Uncooperative Objects Pose, Motion and Inertia Tensor Estimation via Stereovision

Autonomous close proximity operations are an arduous and attractive problem in space mission design. In particular, the estimation of pose, motion and inertia properties of an uncooperative object is a challenging task because of the lack of available a priori information. In addition, good computational performance is necessary for real applications. This paper develops a method to estimate the relative position, velocity, angular velocity, attitude and inertia properties of an uncooperative space object using only stereo-vision measurements. The classical Extended Kalman Filter (EKF) and an Iterated Extended Kalman Filter (IEKF) are used and compared for the estimation procedure. The relative simplicity and low computational cost of the proposed algorithm allow for an online implementation for real applications. The developed algorithm is validated by numerical simulations in MATLAB using different initial conditions and uncertainty levels. The goal of the simulations is to verify the accuracy and robustness of the proposed estimation algorithm. The obtained results show satisfactory convergence of the estimation errors for all the considered quantities. An analysis of the computational cost is addressed to confirm the possibility of an onboard application. The obtained results, in several simulations, outperform similar works present in literature. In addition, a video processing procedure is presented to reconstruct the geometrical properties of a body using cameras. This method has been experimentally validated at the ADAMUS (ADvanced Autonomous MUltiple Spacecraft) Lab at the University of Florida.