  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.}
