Noise analysis in camera calibration

The main goal of this paper is to demonstrate the noise analysis techniques for camera calibration in the context of the theory of total least squares, and to make clear that robust and reliable calibration algorithms cannot be designed without a thorough statistical consideration of the consequences of errors in the input data. We focus on the non-iterative linear algorithm for estimating the camera parameters and present a comprehensive statistical derivation of the compelling necessity for the normalization transforms. It turns out that without these transformations the results of the well-known non-iterative methods for camera calibration are biased and inconsistent. With some further improvements proposed in this paper, the quality of the algorithm can be further enhanced