The resurgence of interest in landing on the Moon has sparked the creation of a number of novel technologies concerning Terrain-Relative Navigation (TRN) algorithms. They aid in the need for increasingly precise landing, as well as ensuring fully autonomous operations. To achieve this, most technologies use a ubiquitous feature present on the Moon: impact craters. This research describes the design, development, and testing of an end-to-end TRN system demonstrator that utilises a novel region-based Crater Detection Algorithm (CDA) based on Ellipse R-CNN together with a projective invariant-based crater pattern matching technique that allows for robust ego-position estimation. The system was tested using physically accurate camera views of the Lunar surface. The development of this demonstrator yielded three additions to the field of TRN: a flexible data generation pipeline, a novel AI-based CDA, and results of a novel region-based model in tandem with a modern crater pattern matching technique.