Binocular vision navigation algorithm for AAR of flying boom UAV

Bao Jiyu; Wang Long; Dong Xinmin

doi:10.5768/JAO201738.0602002

Journal of Applied Optics > 2017 > 38(6): 910-916. > DOI: 10.5768/JAO201738.0602002

Bao Jiyu, Wang Long, Dong Xinmin. Binocular vision navigation algorithm for AAR of flying boom UAV[J]. Journal of Applied Optics, 2017, 38(6): 910-916. DOI: 10.5768/JAO201738.0602002

Citation:

Bao Jiyu, Wang Long, Dong Xinmin. Binocular vision navigation algorithm for AAR of flying boom UAV[J]. Journal of Applied Optics, 2017, 38(6): 910-916. DOI: 10.5768/JAO201738.0602002

Citation:

Bao Jiyu, Wang Long, Dong Xinmin. Binocular vision navigation algorithm for AAR of flying boom UAV[J]. Journal of Applied Optics, 2017, 38(6): 910-916. DOI: 10.5768/JAO201738.0602002

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Binocular vision navigation algorithm for AAR of flying boom UAV

College of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi'an 710038, China

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Received Date: November 27, 2016
Revised Date: May 24, 2017

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Abstract

Abstract

Aiming at the estimation problem of relative position and attitude of autonomous aerial refueling(AAR) of flying boom UAV in close formation stage, the relative pose estimation algorithm based on binocular vision is studied. The algorithm uses Harris method to extract feature points and quickly matches them. The 3D coordinates of feature points in camera coordinate system are obtained by Sampson method, and the objective function is established with the minimum square sum of reconstructed error. The position and pose parameters are solved by unit quaternion method. Finally simulation platform is used to verify the effectiveness of the algorithm. Results show the relative position error is better than 0.1 m, the relative attitude error is less than 0.5°, and the accuracy meets the requirements of AAR relative navigation performance.
- unmanned aerial vehicle,
- AAR,
- binocular vision,
- pose estimation

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References (14)

References

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Binocular vision navigation algorithm for AAR of flying boom UAV