Domain randomisation and CNN-based keypoint-regressing pose initialisation for relative navigation with uncooperative finite-symmetric spacecraft targets using monocular camera images

Abstract

Vision-based relative navigation technology is a key enabler of several areas of the space industry such as on-orbit servicing, space debris removal, and formation flying. A particularly demanding scenario is navigating relative to a non-cooperative target that does not offer any navigational aid and is unable to stabilise its attitude. This research integrates a convolutional neural network (CNN) and an EPnP-solver in a pose initialisation system. The system's performance is benchmarked on images gathered from the European Proximity Operations Simulator EPOS 2.0 laboratory. A synthetic dataset is generated using Blender as a rendering engine. A segmentation-based pose estimation CNN is trained using the synthetic dataset and the resulting pose estimation performance is evaluated on a set of real images gathered from the cameras of the EPOS 2.0 robotic close-range relative navigation laboratory. It is demonstrated that a synthetic-image-trained CNN-based pose estimation pipeline is able to successfully perform in a close-range visual relative navigation setting on real camera images of a 6-facet symmetrical spacecraft.