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Content Provider | IEEE Xplore Digital Library |
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Author | Perrollaz, M. Yoder, J.-D. Spalanzani, A. Laugier, C. |
Copyright Year | 2010 |
Description | Author affiliation: INRIA Grenoble, France (Perrollaz, M.; Spalanzani, A.; Laugier, C.) || Ohio Northern University, USA (Yoder, J.-D.) |
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 [1]. 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. |
Starting Page | 2721 |
Ending Page | 2726 |
File Size | 977659 |
Page Count | 6 |
File Format | |
ISBN | 9781424466740 |
ISSN | 21530866 |
e-ISBN | 9781424466764 |
DOI | 10.1109/IROS.2010.5649690 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2010-10-18 |
Publisher Place | Taiwan |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Pixel Sensors Copper Cameras Vehicles Roads Probability density function |
Content Type | Text |
Resource Type | Article |
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