Abstract A Robotic Active Debris Removal (ADR) mission requires close-proximity operations for capturing potentially tumbling, uncooperative targets and disposing of them. These operations rely on accurate relative navigation between the chaser and the target to safely perform the mission. In the case of a known piece of debris that has not been designed to be serviced or disposed of, the knowledge of its shape factor allows relative pose determination. In the case of unknown space debris, however, no relative pose determination is possible as no reference is available beforehand. In this paper, we derive an onboard software architecture for estimating the shape and dynamical model of an unknown, uncooperative space debris, and perform tracking, using features detected in the image and the associated depth. Firstly, an Error-State Kalman filter (ESKF) is used to estimate the chaser trajectory and attitude as well as the target shape in an arbitrarily fixed target frame. The next step in the architecture is to apply a Gaussian Processes (GPs) algorithm to model the rotational dynamics of the debris, allowing an accurate attitude propagation independently of its tumbling mode. Finally, a second ESKF is instantiated using existing knowledge to track the position and attitude of the target. This work assumes the pre-existence of a feature detection, tracking and matching algorithm, and fusing data from a depth sensor allowing the retrieval of pixel coordinates and depth for each detected feature. The proposed architecture has been tested and validated in a computer-simulated environment with parameters representative of a real-world ADR mission. Results from over 100 simulations show a mean position and attitude error of 0.3 m and 1.5°, respectively.

Highlights • Dealing with an unknown space debris requires building knowledge on the go. • A multi-stage architecture is presented for onboard estimation of such objects. • A machine learning algorithm is used to predict the tumbling of the debris without inertia. • Simulations shows a position and attitude accuracy under 1 m and 2° respectively.