DIRECTION OF MOTION-BASED OPTICAL NAVIGATION USING DEEP LEARNING: METHODS AND RESULTS FROM DAWN AT VESTA

Direction of Motion (DoM) observables, extracted from landmarks tracked across successive image pairs, can provide valuable information to support spacecraft orbit determination. Unlike standard optical navigation methods, this approach does not require prior knowledge of the target’s shape model or georeferenced landmark catalogs, making it suitable for the exploration of uncharted bodies. In this study, deep learning techniques are integrated into the navigation framework to detect and match keypoints across successive acquisitions, enabling DoM estimation. The developed framework is designed to process DoM data, also in combination with other measurement types. By using real data from NASA’s Dawn mission at Vesta, we demonstrate that DL-derived DoM observables, combined with radiometric tracking data, can significantly enhance trajectory reconstruction in scenarios with limited tracking coverage.