Road recognition from a single image using prior information

In this study, we present a novel road recognition method using a single image for mobile robot navigation. Vision-based road recognition in outdoor environments remains a significant challenge. Our approach exploits digital street maps, the robot position, and prior knowledge of the environment. We segment an input image into superpixels, which are grouped into various object classes such as roadway, sidewalk, curb, and wall. We formulate the classification problem as an energy minimization problem and employ graph cuts to estimate the optimal object classes in the image. Although prior information assists recognition, erroneous information can lead to false recognition. Therefore, we incorporate localization into our recognition method to correct errors in robot position. The effectiveness of our method was verified through experiments using real-world urban datasets.