Centroid regression using CNN-based Image Processing Algorithm with application to a binary asteroid system

Autonomous Optical Navigation is essential for the proximity operations of space missions to asteroids that usually have irregular gravity fields. One core component of this navigation strategy is the Image Processing (IP) algorithm that extracts optical observables from images captured by the spacecraft's on-board camera. Among these observables, the centroid of the asteroid is important to determine the position between the spacecraft and the body, which is the focus of this research. However, the performance of standard IP algorithms is affected and constrained by the features of the images, such as the shape of the asteroid, the illumination conditions and the presence of additional bodies, therefore, the quality of the extracted optical observables is influenced. To address the latter two challenges, this paper develops a Convolutional Neural Networks (CNN)-based IP algorithm and applies it to the Early Characterization Phase (ECP) of the European Space Agency's HERA mission with the target body of binary asteroid Didymos. This algorithm is capable of estimating the centroid of the primary body successfully with high accuracy and without being affected by the presence of the secondary body or the illumination in the input images. In addition, it can also estimate the centroid of the secondary body when the two bodies are in the same image, which increases the robustness of the overall navigation strategy.