Recent progress in monocular localization of modelled objects allow to design a robot command based on pose from visual information. The computing time of such algorithms was was once too great for them to be used in real-time closed-loop control, but this is no longer so. The principle of this command is to control the camera location relative to the pose of a target object: this approach is not only straightforward, but moreover it is fairly general in the fact that it does not depend on the visual information which is treated but only of the localization delivered by these treatments. This command has been implemented on a camera-equipped robotic system, a Cartesian robot arm, to track volumic objects.

This paper presents a system for modeling solids of revolution from a set of images taken with a zoom-lens. Using a zoom in monocular vision has interesting optical properties. The displacement of the optical center along the optical axis, when the focal length is modified, permits one to implement a triangulation process. Modeling a solid of revolution requires the estimation of the 3D location of its revolution axis. First, the authors describe an original method, using zoom properties, to compute the 3D pose of the revolution axis. It requires the detection in each image of the object axis projection. Then, the modeling algorithm is presented. It is based on the resolution of the inverse perspective problem for points detected in the images. An experimental result of reconstruction, from a real images set is finally given.>

This paper presents a new method that permits to solve the problem of determination of a modelled 3D-object spatial attitude from a single perspective image and to compute the covariance matrix associated to the attitude parameters. Its principle is based on the interpretation of at least three segments as the perspective projection of linear ridges of the object model and on the iterative search ( using Kalman filtering) of the model attitude consistent with these projections. The knowledge of the attitude and of the associated covariances enables to use a higher level Kalman filter to track an object along an image sequence. In the tracking process this Kalman filter is used to predict the attitude of the object and the error matrices are used to make robust automatic matches between the image segments and the model ridges. Tracking experiments have been made that proves the validity of this approach.

It is shown that it is possible to infer 3-D information from a set of images taken with a zoom lens. A precise study of the optical properties of such a lens gives two major results: The intersection between the optical axis and the image plane can be independently and accurately determined, and the pin-hole model cannot be used directly for the approximation of such a complex lens system. To explain the optical phenomena occurring during a zoom-lens focal-length change, it is shown that a thick optical model must be considered. Experimental reconstruction results for a set of real images, are given.>

The localization of some kinds of modeled generalized cylinders from a single brightness perspective image is addressed. It is shown how the zero-curvature points of their contours can be used to solve the inverse perspective problem. Three key theorems about the perspective projection of space curves and of the limbs of a straight homogeneous generalized cylinder whose scaling function has at least one zero-curvature point are discussed. In view of the localization of homogeneous generalized cylinders, an algorithm previously developed by the authors which estimates the pose of a line-triplet is adapted. A new theoretical result about the inverse perspective projection of cones of revolution useful for the localization of objects of revolution is presented. The corresponding algorithms have been implemented, and results of experiments demonstrate the feasibility of the proposed localization methods.>

A method for finding analytical solutions to the problem of determining the attitude of a 3D object in space from a single perspective image is presented. Its principle is based on the interpretation of a triplet of any image lines as the perspective projection of a triplet of linear ridges of the object model, and on the search for the model attitude consistent with these projections. The geometrical transformations to be applied to the model to bring it into the corresponding location are obtained by the resolution of an eight-degree equation in the general case. Using simple logical rules, it is shown on examples related to polyhedra that this approach leads to results useful for both location and recognition of 3D objects because few admissible hypotheses are retained from the interpolation of the three line segments. Line matching by the prediction-verification procedure is thus less complex.>

A method to find the analytical solutions of the inverse perspective problem for the determination of the 3-D object attitude in space from a single perspective image is presented. Its principle is based on the interpretation of a triplet of any image lines as the perspective projection of triplet of linear ridges of the object model. The geometrical transformations to apply to the model to bring it into the corresponding location are obtained by the resolution of an eighth-degree equation. The number of admissible solutions can still be reduced, using simple pruning rules. This approach leads to very strong results useful for both location and recognition of 3D objects. Because few admissible hypotheses are retained, the line-matching procedure by prediction-verification is less complex.>

For real-time location, a new algorithm is proposed which uses a simple but efficient description of the image of any isolated object. This description is a small set of points, one being the barycenter of the contour of the image object, and the others being the intersection points of the contour and of a circle centered at the barycenter. Two characteristic vectors are calculated and used for the determination of the object orientation and of the face of a flat object. Several methods are given to obtain the intersection points. Some details of the implementation of the algorithm and real-time experimental results are also mentioned.