A comparison of Pose Estimation algorithms for Machine Vision based Aerial Refueling for UAVs

This paper focuses on the analysis of the performance of specific 'detection and labeling' and 'pose estimation' algorithms within a Machine Vision (MV)-based approach for the problem of Autonomous Aerial Refueling (AAR) of UAVs. A robust 'detection and labeling algorithm' for the correct identification and sorting of the optical markers is proposed; a sorted list of marker positions is then provided as input to the 'pose estimation' algorithm. A detailed study of the performance of two specific 'pose estimation' algorithms (GLSDC and LHM) is performed with special emphasis on the required computational effort as well as on the robustness and error propagation characteristics. Extensive simulation studies demonstrate the performance of the LHM and GLSDC algorithms and show the importance of a robust 'detection and labeling' algorithm. The simulation effort is performed using a detailed modeling of the AAR maneuver according to the USAF refueling method.