Data-Driven Image Processing for Onboard Optical Navigation Around a Binary Asteroid

Abstract

In this work data-driven image processing options for a CubeSat mission around a binary asteroid system are investigated. The methods considered belong to two main branches of image processing methods: centroid and artificial intelligence. The former is represented by three variations of centroiding methods, and the latter by three neural networks and one convolutional neural network. The first contribution of this work is an enhanced center of brightness method with a data-driven scattering law. This method is demonstrated to share similarities with neural networks in terms of both design and performance, with the advantage of relying on a traditional, robust, and fully explainable algorithm. The second contribution is given by the performance assessment between the different families of image processing methods. For this purpose, the Milani mission is considered as a case study: a 6U CubeSat that will visit the Didymos system as part of the Hera mission. From this analysis, it emerges that convolutional networks perform better than other methods across all metrics considered. This hints to the importance of filtering techniques to extract spatial information from images, which is a unique feature of the convolutional approach over the other image processing methods considered.