Calibration of a stereoscopic vision system in the presence of errors in pitch angle
Date
2022Abstract
This paper proposes a novel method for the calibration of a stereo camera system used
to reconstruct 3D scenes. An error in the pitch angle of the cameras causes the reconstructed scene
to exhibit some distortion with respect to the real scene. To do the calibration procedure, whose
purpose is to eliminate or at least minimize said distortion, machine learning techniques have been
used, and more specifically, regression algorithms. These algorithms are trained with a large number
of vectors of input features with their respective outputs, since, in view of the application of the
procedure proposed, it is important that the training set be sufficiently representative of the variety
that can occur in a real scene, which includes the different orientations that the pitch angle can take,
the error in said angle and the effect that all this has on the reconstruction process. The most efficient
regression algorithms for estimating the error in the pitch angle are derived from decision trees and
certain neural network configurations. Once estimated, the error can be corrected, thus making the
reconstructed scene appear more like the real one. Although the authors base their method on U-V
disparity and employ this same technique to completely reconstruct the 3D scene, one of the most
interesting features of the method proposed is that it can be applied regardless of the technique used
to carry out said reconstruction.