Vision-Based Relative Pose Estimation for Autonomous Rendezvous And Docking

Autonomous rendezvous and docking (ARD) plays a very important role in planned space programs such as on-orbit construction and assembly, refueling of satellites, repairing or rescuing failed satellites, active removal of space debris, autonomous re-supply and crew exchange of space stations, and so on. However,the success of ARD rests with the estimation accuracy and efficiency of relative pose among various spacecraft in rendezvous and docking. In this paper, a high accuracy and efficiency estimation algorithm of relative pose of cooperative space targets is presented based on monocular vision imaging, in which a modified gravity model approach and multiple targets tracking methods are employed to improve the accuracy of feature extraction and enhance the estimation efficiency, moreover the Levenberg-Marquardt method (LMM) is used to achieve a well global convergence. The comprehensive experiment results demonstrate its outstanding predominance in estimation accuracy and efficiency.

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Fast Algorithms of Multi-Object Recognition and High Precision Localization for Pose Estimation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.333-335.1192 ◽

2013 ◽

Vol 333-335 ◽

pp. 1192-1197

Author(s):

Ying Jin Zhang ◽

Shi Yin Qin ◽

Xiao Hui Hu

Keyword(s):

Object Recognition ◽

Pose Estimation ◽

Median Filter ◽

Region Growing ◽

Random Errors ◽

Bilinear Interpolation ◽

Visual Measurement ◽

Accurate Localization ◽

Autonomous Rendezvous ◽

Rendezvous And Docking

The recognition and localization of cooperative objects are very important for spacecraft pose estimation towards autonomous rendezvous and docking (RVD). In this paper, an adaptive threshold segmentation algorithm is proposed base on weighted maximum gray value for multi-object detection, and eight-neighborhood region growing is employed for multi-object recognition. In order to achieve high-accurate localization, a sub-pixel object positioning approach is put forward by combination bilinear interpolation with median filtering, which employs bilinear interpolation to calculate sub-pixel centroid for reducing algorithm systematic errors, and applies median filter to reduce random errors produced by image noises. The experimental results show that the proposed algorithms are feasible and effective with high positioning accuracy of 0.01 pixels, and have outstanding advantages of anti-disturbance and real-time performance, thus can satisfy the practical requirements in the visual measurement and pose estimation of cooperative objects for the RVD in space exploration.

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Feature-tracking based pose estimation for autonomous rendezvous and docking

2010 3rd International Symposium on Systems and Control in Aeronautics and Astronautics ◽

10.1109/isscaa.2010.5633267 ◽

2010 ◽

Cited By ~ 3

Author(s):

Daihou Wang ◽

Hanghang Ren ◽

Yiran Wu ◽

Changhong Wang

Keyword(s):

Pose Estimation ◽

Feature Tracking ◽

Autonomous Rendezvous ◽

Rendezvous And Docking

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Autonomous Rendezvous and Docking (AR&D) for future spacecraft missions

10.2514/6.1999-4598 ◽

1999 ◽

Cited By ~ 1

Author(s):

W. Cook ◽

Scott Lindell

Keyword(s):

Autonomous Rendezvous ◽

Rendezvous And Docking

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WTLS-Bayes Filter-based Relative Pose Estimation for Space Non-cooperative Unknown Target Without a Priori Knowledge

Measurement Science and Technology ◽

10.1088/1361-6501/ac3624 ◽

2021 ◽

Author(s):

CHENGGUANG ZHU ◽

zhongpai Gao ◽

Jiankang Zhao ◽

Haihui Long ◽

Chuanqi Liu

Keyword(s):

Pose Estimation ◽

A Priori ◽

Convergence Time ◽

Estimation Accuracy ◽

A Priori Knowledge ◽

One Step ◽

Relative Pose ◽

The One ◽

Negative Factors ◽

Priori Knowledge

Abstract The relative pose estimation of a space noncooperative target is an attractive yet challenging task due to the complexity of the target background and illumination, and the lack of a priori knowledge. Unfortunately, these negative factors have a grave impact on the estimation accuracy and the robustness of filter algorithms. In response, this paper proposes a novel filter algorithm to estimate the relative pose to improve the robustness based on a stereovision system. First, to obtain a coarse relative pose, the weighted total least squares (WTLS) algorithm is adopted to estimate the relative pose based on several feature points. The resulting relative pose is fed into the subsequent filter scheme as observation quantities. Second, the classic Bayes filter is exploited to estimate the relative state except for moment-of-inertia ratios. Additionally, the one-step prediction results are used as feedback for WTLS initialization. The proposed algorithm successfully eliminates the dependency on continuous tracking of several fixed points. Finally, comparison experiments demonstrate that the proposed algorithm presents a better performance in terms of robustness and convergence time.

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