An ultracompact laser terrain mapper for deployment onboard unmanned aerial vehicles
Bibliographic record
Abstract
Airborne laser terrain mapping systems have redefined the realm of topographic mapping. Lidars with kilohertz collection rates and long ranges have made airborne surveying a quick, efficient and highly productive endeavor. Despite the current industry efforts toward improving airborne lidar range, collection rate, resolution and accuracies, and with the advent of Unmanned Aerial Vehicles (UAVs) and their myriad advantages, military and civil applications alike are looking for very compact and rugged lidar systems that can fit within the tight volumetric, form-factor, mass and power constraints imposed by UAVs. Optech has developed a very compact airborne laser terrain mapper that's geared toward UAV deployment. The system is composed of a highly integrated unit that combines a lidar transceiver, a position orientation sensor and control electronics in a 1 cubic foot - 57 lb package. Such level of compactness is achieved by employing the latest laser technology trends along with featuring very compact optical design, and using the latest control and data collection architecture technology. This paper describes the UAV requirements that drove the system design, the technology employed and optimizations implemented in the system to achieve its ultra-compact size.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".