Geometric camera calibration using circular control points

TLDR

Modern CCD cameras can achieve spatial accuracy better than 1/50 of a pixel, yet various error sources and the assumption of unbiased observations and a perfect camera model often reduce calibration accuracy. The paper presents a calibration procedure designed for precise 3‑D computer‑vision applications. It introduces bias correction for circular control points and a nonrecursive method for inverting the distortion model, accompanied by an accuracy analysis and discussion of limiting error sources. Synthetic experiments show improved calibration under limited error conditions, while real‑image tests reveal that suppressing external error sources is essential for successful calibration.

Abstract

Modern CCD cameras are usually capable of a spatial accuracy greater than 1/50 of the pixel size. However, such accuracy is not easily attained due to various error sources that can affect the image formation process. Current calibration methods typically assume that the observations are unbiased, the only error is the zero-mean independent and identically distributed random noise in the observed image coordinates, and the camera model completely explains the mapping between the 3D coordinates and the image coordinates. In general, these conditions are not met, causing the calibration results to be less accurate than expected. In the paper, a calibration procedure for precise 3D computer vision applications is described. It introduces bias correction for circular control points and a nonrecursive method for reversing the distortion model. The accuracy analysis is presented and the error sources that can reduce the theoretical accuracy are discussed. The tests with synthetic images indicate improvements in the calibration results in limited error conditions. In real images, the suppression of external error sources becomes a prerequisite for successful calibration.