Binocular Pose Estimation for UAV Autonomous Aerial Refueling via Brain Storm Optimization

Autonomous aerial refueling (AAR) is a crucial technique of unmanned aerial vehicles (UAVs) to push the fuel limits and play a great role in both civilian and military domains. This paper presents an accurate and robust binocular pose estimation algorithms optimized by brain storm optimization (BSO), which is developed from a robust non-iterative solution of PnP (RPnP). In this algorithm, BSO is employed to select the best rotation axis in RPnP. A large quantity of contrastive simulation experiments has been conducted to verify the proposed algorithm. Furthermore, this work built an aerial verification platform for vision-based AAR. A tanker UAV and a receiver UAV were applied to implement AAR. The real-time visual measuring system includes feature extraction and pose estimation. Several state-of-the-art pose estimation algorithms and the proposed method (which refers to BSO-BPnP) have been tested in the aerial verification platform. Adequate comparative trials and detailed analyses are given in this paper.