Automated Eye-in-Hand Robot-3D Scanner Calibration for Low Stitching Errors
Why this work is in the frame
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Bibliographic record
Abstract
A 3D measurement system consisting of a 3D scanner and an industrial robot (eye-in-hand) is commonly used to scan large object under test (OUT) from multiple fieldof-views (FOVs) for complete measurement. A data stitching process is required to align multiple FOVs into a single coordinate system. Marker-free stitching assisted by robot’s accurate positioning becomes increasingly attractive since it bypasses the cumbersome traditional fiducial marker-based method. Most existing methods directly use initial Denavit-Hartenberg (DH) parameters and hand-eye calibration to calculate the transformations between multiple FOVs. Since accuracy of DH parameters deteriorates over time, such methods suffer from high stitching errors (e.g., 0.2 mm) in long-term routine industrial use. This paper reports a new robot-scanner calibration approach to realize such measurement with low data stitching errors. During long-term continuous measurement, the robot periodically moves towards a 2D standard calibration board to optimize kinematic model’s parameters to maintain a low stitching error. This capability is enabled by several techniques including virtual arm-based robot-scanner kinematic model, trajectory-based robot-world transformation calculation, nonlinear optimization. Experimental results demonstrated a low data stitching error (< 0.1 mm) similar to the cumbersome marker-based method and a lower system downtime (< 60 seconds vs. 10-15 minutes by traditional DH and hand-eye calibration).
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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 | 0.001 |
| 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 it