A self-calibration approach to extrinsic parameter estimation of stereo cameras

A self-calibration technique is proposed in this paper to estimate extrinsic parameters of a stereo camera system. This technique does not require external 3D measurements of precision calibration points. Furthermore, it is conceptually simple and easy to implement. It has applications in such areas as autonomous vehicle navigation, robotics and computer vision. The proposed approach relies solely on distance measurements of a fixed-length object, say a stick. While the object is moved in the 3D space, the image coordinates of the object end points are extracted from the image sequence. A cost function that relates unknown parameters to measurement residuals is formulated. A nonlinear least squares algorithm is then applied to compute the parameters by minimizing the cost function, using the measured image coordinates and the known length of the object. Simulation studies in this papers answer questions such as the number of iterations needed for the algorithm to converge, the number of measurements needed for a robust estimation, singularity cases, and noise sensitivities of the algorithm