Post-flight Evaluation of Lidar-based Digital Terrain Models for OSIRIS-REx Navigation at Bennu
Bibliographic record
Abstract
Abstract The Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) spacecraft spent more than 2 yr characterizing near-Earth asteroid (101955) Bennu. The OSIRIS-REx Laser Altimeter (OLA) was responsible for producing the most accurate reconstruction of the asteroid’s surface—down to a global resolution of around 5 cm with a data precision of ±1.25 cm. However, the best-quality global OLA digital terrain model (DTM), version 21 (v21), was not available for navigation during proximity operations, nor was the utility of this model evaluated for processing images and altimeter data for navigation. The focus of this paper is the post-flight assessment of the final OLA v21 DTM, its performance for navigation-related analysis, and estimates of corrections needed for the DTM and measurement models. We created 15 cm resolution maplets for processing optical navigation (OpNav) data, and 5 cm resolution DTMs for processing altimeter data, to estimate a combined spacecraft trajectory over five phases of the mission. Our estimated corrections to the OLA instrument model produce altimeter data residuals with a precision of 7.12 cm (1 σ ; one standard deviation from the mean). The OpNav maplets produce image residuals at 0.2 px (1 σ ) and estimated landmark locations accurate to ±6 cm, outperforming DTM navigation-related performance requirements. Finally, our estimate of the global DTM scale is more precise and within 1.1 σ of previously reported values. We find that a slight discrepancy persists between the image and altimeter data, with image data suggesting that the DTM is too small by 0.049%, but nevertheless is exceptional for navigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".