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

Abstract : 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.
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https://hal.inria.fr/inria-00527785
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Submitted on : Wednesday, October 20, 2010 - 11:48:44 AM
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Mathias Perrollaz, John-David Yoder, Anne Spalanzani, Christian Laugier. Using the disparity space to compute occupancy grids from stereo-vision. International Conference on Intelligent Robots and Systems (IROS), Oct 2010, Taipei, Taiwan. ⟨inria-00527785⟩

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