Future asteroid missions require autonomous navigation to handle ephemeris uncertainties, irregular gravity, and communication delays. This thesis demonstrates that realistic simulation through data augmentation enables robust optical learning-based navigation for asteroid proximity operations. A pose estimation pipeline based on deep learning is developed for asteroid 433 Eros. An SSD MobileNetV2-FPN detector localizes the asteroid, an LPN-101 network identifies 129 keypoints, and a CEPPnP solver estimates the six-degree-of-freedom satellite pose from 2D-3D correspondences. Cross-domain evaluation of six synthetic datasets reveals that augmentation-based training is crucial for realistic scenarios. The augmented-trained model achieves optimal performances (meeting <10% position and <5° rotation requirements in 99.78% of cases), while non-augmented models fail against realistic data. Hardware-in-the-loop validation using a 1:100000 mockup confirms sim-to-real transfer with centimeter-level accuracy. Evaluation of actual NEAR-Shoemaker imagery validates the model's ability to generalize to authentic mission data without the need for retraining.