Detection of Long Narrow Landing Features for Autonomous UAV Perching
Bibliographic record
Abstract
It is highly advantageous for Unmanned Aerial Vehicles (UAVs) to be able to perch on natural features or human-made structures in order to conserve power, maintain a fixed position for data collection, or for performing manipulator tasks. Long narrow landing features can commonly be found on industrial structures such as power transmission towers and are convenient for UAVs to perch on due to being easy to grasp and being generally unobstructed by the presence of other nearby objects. A landing feature detection algorithm was developed and implemented using a Microsoft Kinect to provide 3D point cloud data. The algorithm was tested on a scene composed of two step-ladders and several rods joining the two together. The algorithm detects line segments in the point cloud data using Random Sample Consensus (RANSAC), and then evaluates the suitability of the detected lines for landing. The algorithm successfully detected rods which were at a suitable orientation, the correct diameter and length, and which were unobstructed by other objects.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".