Landing System Development Based on Inverse Homography Range Camera Fusion (IHRCF)
Bibliographic record
Abstract
The Unmanned Aerial Vehicle (UAV) is one of the most remarkable inventions of the last 100 years. Much research has been invested in the development of this flying robot. The landing system is one of the more challenging aspects of this system's development. Artificial Intelligence (AI) has become the preferred technique for landing system development, including reinforcement learning. However, current research is more focused is on system development based on image processing and advanced geometry. A novel calibration based on our previous research had been used to ameliorate the accuracy of the AprilTag pose estimation. With the help of advanced geometry from camera and range sensor data, a process known as Inverse Homography Range Camera Fusion (IHRCF), a pose estimation that outperforms our previous work, is now possible. The range sensor used here is a Time of Flight (ToF) sensor, but the algorithm can be used with any range sensor. First, images are captured by the image acquisition device, a monocular camera. Next, the corners of the landing landmark are detected through AprilTag detection algorithms (ATDA). The pixel correspondence between the image and the range sensor is then calculated via the calibration data. In the succeeding phase, the planar homography between the real-world locations of sensor data and their obtained pixel coordinates is calculated. In the next phase, the pixel coordinates of the AprilTag-detected four corners are transformed by inverse planar homography from pixel coordinates to world coordinates in the camera frame. Finally, knowing the world frame corner points of the AprilTag, rigid body transformation can be used to create the pose data. A CoppeliaSim simulation environment was used to evaluate the IHRCF algorithm, and the test was implemented in real-time Software-in-the-Loop (SIL). The IHRCF algorithm outperformed the AprilTag-only detection approach significantly in both translational and rotational terms. To conclude, the conventional landmark detection algorithm can be ameliorated by incorporating sensor fusion for cameras with lower radial distortion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".