Reconstruction of a road by local image matches and global 3D optimization

A method is presented for reconstructing a 3-D 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 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. It shows that the roads reconstructed by the proposed method match the actual roads better than those reconstructed by two other road reconstruction algorithms. Experiments with 50 road images taken by the autonomous land vehicle (ALV) showed 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