Real time vision for robotics using a moving fovea approach with multi resolution

We propose a new approach to reduce and abstract visual data useful for robotics applications. Basically, a moving fovea in combination with a multi-resolution representation is created from a pair of input images given by a stereo head, that reduces hundreds of times the amount of information from the original images. With this new theoretical approach we are able to compute several feature maps, including several filters, stereo matching, and motion, in real time, that is at more than 30 frames per second. As the main contribution, the moving fovea allows, most of the time, a robot to avoid performing physical motion with the cameras in order to get a desirable region in the images center. We present mathematical formalization of the moving fovea approach, the algorithms, and details of the implementation of such schema. We validate it with experimental results. This approach has demonstrated to be very useful to robotics vision.