Autonomous Stereo Camera Parameter Estimation for Outdoor Visual Servoing
Bibliographic record
Abstract
In the majority of vision applications, sensor calibration is a prerequisite to proper use of the sensor for both measurement and control. The objective in camera calibration is to estimate a set of parameters to construct a mapping between the 3D position of a target point and its 2D image coordinates. In this paper an autonomous stereo camera calibration technique with applications in industrial outdoor visual servoing systems is presented. The stereo camera model obtained was used to estimate the pose of the target object during the robot servoing process. The heavy-duty stereo camera rig was installed on the torso of an outdoor 3DOF robotic manipulator. An efficient iterative least- squares parameter estimation algorithm was used to estimate the transformation parameters between the 3D world coordinates of the target object and its 2D image coordinates in the stereo image planes. The stereo camera calibration is entirely an autonomous process as the robot moves the calibration tool within its workspace and the stereo camera model is produced after the data collection process. The stereo cameras are treated as a single unit and a single transformation is obtained for the stereo camera pair in the system. The calibration process is fast, efficient and no human interaction is required during the process.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".