State, shape, and parameter estimation of space objects from range images

An architecture for the estimation of dynamic state, geometric shape, and model parameters of objects in orbit using on-orbit cooperative 3-D vision sensors is presented. This has application in many current and projected space missions, such as automated satellite capture and servicing, debris capture and mitigation, and large space structure assembly and maintenance. The method presented here consists of three parts: (1) kinematic data fusion, which condenses sensory data into coarse kinematic surrogate measurements; (2) Kalman filtering, which filters these surrogate measurements and extracts the full dynamic state and model parameters of the target; and (3) shape estimation, which uses filtered pose information and the raw sensory data to build a body-fixed probabilistic map of the target's shape. This method does not rely on feature detection, optical flow, or model matching, but rather exploits the well-modeled dynamics of objects in space using the Kalman filter. The architecture is computationally fast since only coarse measurements need to be provided to the Kalman filter. This paper illustrates the three steps of the architecture in the context of rigid body (satellite and debris) estimation and flexible structure estimation.