Computer Vision Systems For Robotic Servicing of the Hubble Space Telescope

Lyn, Craig; Mooney, Gavin; Bush, Doug; Jasiobedzki, Piotr; King, Denise; Krishnasamy, Rubakumar; Ng, Ho-Kong; Ogilvie, Andrew; Pennells, Mark; Pop, Horațiu; Rossi, Stephen J.; Umasuthan, Manickam

doi:10.2514/6.2007-6259

Cited by 9 publications

(2 citation statements)

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“…If stereo vision system tracks these feature points simultaneously during the relative pose estimation, better measurements will be achieved by an averaging operation. Substituting (14) into (13), the observation equations can be rewritten as…”

Section: Measurement Modelmentioning

confidence: 99%

“…Furthermore, the dynamic model can also be used to predict the motion of the target satellite, and then the optimal path planning can be conducted to guide a robotic manipulator to capture the tumbling target at a rendezvous point with the same velocity [11]. Vision system is more and more commonly utilized for the relative pose measurements in the rendezvous missions due to the low cost, low mass, and low energy requirement of CCD/CMOS cameras [12,13]. Nagamatsu et al proposed a 12th order extended Kalman filter to estimate the position and attitude of a torque-free target satellite by using a simplified dynamical model [14].…”

Section: Introductionmentioning

confidence: 99%

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Stereo-Vision-Based Relative Pose Estimation for the Rendezvous and Docking of Noncooperative Satellites

Feng

Qiao

et al. 2014

Mathematical Problems in Engineering

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Autonomous on-orbit servicing is expected to play an important role in future space activities. Acquiring the relative pose information and inertial parameters of target is one of the key technologies for autonomous capturing. In this paper, an estimation method of relative pose based on stereo vision is presented for the final phase of the rendezvous and docking of noncooperative satellites. The proposed estimation method utilizes the sparse stereo vision algorithm instead of the dense stereo algorithm. The method consists of three parts: (1) body frame reestablishment, which establishes the body-fixed frame for the target satellite using the natural features on the surface and measures the relative attitude based on TRIAD and QUEST; (2) translational parameter estimation, which designs a standard Kalman filter to estimate the translational states and the location of mass center; (3) rotational parameter estimation, which designs an extended Kalman filter and an unscented Kalman filter, respectively, to estimate the rotational states and all the moment-of-inertia ratios. Compared to the dense stereo algorithm, the proposed method can avoid degeneracy when the target has a high degree of axial symmetry and reduce the number of sensors. The validity of the proposed method is verified by numerical simulations.

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Section: Measurement Modelmentioning

confidence: 99%