Performing Improved Two-step Camera Calibration with Weighted Total Least-squares

In order to improve the Tsai’s two-step camera calibration method, we present a camera model which accounts for major sources of lens distortion, namely: radial, decentering, and thin prism distortions. The coordinates of principle points will be calculated at the same time. In the camera calibration model, considering the errors existing both in the observation vector and the coefficient matrix, the Total Least-Squares (TLS) solution is preferred to be utilized. The Errors-In-Variables (EIV) model will be adjusted by the solution within the nonlinear Gauss-Helmert (GH) model here. At the end of the contribution, the real experiment is investigated to demonstrate the improved two-step camera calibration method proposed in this paper. The results show that using the iteratively linearized GH model to solve this proposed method, the camera calibration parameters will be more stable and accurate, and the calculation can be preceded regardless of whether the variance covariance matrices are full or diagonal.