Autonomous Navigation Performance Using Natural Feature Tracking during the OSIRIS-REx Touch-and-Go Sample Collection Event
Bibliographic record
Abstract
Abstract When the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) spacecraft collected a sample of surface material from asteroid Bennu in 2020 October, it was the first time that an autonomous optical navigation system relying on natural terrain features had been used to guide a spacecraft to a planetary surface. This system, called Natural Feature Tracking (NFT), works by rendering features from digital terrain models and then correlating them with the terrain in real-time navigation images to estimate the spacecraft's position and velocity with respect to the asteroid. Here we describe how the OSIRIS-REx mission built the catalog of features for NFT and how those features performed during rehearsals for and execution of the Touch-and-Go (TAG) sample collection event. Feature performance (quality and accuracy of match) in the rendering and correlation process is the basis of the NFT measurement. All features scored well above the minimum correlation threshold thanks to the effort invested in selecting and modeling them. Residuals across the TAG trajectory were small, indicating that features in the catalog were defined consistently relative to each other. NFT delivered the spacecraft to within 1 m of the targeted location, with a difference of only 3.5 cm and 1.4 s from the predicted location and time of touch. This exceptional performance was crucial for spacecraft safety given Bennu's rough and hazardous terrain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".