Full-scale testing and platform stabilization of a scanning lidar system for planetary landing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it