A Vision-based Method for 3D Pose Estimation of Non-cooperative Space Target

Xiaoyuan REN; Libing JIANG; Weijun ZHONG; Zhuang WANG

doi:10.11999/JEIT200440

Volume 43 Issue 12

Dec. 2021

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Xiaoyuan REN, Libing JIANG, Weijun ZHONG, Zhuang WANG. A Vision-based Method for 3D Pose Estimation of Non-cooperative Space Target[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3476-3485. doi: 10.11999/JEIT200440

Citation:

Xiaoyuan REN, Libing JIANG, Weijun ZHONG, Zhuang WANG. A Vision-based Method for 3D Pose Estimation of Non-cooperative Space Target[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3476-3485. doi: 10.11999/JEIT200440

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A Vision-based Method for 3D Pose Estimation of Non-cooperative Space Target

doi: 10.11999/JEIT200440

1.
Key Laboratory on ATR, National University of Defense Technology, Changsha 410073, China
2.
State Key Laboratory of Astronautic Dynamics, Xi’an 724403, China

Funds: The Key Laboratory Foundation of National Defense Technology (6142503180202)

Received Date: 2020-06-01
Rev Recd Date: 2021-03-28

Available Online: 2021-04-29

Publish Date: 2021-12-21

Abstract

Abstract

Establishing correspondence between the target model and the input image is an important step for the pose estimation of non-cooperative space target. Current methods always rely on complex image features and generation of candidate, which can be costly and time consuming. To solve the problems above, this paper proposes a pose estimation method that first conducts initial estimation based on deep neural network and then conducts accurate estimation through correspondence between the known target model and the input image is proposed. The deep neural network provides the stable initial value which reduces the candidates of correspondence between the target model and image. In addition, a more efficient feature extraction and matching method is adopted in this paper instead of complex multi-dimensional features. The simulation results show that the method proposed performs well both in efficiency and accuracy.
- Pose estimation,
- Space target,
- Deep learning,
- Feature matching

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References(19)

References

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