JRM Vol.24 No.1 pp. 16-27
doi: 10.20965/jrm.2012.p0016


Self-Supervised Online Long-Range Road Estimation in Complicated Urban Environments

Yoji Kuroda, Masataka Suzuki, Teppei Saitoh,
and Eisuke Terada

Department of Mechanical Engineering, Meiji University, 1-1-1 Higashimita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan

January 26, 2011
April 13, 2011
February 20, 2012
road perception, robot vision, terrain classification, level-set method, self-supervised learning

In this paper, we propose a long-range road estimation method for autonomousmobile robots in unstructured urban environments. Near-range road surface conditions are estimated by using remission value as reflectivity of a laser scanner. Graph cut algorithm is applied to estimate road region robustly also in complicated environments. Moreover, we propose a novel image segmentation method to estimate long-range road surface. A compact texture/color feature is integrated with level-set method to estimate precise road boundaries robustly. Our proposed image segmentation approach gives better performance compared with standard classification approach. Finally, we run our autonomous mobile robot in “Tsukuba Challenge 2009” and our university campus, and experimental results have shown a marked increase accuracy in road estimation over standard methods.

Cite this article as:
Y. Kuroda, M. Suzuki, T. Saitoh, and <. Terada, “Self-Supervised Online Long-Range Road Estimation in Complicated Urban Environments,” J. Robot. Mechatron., Vol.24, No.1, pp. 16-27, 2012.
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Last updated on Jul. 04, 2020