Obstacle detection for low flying UAS using monocular camera
Why this work is in the frame
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Bibliographic record
Abstract
This paper describes an obstacle detection algorithm for low flying unmanned aircraft system (UAS) using an inertial aided inverse depth Extended Kalman Filter (EKF) framework. The EKF framework fuses inertial measurements with monocular image sensor measurements to estimate the positions of a number of landmarks as well as the position and orientation of the UAS. A high resolution sparse terrain elevation map and UAS trajectory can then be computed from the filter state vector. An inverse depth parameterization is used to describe the position of the landmarks so that features at all ranges can be tracked by the filter. A test flight was conducted to test the algorithm in a realistic scenario. The result shows that the algorithm produces accurate terrain elevation model, and is capable of generating accurate high resolution terrain elevation map when image sensor with high resolution and dynamic range is used.
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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 | 0.000 |
| 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 it