Vergence control for robotic heads using log-polar images

Abstract

This paper describes a real-time vergence control mechanism based on, log-polar images, developed for a robot head. The real-time control of active vision systems imposes strong constraints on the computational complexity of the vision algorithms. In this paper, we illustrate that vergence of a stereo head can be achieved at reduced computational cost using log-polar images. These images have higher resolution at the center, where the attention is focused on, and the rest of the visual field is covered at a coarser resolution, still enabling the detection of events at the image periphery. The main advantages of using a non-uniform image sampling mechanism, such as the log-polar images, are related both to perceptual and algorithm complexity issues. We show that, when using correlation measures to control vergence, log-polar images give better results than cartesian images. Additionally, as log-polar images are smaller, the computation time is reduced. Two algorithms for closed loop vergence control, using correlation measures over log-polar images, are proposed. In the test examples described in the paper, we compare the two algorithms and address the problem of designing an adequate log-polar sensor, by introducing a performance analysis to evaluate the various log-polar sensor layouts.