The use of unmanned aerial vehicles (UAVs) for autonomous inspection tasks has become more prominent in recent years. To make the most of the autonomous inspection, the parameters governing control, perception and navigation of the robot should be tuned precisely to the necessary task. Currently, the use of motion capture (mocap) systems is the norm when performing the stringent evaluation of simultaneous localization and mapping (SLAM) and advanced controllers. In this paper, we address the use of a cost-effective solution to ground-truthing and evaluation of said algorithms in large industrial environments. To this end, we use fiducial markers, deployed in known locations, in order to estimate the pose of the vehicle in 6 degrees-of-freedom (6DOF) and test them against a state-of-the-art mocap system. We additionally test the method in the field, by deploying the markers to the environment of interest and applying widely used SLAM implementations to confirm its efficacy by evaluating their performance in two emulated inspection task scenarios. We find that our method is comparable in performance to the state-of-the-art mocap systems without the need for laborious calibration and is capable of providing a pose estimate for evaluating SLAM and underlying UAV control methods.
