Vision-based qualitative path-following control of quadrotor aerial vehicle
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a vision-based qualitative 3D navigation technique as well as first results of adapting Funnel Lane theory into path-following control of quadrotor aerial vehicle. The image's Kanade-Lucas-Tomasi (KLT) corner features are detected along the reference path in order to build a funnel lane for navigation. Then a funnel-lane navigation calculation is developed to estimate the desired yaw angle and height for the next movement. The proposed algorithm uses the front camera, heading measurement and altimeter of the Ar.Drone quadrotor for navigation. The remarkable advantage of the proposed technique is independently working in GPS-denied environments without the support of the external tracking system as well as computationally efficient. As compared to other available approaches, at-least one matched feature is required during path following. The proposed navigation technique can be implemented for visual-homing, visual-servoing and visual-teach-and-repeat (VT&R) applications. The proposed method is simulated in ROS and Gazebo simulator followed by a realtime experiment with the Ar.Drone quadrotor.
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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