Author:
Hara Kosuke, ,Saito Hideo,
Abstract
<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/03.jpg"" width=""300"" /> The localization system</div>For realizing autonomous vehicle driving and advanced safety systems, it is necessary to achieve accurate vehicle localization in cities. This paper proposes a method of accurately estimating vehicle position by matching a map and line segment features detected from images captured by a camera. Features such as white road lines, yellow road lines, road signs, and curb stones, which could be used as clues for vehicle localization, were expressed as line segment features on a two-dimensional road plane in an integrated manner. The detected line segments were subjected to bird’s-eye view transformation to transform them to the vehicle coordinate system so that they could be used for vehicle localization regardless of the camera configuration. Moreover, an extended Kalman filter was applied after a detailed study of the line observation errors for realizing real-time estimation. Vehicle localization was tested under city driving conditions, and the vehicle position was identified with sub-meter accuracy.
Publisher
Fuji Technology Press Ltd.
Subject
Electrical and Electronic Engineering,General Computer Science
Reference23 articles.
1. J. Ziegler, P. Bender, M. Schreiber et al., “Making bertha drive? An autonomous journey on a historic route,” IEEE Intelligent Transportation Systems Magazine, Vol.6, pp. 8-20, 2014.
2. J. McCall and M. Trivedi, “Video-based lane estimation and tracking for driver assistance: Survey, System, and Evaluation,” IEEE Trans. on Intelligent Transportation Systems, Vol.7, No.1, pp. 20-37, 2006.
3. B. Wu, T. Lee, H. Chang et al., “GPS navigation based autonomous driving system design for intelligent vehicles,” IEEE Int. Conf. on Systems, Man and Cybernetics, pp. 3294-3299, 2007.
4. M. Noda, T. Takahashi, D. Deguchi et al., “Vehicle ego-localization by matching in-vehicle camera images to an aerial image,” Asian Conf. on Computer Vision 2010 Workshops – Computer Vision, pp. 163-173, 2011.
5. D. Wong, D. Deguchi, I. Ide et al., “Single camera vehicle localization using SURF scale and dynamic time warping,” Proc. of the IEEE Intelligent Vehicles Symposium, pp. 681-686, 2014.
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