Ground Testing of Digital Terrain Models to Prepare for OSIRIS-REx Autonomous Vision Navigation Using Natural Feature Tracking

• Published in: The planetary science journal 2022-05, Vol.3 (5), p.104
• Main Authors: Mario, C. E., Miller, C. J., Norman, C. D., Palmer, E. E., Weirich, J., Barnouin, O. S., Daly, M. G., Seabrook, J. A., Lorenz, D. A., Olds, R. D., Gaskell, R., Bos, B. J., Rizk, B., Lauretta, D. S.
• Format: Article
• Language: English
• Subjects: Asteroid surfaces; Asteroids
• ISSN: 2632-3338; 2632-3338
• DOI: 10.3847/PSJ/ac5182

The OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer) spacecraft collected a sample from the asteroid Bennu in 2020. This achievement leveraged an autonomous optical navigation approach called Natural Feature Tracking (NFT). NFT provided spacecraft state updates by correlating asteroid surface features rendered from previously acquired terrain data with images taken by the onboard navigation camera. The success of NFT was the culmination of years of preparation and collaboration to ensure that feature data would meet navigation requirements. This paper presents the findings from ground testing performed prior to the spacecraft's arrival at Bennu, in which synthetic data were used to develop and validate the technical approach for building NFT features. Correlation sensitivity testing using synthetic models of Bennu enabled the team to characterize the terrain properties that worked well for feature correlation, the challenges posed by smoother terrain, and the impact of imaging conditions on correlation performance. The team found that models constructed from image data by means of stereophotoclinometry (SPC) worked better than those constructed from laser altimetry data, except when test image pixel sizes were more than a factor of 2 smaller than those of the images used for SPC, and when topography was underrepresented and resulted in incorrect shadows in rendered features. Degradation of laser altimetry data related to noise and spatial sampling also led to poor correlation performance. Albedo variation was found to be a key contributor to correlation performance; topographic data alone were insufficient for NFT.