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Abstract : |
A new method is presented for reconstructing a 3D road from a single image. It finds the images of opposite points of the road (opposite points are points which face each other on the opposite sides of the road; the images of these points are called matching points). For points chosen from one side of the road image, the proposed algorithm finds all the matching point candidates on the other side, based on local properties of a road. However these solutions do not necessarily satisfy the global properties of a typical road. A dynamic programming algorithm is applied to reject the candidates which do not fit the global road. A benchmark using synthetic roads is described, which shows that the roads reconstructed by the proposed method match the actual roads better than two other road reconstruction algorithms. Experiments with 50 road images taken by the Autonomous Land Vehicle (ALV) show that the method is robust with real world data, and that the reconstructions are fairly consistent with road profiles obtained by fusion between range images and video images., |
