Full-scale testing and platform stabilization of a scanning lidar system for planetary landing
Bibliographic record
Abstract
In August 2007, the engineering model of the Rendezvous Lidar System (RLS) was tested at the Sensor Test Range Facility that has been developed at NASA Langley Research Center for the calibration and characterization of 3-D imaging sensors. The three-dimensional test pattern used in this characterization is suitable for an empirical verification of the resolving capability of a lidar for both mid-range terminal rendezvous and hazard avoidance landing. The results of the RLS lidar measurements are reported and compared with image frames generated by a lidar simulator with an Effective Instantaneous Field of View (EIFOV) consistent with the actual scanning time-of-flight lidar specifications. These full-scale tests demonstrated the resolving capability of the lidar under static testing conditions. In landing operations, even though the lidar has a very short exposure time on a per-pulse basis, the dynamic motion of a lander spacecraft with respect to the landing site will cause pulse-to-pulse imaging distortion. MDA, Optech, and NGC Aerospace have teamed together to resolve this issue using motion compensation (platform stabilization) and motion correction (platform residual correction) techniques. Platform stabilization permits images with homogenous density to be generated so that no safe landing sites will be missed; platform residual errors that are not prevented by this stabilization are then corrected in the measurement data prior to map generation. The results of recent developments in platform stabilization and motion correction are reported and discussed in the context of total imaging error budget.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".