LidarBoX: a 3D-printed, open-source altimeter system to improve photogrammetric accuracy for off-the-shelf drones
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
Drones provide a privileged birds’-eye view for collecting high-resolution imagery for morphometric and behavioral sampling of animals. Biologically meaningful measurements extracted from overhead images require an accurate estimate of altitude, but current commercial drones include inaccurate barometer estimates. Recent proposals for coupling altimeter systems to drones have provided customized, open-source solutions, yet assembling such altimeter systems requires advanced technical skills, thereby potentially limiting their use. Here, we built upon recent advances to provide a 3D-printed enclosure for an altimeter system that is inexpensive, self-contained, easy to setup, and transferable across commercial drones. We depart from a published, successful data logger system composed of a GPS and LiDAR sensor and design a more compact and self-powered version (“LidarBoX”) that easily attaches to a variety of commercial drones. We compare flight times with/without LidarBoX attached, test flight maneuverability and performance, and validate the reliability of measurement accuracy. To make LidarBoX accessible, we provide an open-source repository with design code and files and a how-to-assemble guide for non-specialists. We hope this work helps popularize LiDAR altimeter systems on commercial drones to improve the accuracy and reliability of drones as a sampling platform for ecology and wildlife research.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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