Line-scan camera calibration: a robust linear approach

Abstract

Camera calibration requires three steps: estimation of correspondences between world and image coordinates, computation of a linear solution, and nonlinear optimization using the linear estimate as a starting point. The resulting accuracy depends mostly on the first and final steps. However, the nonlinear optimization method can achieve an accurate result only when given an initial estimate close to the global solution. Therefore, obtaining a good linear estimation is crucial for the performance of the camera calibration procedure. This work proposes a robust method to estimate a linear solution for the calibration of line-scan cameras, resulting in individual intrinsic and extrinsic parameters by using only a single line scan. The calculated parameters can then be used by nonlinear optimization methods to finely adjust the estimation of all the line-scan camera parameters, including distortions. The proposed procedure does not impose restrictions on particular orientations, and always generates a well-conditioned problem that can be solved analytically with no optimization required. Extensive experiments are performed to verify the robustness and accuracy of the proposed method. The comparative results demonstrate that the proposed method provides excellent performance.

Geometric transformation modeling for line-scan images under different camera poses

“…6 Currently, line-scan cameras have been widely used in motion measurements of high-speed targets 7,8 and three-dimensional (3D) scene reconstructions. 9,10 In these applications, line-scan camera calibration is an essential step. Several methods for area array camera calibrations have been developed.…”