Autonomous Landing Guidance for Quad-UAVs Based on Visual Image and Altitude Estimation
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Bibliographic record
Abstract
In this paper, an autonomous landing guidance strategy is proposed for quad-UAVs, including landing marker detection, altitude estimation, and adaptive landing commands generation. A double-layered nested marker is designed to ensure that the marker can be captured both in high and low altitudes. A deep learning-based marker detection method is designed where the intersection of union is replaced by the normalized Wasserstein distance in the computation of non-maximum suppression to improve the detection accuracy. The UAV altitude measured by inertial measurement unit is fused with vision-based altitude estimation data to improve the accuracy during the landing process. An image-based visual servoing method is designed to guide the UAV approach to the landing marker. Both simulation and flight experiments are conducted to verify the proposed strategy.
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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