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Relative Attitude and Position Estimation for Spacecraft from Multiple Geometric Features

Relative Attitude and Position Estimation for Spacecraft from Multiple Geometric Features
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摘要 This paper investigates the pose and motion estimation problem using single camera measurement for spacecraft. The leader spacecraft of three-dimensional shape is observed by a calibrated camera fixed on the follower spacecraft. Based on dual numbers,an integrated observation model is proposed based on a combination of multiple geometric features including points,lines and circles,which can improve the robustness and accuracy of the estimation algorithm. A six-degree-of-freedom relative motion model is proposed by using the dual quaternion representation,in which the rotation-translation coupling effect due to the points deviating from the center of the mass is described. Employing the proposed observation model and dynamics model,an Extended Kalman Filter is presented to estimate the relative state between the two spacecraft. Numerical simulations are performed to evaluate the proposed approaches,showing the convergence of relative estimation errors and superior estimation performance. This paper investigates the pose and motion estimation problem using single camera measurement for spacecraft. The leader spacecraft of three-dimensional shape is observed by a calibrated camera fixed on the follower spacecraft. Based on dual numbers,an integrated observation model is proposed based on a combination of multiple geometric features including points,lines and circles,which can improve the robustness and accuracy of the estimation algorithm. A six-degree-of-freedom relative motion model is proposed by using the dual quaternion representation,in which the rotation-translation coupling effect due to the points deviating from the center of the mass is described. Employing the proposed observation model and dynamics model,an Extended Kalman Filter is presented to estimate the relative state between the two spacecraft. Numerical simulations are performed to evaluate the proposed approaches,showing the convergence of relative estimation errors and superior estimation performance.
出处 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2016年第2期87-96,共10页 哈尔滨工业大学学报(英文版)
关键词 dual QUATERNION COUPLED dynamics VISION-BASED estimation KALMAN FILTERING dual quaternion coupled dynamics vision-based estimation Kalman filtering
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