Curb Detection for Road and Sidewalk Detection

Abstract

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 shortest paths, generated from the vanishing point to the last row of the cost map, are searched and regarded as the road borders. Following the curb and road region detection results, the sidewalk area is detected by a region-growing-like method based on the geometry characteristic of the sidewalk area and the layout of road scenes. The proposed method has been tested for road and sidewalk detection tasks on the KITTI dataset. The experimental results of the curb, road, and sidewalk detection demonstrate the accuracy and robustness of this method.