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

1 Bookmark
 · 
57 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We propose a new approach for vision based longitudinal and lateral ve- hicle control which makes extensive use of binocular stereopsis. Longitudi- nal control - i.e. maintaining a safe, constant distance from the vehicle in front - is supported by detecting and measuring the distances to leading vehicles using binocular stereo. A known camera geometry with respect to the locally planar road is used to map the images of the road plane in the two camera views into alignment. Any significant residual image disparity then indicates an object not lying in the road plane and hence a potential obstacle. This approach allows us to separate image features into those lying in the road plane, e.g. lane markers, and those due to other objects. The features which lie on the road are stationary in the scene and appear to move only because of the egomotion of the vehicle. Measurements on these features are used for dynamic update of (a) the camera parameters in the presence of camera vibration and changes in road slope (b) the lateral position of the vehicle with respect to the lane markers. In the absence of this separation, image features due to vehicles which happen to lie in the search zone for lane markers would corrupt the estimation of the road boundary contours. This problem has not yet been addressed by any lane marker based vehicle guid- ance approach, but has to be taken very seriously, since usually one has to cope with crowded traffic scenes where lane markers are often obstructed by vehicles. Lane markers are detected and used for lateral control, i.e. following the road while maintaining a constant lateral distance to the road boundary. For that purpose we model the road and hence the shape of the lane markers as clothoidal curves, the curvatures of which we estimate recursively along the image sequence. These curvature estimates also provides desirable look-ahead information for a smooth ride in the car.
  • Source
    [Show abstract] [Hide abstract]
    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.
    07/1995;
  • Source
    [Show abstract] [Hide abstract]
    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 01/2002; 18(4):511-520.

Full-text (2 Sources)

Download
0 Downloads
Available from