Curb detection, a significant area of autonomous driving, plays an important role in road detection and obstacle avoidance, etc. However, curb detection is challenging due to the problems like occlusions, shadows and the small size of the target. In this paper, we propose a curb detection paradigm for road and sidewalk detection for mobile robots using stereo vision in the urban residential region. First, the flat area is estimated based on the disparity and v-disparity maps generated from stereo matching. In the estimated flat area, to distinguish curbs from the road and obstacles, we propose an efficient 16-dimensional descriptor based on the appearance, geometry, and disparity characteristics of curbs. The curb points can be extracted by an SVM classifier with the obtained descriptors. Second, the curb points are exploited by a vanishing point constrained Dijkstra road model to find the road region, where two …