Reliable and good camera calibration based on machine learning inspired workflow: parameter study and experimental results

S. Reichel; P. Frank; K. Blankenbach

doi:10.1117/12.3000846

13 March 2024 Reliable and good camera calibration based on machine learning inspired workflow: parameter study and experimental results

S. Reichel, P. Frank, K. Blankenbach

Author Affiliations +

Proceedings Volume 12903, AI and Optical Data Sciences V; 129030E (2024) https://doi.org/10.1117/12.3000846
Event: SPIE OPTO, 2024, San Francisco, California, United States

Abstract

Non-contact measurements using digital cameras require a reliable camera calibration typically based on a pinhole camera model with a few lower-order distortions (typically up to 14 parameters in OpenCV). The assessment of the calibration quality is typically judged by the re-projection error (RPE). We use the forward propagation error (FPE) that determines the deviation in real world coordinates using parameters from the camera calibration. With our machine learning-inspired workflow we identify possible outliers for a more reliable camera calibration. We explore the quality of our camera calibration using RPE and FPE by a series of active (emissive display) and passive (illuminated print paper) checkerboard pattern as well as active cosine phase shifting patterns. We compare different camera models, different pattern, number of grid points, and different distances for the phase shifting pattern by comparing results from our simulations and experiments. We found that the 5 parameter OpenCV model was sufficient for a “good” camera calibration. In addition, the active checkerboard pattern displayed on a monitor is better than a passive checkerboard mounted on a stiff flat plate. Both, the active checkerboard and the active phase shifting pattern, are only limited by our target UHD monitor with about 0.37 mm pixel pitch in terms of FPE. We found that both active patterns give a good camera calibration by a correct generation of poses. The active checkerboard pattern shows good results of 0.16 pxl (RPE) and 0.06 mm (FPE) and can easily be interpreted with the FPE. Both values (RPE and FPE) are lower than the “real world uncertainty”.

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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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S. Reichel, P. Frank, and K. Blankenbach "Reliable and good camera calibration based on machine learning inspired workflow: parameter study and experimental results", Proc. SPIE 12903, AI and Optical Data Sciences V, 129030E (13 March 2024); https://doi.org/10.1117/12.3000846

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