Neuromorphic vergence eye movement control of binocular robot vision

Abstract

We describe the reliable vergence eye movement control of a binocular robot vision system based on a disparity computation in the primary visual cortex (V1). The system consists of two silicon retinas, simple cell chips, and an FPGA. The silicon retinas emulate a Laplacian-Gaussian (∇ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> G)-like receptive field of the vertebrate retina. The simple cell chips generate an orientation-selective receptive field by aggregating multiple pixels of the silicon retina, mimicking the Hubel and Wiesel type feed-forward model. The FPGA receives the outputs from the two simple cell chips and calculates the responses of complex cells based on the disparity energy model. This system provides complex cell outputs tuned to five different disparities in real-time. The vergence control signal is obtained by pooling these multiple complex cell responses. We thus demonstrate that the present system can robustly execute vergence movement even in a complex scene.