Obstacle Detection for Low Flying Unmanned Aerial Vehicles Using Stereoscopic Imaging
Bibliographic record
Abstract
This paper describes a stereoscopic imaging algorithm that is modified for obstacle detection in low flying unmanned aerial vehicles (UAVs). In this type of flight, obstacle detection must be carried out quickly for the system to be effective in real time. Additionally, since the aircraft is close to the ground, the horizon is usually at the top of the field of view and obstacles must be distinguished from the clutter of the terrain. The sparse edge detection and reconstruction algorithm proposed, produces fast but partial reconstructions of the environment. One image is passed through a series of edge detectors to generate a very sparse outline of the environment. This outline is then correlated to the second image and the resulting reconstruction is added to a model of the environment. Although each individual reconstruction is incomplete, the overall result after a short initialization period is a model of the environment that is more comprehensive than a single stereoscopic correlation run with a more detailed edge detector. Simulation of the algorithm on test image patterns showed an increase in performance relative to the length of the sequence of stereo pairs. On average, the signal to noise ratio (SNR) for sparse edge reconstruction was significantly higher than that for single correlation with more detailed edge detectors. Additionally it was found that the processing speed of the sparse edge detection algorithm on a pair of stereoscopic images is faster than the processing carried out by a more detailed edge detector. A test flight was also carried out to test the algorithm in a more realistic scenario. The test confirmed that the sparse edge reconstruction algorithm resulted in a much more detailed view of the environment than if a single, more detailed edge detector had been used.
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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 | 0.001 | 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) | 0.000 | 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".