Abstract To solve the detection problem for low-speed mobile robots in urban environments, a road detection approach based on graph model is proposed to detect road region. Firstly, the road image is segmented into sub-images,and the feature vector of each sub-image is computed to generate node set of the graph model. Then, the concept of neighbor radius for nodes is proposed to calculate edge weights between adjacent nodes, from which the edge set of graph is acquired. The graph nodes merging rule based on the minimum spanning tree algorithm is used to combine nodes, which realizes the road image segmentation. Finally, the road nodes are extracted to segment the road region by setting a node sample window. The relationship among the road detection precision, sub-image size and threshold parameter is studied in the experiment. The feasibility of using gray feature to detect road region is verified. The experimental results demonstrate that the proposed approach can detect road regions from different kinds of urban road images effectively and is suitable for the road detection.
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2014, Vol. 27 
