quadcopter can accurately track trajectories with position information from UWB only.