A linear iterative method for auto-calibration using the DAC equation

In this paper, an iterative algorithm for auto-calibration is presented. The proposed algorithm switches between linearly estimating the dual of the absolute conic and the intrinsic parameters, while also incorporating the rank-3 constraint on the intrinsic parameters. The most important property of the algorithm is that it is completely general in the sense that any type of constraint on the intrinsic parameters might be used. The proposed algorithm locates in-between of a non-linear optimization and initial linear computation, and provides robust and sufficiently accurate initial values for a bundle adjustment routine. The performance of the algorithm is shown for both simulated and real data, especially in the important case of natural (zero skew and unit aspect ratio) cameras.

1 Introduction

During the last years there has been an intensive research on the possibility to obtain reconstructions up to an unknown similarity transformation (often called Euclidean reconstructions) without using fully calibrated cameras. In this case, it is necessary to have some additional information about either the intrinsic parameters, the extrinsic parameters or the object in order to obtain the desired Euclidean reconstruction. One way is to use a camera with constant intrinsic parameters, so called self-calibration, which leads to the so called Kruppa equations [3], [5].