Self-made texture and clustering based road recognition for UGV

Three hierarchical algorithms are proposed to get the passable driving road detection for unmanned ground vehicle (UGV). The innovation of this paper is the self-made directional texture which will be produced if the inverse homography transform is exerted on the image. This self-made texture is firstly used to remove the image corresponding to the objects above road. In order to avoid the influence of other color object, the k-means clustering is used to get the seed for region growing. Finally, the directional texture is secondly used to judge whether the image holes after region growing are the road or not. The final experimental results show the proposed algorithm can remove the non-road objects, can and fill the stained color region and can get the rational driving road.

[1]  Ping Wang,et al.  Visual attention mechanism based image shadow defect detection , 2011, 2011 International Conference on Electrical and Control Engineering.

[2]  Shiping Zhu,et al.  An adaptive shadow elimination algorithm using shadow position and edges attributes , 2010, 2010 3rd International Congress on Image and Signal Processing.

[3]  W. Sardha Wijesoma,et al.  Road-boundary detection and tracking using ladar sensing , 2004, IEEE Transactions on Robotics and Automation.

[4]  Franz Kummert,et al.  Spatial ray features for real-time ego-lane extraction , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[5]  Antonio M. López,et al.  Road Detection Based on Illuminant Invariance , 2011, IEEE Transactions on Intelligent Transportation Systems.

[6]  Shumin Fei,et al.  Robust urban road image segmentation , 2014, Proceeding of the 11th World Congress on Intelligent Control and Automation.

[7]  Vincent Frémont,et al.  Color-based road detection and its evaluation on the KITTI road benchmark , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[8]  Wolfgang Förstner,et al.  A temporal filter approach for detection and reconstruction of curbs and road surfaces based on Conditional Random Fields , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[9]  Hossein Pourghassem,et al.  Shadow detection based on combinations of HSV color space and orthogonal transformation in surveillance videos , 2014, 2014 Iranian Conference on Intelligent Systems (ICIS).

[10]  Sergiu Nedevschi,et al.  Curb Detection Based on a Multi-Frame Persistence Map for Urban Driving Scenarios , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[11]  Andrew Zisserman,et al.  Multiple View Geometry in Computer Vision (2nd ed) , 2003 .

[12]  Zhu Zhu,et al.  Graph-based ground segmentation of 3D LIDAR in rough area , 2014, 2014 IEEE International Conference on Technologies for Practical Robot Applications (TePRA).

[13]  Alexander Verl,et al.  Vision-based robust road lane detection in urban environments , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[14]  Junsong Yuan,et al.  Curb detection and tracking using 3D-LIDAR scanner , 2012, 2012 19th IEEE International Conference on Image Processing.

[15]  Jinfeng Yang,et al.  Shadow removal using Retinex theory , 2011, 2011 Third Chinese Conference on Intelligent Visual Surveillance.