Using the disparity space to compute occupancy grids from stereo-vision

• Main Authors: Perrollaz, Mathias, Yoder, John-David, Spalanzani, Anne, Laugier, Christian
• Format: Conference Proceeding
• Language: English
• Subjects: Cameras; Copper; Pixel; Probability density function; Roads; Sensors; Vehicles
• ISSN: 2153-0858; 2153-0866
• DOI: 10.1109/IROS.2010.5649690

The occupancy grid is a popular tool for probabilistic robotics, used for a variety of applications. Such grids are typically based on data from range sensors (e.g. laser, ultrasound), and the computation process is well known. The use of stereo-vision in this framework is less common, and typically treats the stereo sensor as a distance sensor, or fails to account for the uncertainties specific to vision. In this paper, we propose a novel approach to compute occupancy grids from stereo-vision, for the purpose of intelligent vehicles. Occupancy is initially computed directly in the stereoscopic sensor's disparity space, using the sensor's pixel-wise precision during the computation process and allowing the handling of occlusions in the observed area. It is also computationally efficient, since it uses the u-disparity approach to avoid processing a large point cloud. In a second stage, this disparity-space occupancy is transformed into a Cartesian space occupancy grid to be used by subsequent applications. In this paper, we present the method and show results obtained with real road data, comparing this approach with others.