A nonparametric learning approach to vision based mobile robot localization

A nonparametric learning algorithm is used to build a robust mapping between an image obtained from a mobile robot´s on-board camera, and the robot´s current position. The mapping uses 19,200 unprocessed pixel values (160 by 120 pixel image). Because the learning algorithm is nonparametric, it uses the learning data obtained from these raw pixel values to automatically choose a structure for the mapping without human intervention, or any a priori assumptions about what type of image features should be used. The learning data consisting of a series example image inputs and corresponding position values, is collected in a calibration phase where the robot randomly traverses its intended workspace. This process of building visual localization maps for mobile robots is completely general and can be applied to any implementation which uses on-board cameras. We demonstrate the feasibility of this approach on a mobile platform performing in a robotics laboratory workspace. This workspace is visually cluttered, with humans and other objects continually moving within the robot´s environment. The mapping learned in this environment is robust to these dynamic visual features and consistently reports timely localization information (at greater than 7 Hz) to within acceptable limits