Online road segmentation for urban complex environments

In this paper, we propose a novel approach to stable near and long range perception for various complex outdoor environments. Our techniques cope robustly with near-range road estimation using a laser scanner and long-range terrain classification using a color camera. Near-range road surface conditions are estimated by using information of remission value as reflectivity of a laser. We apply graph cut algorithm to grid map in order to estimate road region robustly also in complex environments where fallen leaves exist sparsely. Moreover, we propose superpixel-based terrain classification method which can give a good performance compared with pixel-based classification. Experimental results have shown that demonstrate a marked increase in long-range classification and near-range road estimation accuracy over standard methods.