Learning-based Bias Correction for Time Difference of Arrival Ultra-wideband Localization of Resource-constrained Mobile Robots
| Autor: | Wenda Zhao, Angela P. Schoellig, Jacopo Panerati |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer Science - Machine Learning J.2 Control and Optimization Computer science Real-time computing Biomedical Engineering Ultra-wideband 02 engineering and technology 01 natural sciences J.7 Machine Learning (cs.LG) Computer Science - Robotics 020901 industrial engineering & automation Artificial Intelligence Inertial measurement unit business.industry I.2.6 Mechanical Engineering 010401 analytical chemistry I.2.9 Mobile robot Robotics Multilateration 0104 chemical sciences Computer Science Applications Human-Computer Interaction Control and Systems Engineering Outlier Robot Measurement uncertainty Computer Vision and Pattern Recognition Artificial intelligence business Robotics (cs.RO) |
| Popis: | Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks. Ultra-wideband (UWB) time difference of arrival (TDOA)-based localization is a promising lightweight, low-cost solution that can scale to a large number of devices -- making it especially suited for resource-constrained multi-robot applications. However, the localization accuracy of standard, commercially available UWB radios is often insufficient due to significant measurement bias and outliers. In this letter, we address these issues by proposing a robust UWB TDOA localization framework comprising of (i) learning-based bias correction and (ii) M-estimation-based robust filtering to handle outliers. The key properties of our approach are that (i) the learned biases generalize to different UWB anchor setups and (ii) the approach is computationally efficient enough to run on resource-constrained hardware. We demonstrate our approach on a Crazyflie nano-quadcopter. Experimental results show that the proposed localization framework, relying only on the onboard IMU and UWB, provides an average of 42.08 percent localization error reduction (in three different anchor setups) compared to the baseline approach without bias compensation. {We also show autonomous trajectory tracking on a quadcopter using our UWB TDOA localization approach.} 8 pages, 9 figures, accepted for publication on the IEEE Robotics and Automation Letters and presentation at ICRA 2021 |
| Databáze: | OpenAIRE |
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