The Use of Digital Terrain Models for Natural Feature Tracking at Asteroid Bennu
Bibliographic record
Abstract
Abstract The Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) mission rendezvoused with asteroid (101955) Bennu in 2018 with the primary objective of collecting a sample of regolith from the surface. As the first NASA asteroid sample return mission, OSIRIS-REx deployed several new technologies to achieve program objectives. Here we present an overview of Natural Feature Tracking (NFT), a system developed to autonomously guide the spacecraft to the desired sampling site using optical navigation and the natural terrain on the surface of Bennu. NFT utilized a series of image-based digital terrain models (DTMs) constructed by means of stereophotoclinometry to represent patches on the surface of the asteroid. These DTMs were used to generate synthetic renderings of the terrain and identify features for use in navigating to the sampling location. In addition, high-resolution models of the sampling site constructed from scanning lidar data were used for predicting the time and location of contact with the surface. These models went through a series of validation tests to ensure the performance of the NFT system. When the spacecraft executed the sampling trajectory in 2020 October, NFT enabled real-time guidance updates that delivered it safely to the desired sampling location while also providing critical hazard avoidance capabilities in the rocky Bennu environment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".