The relative pose estimation of an inactive spacecraft by an active servicer spacecraft is a critical task in the design of current and planned space missions, due to its relevance for close-proximity operations, such as In-Orbit Servicing and Active Debris Removal. This paper introduces a novel framework to enable robust monocular pose estimation for close-proximity operations around an uncooperative spacecraft, which combines a Convolutional Neural Network (CNN) for feature detection with a Covariant Efficient Procrustes Perspective-n-Points (CEPPnP) solver and a Multiplicative Extended Kalman Filter (MEKF). The performance of the proposed method is evaluated at different levels of the pose estimation system. A Single-stack Hourglass CNN is proposed for the feature detection step in order to decrease the computational load of the Image Processing (IP), and its accuracy is compared to the standard, more complex High-Resolution Net (HRNet). Subsequently, heatmaps-derived covariance matrices are included in the CEPPnP solver to assess the pose estimation accuracy prior to the navigation filter. This is done in order to support the performance evaluation of the proposed tightly-coupled approach against a loosely-coupled approach, in which the detected features are converted into pseudomeasurements of the relative pose prior to the filter. The performance results of the proposed system indicate that a tightly-coupled approach can guarantee an advantageous coupling between the rotational and translational states within the filter, whilst reflecting a representative measurements covariance. This suggest a promising scheme to cope with the challenging demand for robust navigation in close-proximity scenarios. Synthetic 2D images of the European Space Agency's Envisat spacecraft are used to generate datasets for training, validation and testing of the CNN. Likewise, the images are used to recreate a representative close-proximity scenario for the validation of the proposed filter.

The relative pose estimation of an inactive target by an active servicer spacecraft is a critical task in the design of current and planned space missions, due to its relevance for close-proximity operations, i.e. the rendezvous with a space debris and/or in-orbit servicing. Pose estimation systems based solely on a monocular camera are recently becoming an attractive alternative to systems based on active sensors or stereo cameras, due to their reduced mass, power consumption and system complexity. In this framework, a review of the robustness and applicability of monocular systems for the pose estimation of an uncooperative spacecraft is provided. Special focus is put on the advantages of multispectral monocular systems as well as on the improved robustness of novel image processing schemes and pose estimation solvers. The limitations and drawbacks of the validation of current pose estimation schemes with synthetic images are further discussed, together with the critical trade-offs for the selection of visual-based navigation filters. The state-of-the-art techniques are analyzed in order to provide an insight into the limitations involved under adverse illumination and orbit scenarios, high image contrast, background noise, and low signal-to-noise ratio, which characterize actual space imagery, and which could jeopardize the image processing algorithms and affect the pose estimation accuracy as well as the navigation filter's robustness. Specifically, a comparative assessment of current solutions is given at different levels of the pose estimation process, in order to bring a novel and broad perspective as compared to previous works.