Vision-based pose estimation and relative navigation around uncooperative space objects

Autonomous relative navigation in space has been intensively studied in the last decades due to its vast range of applications. In particular, precise pose and motion estimation of an uncooperative object, such as a Resident Space Object (RSO) has a potential utilization in the domain of space debris removal. This paper investigates the possibility to combine image processing and pose estimation techniques with robust filtering methods. The proposed approach is composed of two main blocks. The first block provides estimates of the pose (relative position and attitude) of the observed uncooperative object by processing images, acquired by a monocular camera. Assuming partial knowledge of the shape of the inspected space object (target), classical corner detection techniques are used to extract feature points, which are then used to estimate the target relative position and attitude parameters. This is done by looking for the pose solution which minimizes the error obtained when trying to match the extracted features with the target model. The second block uses these position and attitude data to correct the estimation of the relative state provided by a navigation filter. Among all the possible strategies, Kalman filters have been chosen for their robustness. Their simplicity guarantees very good computational performance, particularly important in real-time estimation applications. Due to the non-linearity of the equations of the relative motion, an Extended Kalman Filter (EKF) is proposed. In addition, due to the high level of uncertainty of this kind of scenario, a robust non-linear filtering approach is used. In particular, the so-called Extended H-1 Filter is tested. This method makes the EKF estimation more robust by using the H-1 techniques, usually applied to linear cases. Numerical simulations are carried out to assess the performance of the overall architecture, proposed for relative state estimation. The results of the classical EKF and of its robust version are critically compared.