Automatic Wheels and Camera Calibration for Monocular and Differential Mobile Robots
Bibliographic record
Abstract
Mobile robotic systems are highly relevant today in various fields, both in an industrial environment and in terms of their applications in medicine. After assembling the robot, components such as the camera and wheels need to be calibrated. This requires human participation and depends on human factors. The article describes the approach to fully automatic calibration of a robot’s camera and wheels with a subsequent calibration refinement during the operation. It consists of placing the robot in an inaccurate position, but in a pre-marked area, and using data from the camera, information about the environment configuration, as well as the ability to move, in order to perform calibration without external observers or human participation. There are two stages in this process: the camera and the wheel calibrations. The camera calibration collects the necessary set of images by automatically moving the robot in front of the fiducial markers template, and then moving it on the marked floor, assessing its trajectory curvature. Upon calibration completion, the robot automatically moves to the area of its normal operation and it is proposed to refine the calibration during its operation without blocking its work. The suggested approach was experimentally tested on the Duckietown project base. Based on test results, the approach proved to be comparable to manual calibrations and is capable of replacing a human for this task.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".