Paper Info
Title
Binocular Pose Estimation for UAV Autonomous Aerial Refueling via Brain Storm Optimization
Abstract
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.
Year
DOI
Venue
2019
10.1109/CEC.2019.8789952
2019 IEEE Congress on Evolutionary Computation (CEC)
Keywords
Field
DocType
autonomous aerial refueling (AAR),unmanned aerial vehicles (UAVs),pose estimation,brain storm optimization (BSO),binocular camera system
Computer vision,Computer science,Visualization,Storm,Feature extraction,Pose,Artificial intelligence,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-7281-2154-3
0
0.34
References 
Authors
9
6
Name
Order
Citations
PageRank
Cong Zhang114926.42
Xiaobin Xu214522.74
Yuhui Shi34397435.39
Yimin Deng485.73
Cong Li573.15
Hai-Bin Duan624321.03