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

ABSTRACT 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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    ABSTRACT: This paper presents the visual servoing of a six degrees of freedom (6-DOF) manipulator for unknown three-dimensional profile following. The profile has an unknown curvature, but its cross section is known. The visual servoing keeps the transformation between a cross section of the profile and the camera constant with respect to 6 DOE The position of the profile with respect to only five degrees of freedom can be measured with the camera since the image does not provide position information along the profile. The kinematic model of the robot is used to reconstruct the displacement along the profile, i.e., the sixth degree of freedom, and allows to control the profile-following velocity. Experiments show good accuracy for positioning at a sampling rate of 50 Hz. Two control strategies are tested: proportional-integral control and generalized predictive control (GPC). The visual servoing exhibits better accuracy with the GPC in simulations and in real experiments on a 6-DOF manipulator due to the predictive property of the algorithm.
    IEEE Transactions on Robotics and Automation 08/2002; 18(4):511-520. DOI:10.1109/TRA.2002.802201
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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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    ABSTRACT: Vision based road trackers are about to be integrated in current vehicles mainly for security purposes in response to sleepiness problems for example. Nevertheless, such systems must be acceptable for drivers and must have a good reliability both in terms of roadsides recognition and of vehicle location estimation. Such a system must therefore be able to run in spite of difficult situations (due to occlusions, traffic, bad weather conditions, etc). Furthermore, the accuracy of the 3D vehicle estimation must be sufficient in order to feed subsequent warning systems. The system we have designed is able to recognize with reliability the lane sides in the current image and uses a 3D/4D modelling which provides both a good recognition as well as a very accurate 3D parameters estimation (vehicle location, steer angle, road curvatures, etc). The paper focuses mainly on this 3D original estimation stage and presents our recent developments (distance between vehicle and each road side for lane tracking application, analysis distances increasing, vertical road curvature estimation). The algorithm behaviour is then presented in simulated and real situations as well in order to prove the reliability of the approach.
    Intelligent Vehicle Symposium, 2002. IEEE; 07/2002
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    ABSTRACT: This paper describes two projects applying computer vision to In- telligent Vehicle Highway Systems. The first project has resulted in the development of a system for monitoring traffic scenes using video information. The objective is to estimate traffic parameters such as flow rates, speeds and link travel times, as well as to detect quickly disruptive incidents such as stalled vehicles and accidents. The second project is aimed at developing vision as a sensor technology for vehicle control. The novel feature of this project, compared to most previ- ous approaches, is the extensive use of binocular stereopsis. First, it provides information for obstacle detection, grouping, and range esti- mation which is directly used for longitudinal control. Secondly, the obstacle-ground separation enables robust localization of partially oc- cluded lane boundaries as v/ell as the dynamic update of camera rig parameters to deal with vibrations and vertical road curvature.
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