Abstract
A novel algorithm capable of estimating free space for vehicle navigation is presented. When a disparity map — dense or sparse — from stereo matching and a longitudinal profile of the road surface on the disparity domain are provided, the free space is estimated precisely. According to the longitudinal profile of the road surface, the disparity map is classified into an obstacle disparity map and a road surface disparity map. After combining these two disparity maps through a score map, a border line separating the road surface and the non-road surface is estimated using dynamic programming on a udisparity representation. The main contribution of the proposed approach is the robust detection of the free space and the distance between stereo cameras and obstacles, whereby the detection is sufficiently rapid for vehicle navigation. The validity of the proposed algorithm is demonstrated by experiments through many outdoor road images from various traffic scenarios.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Badino, H., Mester, R., Vaudrey, T. and Franke, U. (2008). Stereo-based free space computation in complex traffic scenarios. IEEE Southwest Symp. Image Analysis and Interpretation, 189–192.
Bertozzi, M. and Broggi, A. (1998). GOLD: A parallel real-time stereo vision system for generic obstacle and lane detection. IEEE Trans. Image Processing 7, 1, 62–81.
Hillier, F. S. (2006). Introduction to Operations Research. 8/E. McGraw Hill. New York.
Kubota, S., Nakano, T. and Okamoto, Y. (2007). A global optimization algorithm for real-time on-board stereo obstacle detection systems. IEEE Intelligent Vehicle Symp., 7–12.
Labayrade, R., Aubert, D. and Tarel, J. P. (2002). Real time obstacle detection in stereovision on non flat road geometry through “V-disparity” representation. Proc. IEEE Intelligent Vehicles Symp., 646–651.
Lee, K. Y., Park, J. M. and Lee, J. W. (2014). Estimation of longitudinal profile of road surface from stereo disparity using dijkstra algorithm. Int. J. Control, Automation, and Systems 12, 4, 895–903.
Lee, K. Y. and Lee, J. W. (2011). Extracting of corresponding points of stereo images based on dynamic programming. J. Institute of Control, Robotics and Systems 17, 5, 397–404.
Lee, K. Y., Lee, J. W. and Houshangi, N. (2009). A stereo matching algorithm based on top-view transformation and dynamic programming for road-vehicle detection. Int. J. Control, Automation, and Systems 7, 2, 221–231.
Oniga, F. and Nedevschi, S. (2010). Processing dense stereo data using elevation maps: Road surface, traffic isle, and obstacle detection. IEEE Trans. Veh. Technol. 59, 3, 1172–1182.
Song, G. Y. and Lee, J. W. (2013). Accurate dense stereo matching of slanted surfaces using 2D integral images. Proc. ICVS, 828–833.
Tsai, R. Y. (1987). A versatile camera calibration technique for high accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J. Robotics Automat RA-3, 4, 323–344.
Wedel, A., Franke, U., Badino, H. and Cremers, D. (2008). B-spline modeling of road surfaces for free space estimation. IEEE Intelligent Vehicle Symp., 828–833.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lee, K.Y., Song, G.Y., Park, J.M. et al. Stereo vision enabling fast estimation of free space on traffic roads for autonomous navigation. Int.J Automot. Technol. 16, 107–115 (2015). https://doi.org/10.1007/s12239-015-0012-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12239-015-0012-7