Lidar for obstacle detection during helicopter 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
Helicopter pilots in military and civilian operations need visual assistance for safe flight and landing under adverse conditions, especially during white-out condition or brown-out condition, in which it is difficult for a pilot to see obstacles or ground through snow or dust generated by the helicopter's rotorwash. There have been intensive efforts to develop a sensor that can detect obstacles or ground inside aerosols in recent years. LIDAR can use the gating function of timing discrimination to suppress the effect of scattering from aerosols, it can generally "see" farther than passive sensors such as human eyes and cameras inside aerosols. The challenge of using a LIDAR under aerosol conditions is not only the requirement of high laser power for penetrating aerosols, but also the requirement of high detection dynamic range and the suppression of aerosol scattering in front of a LIDAR. Neptec's Obscurant Penetrating Autosynchronous LIDAR (OPAL) uses an autosynchronized optical design, which utilizes a triangulation relationship to control the amount of return beam accepted by the TOF (time-of-flight) receiver as a function of target range. The design also maintains this property during high-speed optical scanning. As a result, OPAL can suppress the return signals from nearby aerosol scattering and, at the same time, have a sensitivity and dynamic range to detect obstacles or ground inside aerosol. Neptec has conducted experiments to study the effect of atmospheric aerosol scattering on LIDAR, FLIR and human vision by using a propagation and aerosol evaluation corridor. Neptec has also carried out flight tests of a prototype of OPAL on a NRC Bell 412 helicopter. In this paper, the concept of the OPAL that is uniquely designed to penetrate aerosols will be described and its applications in helicopter landing will be discussed.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it