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Road detection for inner-city scenarios remains a difficult problem due to the high complexity in scene layout with unmarked or weakly marked roads and poor lighting conditions. This paper introduces a novel method based on multi normalized-histogram with Joint Boosting algorithm to road recognition. The approach performs three modules in parallel that are the Image Segmentation, the Texton Maps and...
The navigation of an autonomous vehicle is a highly complex task and the dynamic environment is used as a source for reasoning. Road detection is a major issue in autonomous systems and advanced driving assistance systems applied for inner-city. Uncertainty may arise in environments with unmarked or weakly marked roads or poor lightning conditions. Moreover, when a common benchmark is not used, it...
This paper presents an approach to refine noisy and sparse disparity maps from weakly-textured urban environments, enhancing their applicability in perception algorithms applied to autonomous vehicles urban navigation. Typically, the disparity maps are constructed by stereo matching techniques based on some image correlation algorithm. However, in urban environments with low texture variance elements,...
This paper presents an approach for road detection based on image segmentation. This segmentation is resulted from merging 2D and 3D image processing data from a stereo vision system. The 2D layer returns a matrix containing pixel's clusters based on the Watershed transform. Whereas the 3D layer return labels, that are classified by the V-Disparity technique, to free spaces, obstacles and non-classified...
Environment perception is a major research issue which is very important in the field of robotic system. In order to identify the horizon line and the drivable region, we have proposed a visual-perception system based on an automatic image discarding method as a simple solution to improve the system performance. In this paper, all these previous methods are organized in a visual-perception layer which...
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