Disparity Estimation on Log-Polar Images and Vergence Control

An important issue in the realization of an autonomous robot with stereoscopic vision is the control of vergence. Together with version, it determines uniquely the position of the fixation point in space. Vergence control is directly related to both depth perception and binocular fusion. Previous works in this field employed either a measure of correlation of stereo images or some kind of disparity-related estimate. In this paper, we present a new method of extracting a global disparity measure for vergence control, which does not require a priori segmentation of the object of interest. Our method uses images acquired by retina-like sensors and, therefore, the computation is performed in the log-polar plane. The technique we present here is: (i) global, in the sense that it is an integral measure over the whole image, (ii) computationally inexpensive, considering that the goal was to use it in the robot control loop rather than to accurately measure some 3D world features. Moreover, the proposed technique is robust and independent of the average illumination as well as of other features of the target such as size, shape, and direction of motion. It provides a precise and linear estimate of the vergence error, which is the only requirement from the control point of view. Several experimental results on a real robotic setup demonstrate the effectiveness of the proposed technique.