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Accurate indoor localization is a crucial enabling technology for manyrobotics applications, from warehouse management to monitoring tasks.Ultra-wideband (UWB) time difference of arrival (TDOA)-based localization is apromising lightweight, low-cost solution that can scale to a large number ofdevices -- making it especially suited for resource-constrained multi-robotapplications. However, the localization accuracy of standard, commerciallyavailable UWB radios is often insufficient due to significant measurement biasand outliers. In this letter, we address these issues by proposing a robust UWBTDOA localization framework comprising of (i) learning-based bias correctionand (ii) M-estimation-based robust filtering to handle outliers. The keyproperties of our approach are that (i) the learned biases generalize todifferent UWB anchor setups and (ii) the approach is computationally efficientenough to run on resource-constrained hardware. We demonstrate our approach ona Crazyflie nano-quadcopter. Experimental results show that the proposedlocalization framework, relying only on the onboard IMU and UWB, provides anaverage of 42.08 percent localization error reduction (in three differentanchor setups) compared to the baseline approach without bias compensation. {Wealso show autonomous trajectory tracking on a quadcopter using our UWB TDOAlocalization approach.}

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