Ground Testing of Digital Terrain Models to Prepare for OSIRIS-REx Autonomous Vision Navigation Using Natural Feature Tracking
Bibliographic record
Abstract
Abstract 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".