Conference Paper

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

Comput. Vision Lab., Maryland Univ., College Park, MD
DOI: 10.1109/ROBOT.1990.126186 Conference: Robotics and Automation, 1990. Proceedings., 1990 IEEE International Conference on
Source: IEEE Xplore


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

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    • "It is a two dimensional line following experiment where the line can be curved but stays always in a plane parallel to the image plane of the camera. Other works are related to 3D reconstruction of a profile: e.g., in [3], [4], the authors deal with the 3D reconstruction of road geometry for autonomous land vehicles. This is a profile following task limited to 2 DOF, with assumptions that are specific to this type of application (e.g., the distance of the camera with respect to the road is a known constant) that cannot be used in our problem. "
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    • "Nevertheless, starting from image roadsides location, several reconstruction technics are able to compute 3D parameters of the road. These methods could be very noise sensitive since they need differential minimisation [3], [10] or approximations [18], [14]. Furthermore, since these methods are more often decorrelated from the recognition scheme, errors due to this stage could be dif┬úcult to manage even if the reconstruction scheme is powerful (see for example [1]). "
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