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PROCESSING REAL-TIME STEREO VIDEO IN DISPARITY SPACE FOR OBSTACLE MAPPING

This thesis addresses the problem of the robust and rapid detection and accurate mapping of obstacles in 3-space to a 2D map using live real-time stereo vision. The significance of this work is the novel method of the algorithm, which processes information extremely fast and solely in disparity space and creates a real-time obstacle map. The accuracy and robustness are further improved by floor noise cancellation which features a calibration algorithm that allows expeditious setting of the calibration parameters. • Removing floor-ground background and other unwanted " noise ". • Separating the image into depth planes. • Aggregating column information into smaller usable sets and summing those sets by depth to produce obstacle information at each depth. • Using that information to produce a " live " occupancy grid of objects. Based on this approach, an algorithm is presented with Matlab code that processes still disparity maps to produce obstacle size and location information in a simple format. In addition, an algorithm is also presented in C++ that creates a calibrated obstacle map in real-time using a commercially available stereo camera system.