This paper presents a fault-tolerant method for pose estimation of space objects using 3-D vision data by integration of a Kalman filter (KF) and an Iterative Closest Point (ICP) algorithm in a closed-loop configuration. The initial guess for the internal ICP iteration is provided by state estimate propagation of the Kalman filer. The Kalman filter is capable of not only estimating the target's states, but also its inertial parameters. This allows the motion of target to be predictable as soon as the filter converges. Consequently, the ICP can maintain pose tracking over a wider range of velocity due to increased precision of ICP initialization. Furthermore, incorporation of the target's dynamics model in the estimation process allows the estimator continuously provide pose estimation even when the sensor temporally loses its signal namely due to obstruction. The capabilities of the pose estimation methodology is demonstrated by a ground testbed for Automated Rendezvous & Docking (AR&D).
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