Pseudo-Doppler Velocity Navigation for Lidar-Based Planetary Exploration
Bibliographic record
Abstract
When landing on a planetary body, the knowledge of the Lander velocity relative to the surface is required in order to ensure a safe touchdown. A conventional Doppler radar can provide this measurement. However, it has been shown in previous studies that Lidar mappers, with their ability to view the landing area in three dimensions, are ideal sensors for hazard-avoidance landing. In order to minimize the power, mass and volume of on-board sensors, this paper proposes to use the Lidar data to estimate the Lander relative velocity. An innovative Lidar-based Pseudo-Doppler velocity determination algorithm is presented with the purpose of determining velocity in conditions of high relative velocity, condition in which techniques based on feature matching may fail because of the distortions created by the Lander motion. The techniques proposed here could estimate the velocity during and at the end of the parachute phase when landing on Mars. The algorithm is described and validated by simulation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".