Stereovision-based road boundary detection for intelligent vehicles in challenging scenarios

Road detection is a crucial problem for intelligent vehicles and mobile robots. Most of the methods proposed nowadays only achieve reliable results in relatively well-arranged environments. In this paper, we proposed a stereovision-based road boundary detection method by combining homography estimation and MRF-based belief propagation to cope with challenging scenarios such as unstructured roads with unhomogeneous surfaces. In the method, each pixel in the reference image is firstly labeled as ¿road¿ or ¿non-road¿ by minimizing a well defined energy function that accounts for the planar road region. Subsequently, both of the road boundaries are generated using Catmull-Rom splines based on RANdom SAmple Consensus (RANSAC) algorithm with varying road structure models to help the intelligent vehicle understand the structure as well as safe range of current road. In the suggested framework, both intensity and geometry information of road scenarios are used to contain all the regions belonging to the planar road plane, and the left and right road boundaries are generated separately using a robust fitting algorithm to handle different road structures. Therefore, more accurate as well as robust detection of the road can be expected. Experimental results on a wide variety of typical but challenging scenarios have demonstrated the effectiveness of the proposed method.