What you see in the video is an automated camera calibration process developed by our computer vision team. It enables robots or unskilled operators to retrieve intrinsic and distortion parameters of excellent quality using a minimal set of camera shots. In this case the chessboard is moved manually, but having a 6DoF robot wouldn’t change much.

After grabbing 3 initial shots of the chessboard with free poses, the tool iteratively suggests the next best pose of the chessboard in order to maximize the quality of the estimation over the intrinsic and the distortion parameters.

In the upper right part of the view there is the plot of the reprojection error measured on an independent test set of images (red line: 95% quantile, blu line: mean); the error reaches a stable (and excellent) value with as low as 6 images. Our tests demonstrate that with common optical configurations, the automated process ends up producing a calibration similar to the one produced by the skilled operator, but with a lower number of images. However when the optical specs become challenging (especially for focal length or distortion parameters), the automated process outperforms the skilled engineer.