Learning-based Bias Correction for Ultra-wideband Localization of Resource-constrained Mobile Robots

• URL: http://arxiv.org/abs/2003.09371v1
• Date: Fri, 20 Mar 2020 16:47:33 GMT
• Title: Learning-based Bias Correction for Ultra-wideband Localization of Resource-constrained Mobile Robots
• Authors: Wenda Zhao, Abhishek Goudar, Jacopo Panerati, and Angela P. Schoellig (University of Toronto Institute for Aerospace Studies, Vector Institute for Artificial Intelligence)
• Abstract summary: We propose a bias correction framework compatible with two-way ranging and time difference of arrival ultra-wideband localization. This approach is scalable and frugal enough to be deployed on-board a nano-quadcopter's microcontroller.
• Score: 9.609597889226398
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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