Describe a calibration workflow for a virtual inspection system and explain why each step matters.

• A. Calibrate cameras/scanners with random targets; verify scale; adjust intrinsic/extrinsic parameters; validate with reference measurements; re-run after system changes; ensures accurate geometry
• B. Calibrate cameras/scanners with known targets; verify scale; adjust intrinsic/extrinsic parameters; validate with reference measurements; re-run after system changes; ensures accurate geometry
• C. Only run automated self-calibration; never verify references
• D. Calibrate after field deployment; no re-test

Answer: (B)

Using known calibration targets anchors sensor measurements to real-world dimensions, which is essential for any virtual inspection system to produce trustworthy, metric results. When targets with precisely known geometry are used, you can solve for the camera’s intrinsic parameters (focal length, principal point, lens distortion) and the extrinsic relationships between sensors, establishing a consistent spatial frame and accurate scale across all measurements.

Verifying the scale is crucial because it confirms that the units in your reconstructions match real-world units, preventing systematic size errors in all measurements and alignments. After estimating the parameters, you adjust them to minimize errors between the observed target positions in images or scans and their known positions, tightening the accuracy of the geometric model.

Validating with reference measurements provides an external check against ground truth, ensuring that the reconstructed geometry aligns with reality rather than only matching the calibration setup. Finally, re-running calibration after system changes—such as adding or replacing sensors, altering configurations, or experiencing environmental shifts—ensures any drift or re-alignment is caught and corrected, preserving long-term accuracy.

Options that rely on random targets, on automated self-calibration without external references, or on calibrating only after deployment without re-testing lose the essential ground-truth checks and drift detection that keep the geometry reliable for inspections.