  Simulation can and should play a critical role in the development and testing
of algorithms for autonomous agents. What might reduce its impact is the
``sim2real'' gap -- the algorithm response differs between operation in
simulated versus real-world environments. This paper introduces an approach to
evaluate this gap, focusing on the accuracy of sensor simulation --
specifically IMU and GPS -- in velocity estimation tasks for autonomous agents.
Using a scaled autonomous vehicle, we conduct 40 real-world experiments across
diverse environments then replicate the experiments in simulation with five
distinct sensor noise models. We note that direct comparison of raw simulation
and real sensor data fails to quantify the sim2real gap for robotics
applications. We demonstrate that by using a state of the art state-estimation
package as a ``judge'', and by evaluating the performance of this
state-estimator in both real and simulated scenarios, we can isolate the
sim2real discrepancies stemming from sensor simulations alone. The dataset
generated is open-source and publicly available for unfettered use.
