Abstract
With the high focus on autonomous aerial refueling (AAR), it becomes increasingly urgent to design efficient methods or algorithms for solving the AAR problems in complicated aerial environments. A vision-based technology for AAR is developed in this paper, and five monocular and binocular visual algorithms for pose estimation of the unmanned aerial vehicles (UAVs) are adopted and verified in this AAR system. The real-time on-board vision system is also designed for precise navigation in the UAVs docking phase. A series of out-door comparative experiments for different pose estimation algorithms are conducted to verify the feasibility and accuracy of the vision algorithms in AAR.
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References
Sun C H, Duan H B. Markov decision evolutionary game theoretic learning for cooperative sensing of unmanned aerial vehicles. Sci China Tech Sci, 2015, 58: 1392–1400
Wang Y, Wang D B. Variable thrust directional control technique for plateau unmanned aerial vehicles. Sci China Inf Sci, 2016, 59: 033201
Qiu H X, Wei C, Dou R, et al. Fully autonomous flying: from collective motion in bird flocks to unmanned aerial vehicle autonomous swarms. Sci China Inf Sci, 2015, 58: 128201
Hill A C, Rowan Y, Kersel M M. Mapping with aerial photographs. Near Eastern Archaeology, 2014, 77: 182–186
Mancini F, Dubbini M, Gattelli M, et al. Using unmanned aerial vehicles (UAV) for high-resolution reconstruction of topography: the structure from motion approach on coastal environments. Remote Sens, 2013, 5: 6880–6898
Honma T, Kaku K, Usup A, et al. Trop Peatland Ecosyst. Japan: Springer Japan, 2016. 397–406
Barrell J, Grant J. High-resolution, low-altitude aerial photography in physical geography: a case study characterizing eelgrass (zostera marina l.) and blue mussel (mytilus edulis l.) landscape mosaic structure. Prog Phys Geog, 2015, 8: 440–459
Gallagher A. Surveillance UAV. WPI Qualifying Report. 2014
Borck H, Karneeb J, Alford R, et al. Case-based behavior recognition in beyond visual range air combat. In: Proceedings of the 28th International Florida Artificial Intelligence Research Society Conference, Hollywood: AAAI, 2015. 1–7
Zhu Z S, Su A, Liu H B, et al. Vision navigation for aircrafts based on 3D reconstruction from real-time image sequences. Sci China Tech Sci, 2015, 58: 1196–1208
Zhang J J, Xue M, Xie J. Research on assessment method of intersystem and intersystem of the global navigation satellite system. Sci China Tech Sci, 2015, 58: 1672–1681
Hua C S, Qi J T, Shang H, et al. Detection of collapsed buildings with the aerial images captured from UAV. Sci China Inf Sci, 2016, 59:1–15
Chen Z, Di S, Cao Z X, et al. A 256×256 time-of-flight image sensor based on center-tap demodulation pixel structure. Sci China Inf Sci, 2016, 59: 1–10
Liu X C, Wang H, Fu D, et al. An area-based position and attitude estimation for unmanned aerial vehicle navigation. Sci China Tech Sci, 2015, 58: 916–926
Valasek J, Gunnam K, Kimmett J, et al. Vision-based sensor and navigation system for autonomous air refueling. J Guid Control Dynam, 2005, 28: 979–989
Chen C I, Stettner R. Drogue tracking using 3D flash lidar for autonomous aerial refueling. In: Proceedings of Defense, Security, and Sensing. Orlando: SPIE, 2011. 80370Q-80370Q-11
Campa G, Fravolini M L, Ficola A, et al. Autonomous aerial refueling for UAVs using a combined GPS-machine vision guidance. In: Proceedings of Guidance, Navigation, and Control Conference and Exhibit, Providence: AIAA, 2004. 1–11
Doebbler J, Spaeth T, Valasek J, et al. Boom and receptacle autonomous air refueling using visual snake optical sensor. J Guid Control Dynam, 2007, 30: 1753–1769
Kimmett J, Valasek J, Junkins J L. Autonomous aerial refueling utilizing a vision based navigation system. Proceedings of the 2002 Guidance Navigation and Control Conference, Monterey: AIAA. 2002, 1–11
Duan H B, Li H, Luo Q N, et al. A binocular vision-based UAVs autonomous aerial refueling platform. Sci China Inf Sci, 2016, 59: 053201
Munkres J. Algorithms for the assignment and transportation problems. J Soc Ind Appl Math, 1957, 5: 32–38
Lepetit V, Moreno-Noguer F, Fua P. Epnp: An accurate o (n) solution to the pnp problem. Int J Comput Vision, 2009, 81: 155–166
Haralick R M, Joo H, Lee C N, et al. Pose estimation from corresponding point data. IEEE T Syst Man Cy, 1989, 19: 1426–1446
Lu C P, Hager G D, Mjolsness E. Fast and globally convergent pose estimation from video images. IEEE T Pattern Anal, 2000, 22: 610–622
Horn B K P, Hilden H M, Negahdaripour S. Closed-form solution of absolute orientation using orthonormal matrices. J Opt Soc Am A, 1988, 5: 1127–1135
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Li, H., Duan, H. Verification of monocular and binocular pose estimation algorithms in vision-based UAVs autonomous aerial refueling system. Sci. China Technol. Sci. 59, 1730–1738 (2016). https://doi.org/10.1007/s11431-016-6097-z
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DOI: https://doi.org/10.1007/s11431-016-6097-z