Towards Full Omnidirectional Depth Sensing Using Active Vision for Small Unmanned Aerial Vehicles
Bibliographic record
Abstract
Collision avoidance for small unmanned aerial vehicles operating in a variety of environments is limited by the types of available depth sensors. Currently, there are no sensors that are lightweight, function outdoors in sunlight, and cover enough of a field of view to be useful in complex environments, although many sensors excel in one or two of these areas. We present a new depth estimation method, based on concepts from multi-view stereo and structured light methods, that uses only lightweight miniature cameras and a small laser dot matrix projector to produce measurements in the range of 1-12 meters. The field of view of the system is limited only by the number and type of cameras/projectors used, and can be fully Omni directional if desired. The sensitivity of the system to design and calibration parameters is tested in simulation, and results from a functional prototype are presented.
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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 | 0.001 |
| 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".