Deep Learning-Optimized Monocular Navigation for Autonomous Rendezvous and Proximity Maneuvers in Small Satellite Missions

Accurate estimation of the position and orientation of a spacecraft during proximity operations—such as rendezvous, docking, on-orbit servicing (OOS), and active debris removal (ADR)—is critical to ensuring mission success and safety. Traditional visual navigation methods based on hand-engineered feature matching often struggle with robustness and generalization, while existing deep learning approaches face limitations due to heuristic hyperparameter tuning and limited training data. In this work, a novel convolutional neural network (CNN)-based architecture for monocular pose estimation of non-cooperative spacecraft is proposed, specifically designed to improve robustness across diverse operational scenarios. The model is trained on a high-fidelity synthetic dataset comprising approximately 25,000 images, simulating realistic proximity conditions with variations in lighting, background textures, and spacecraft geometries. To assess its performance, an extensive benchmarking study is conducted against representative State-of-the-Art methods using standardized evaluation metrics and controlled test conditions. The results demonstrate the competitive performance of the proposed method and provide critical insights into the factors affecting pose estimation accuracy in realistic spaceborne applications.