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

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...

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Bibliographic Details
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
Online Access:Request full text
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Summary: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.
ISSN:2153-0858
2153-0866
DOI:10.1109/IROS.2010.5649690