International audience

Constant Guillot1 [email protected] Maxime Taron1 [email protected] Patrick Sayd1 [email protected] Quoc-Cuong Pham1 [email protected] Christophe Tilmant2 [email protected] Jean-Marc Lavest2 [email protected] 1 CEA LIST Laboratoire Vision et Ingenierie des Contenus, BP 94, Gif-sur-Yvette, F-91191 France 2 LASMEA UMR 6602, PRES Clermont Universite/CNRS, 63177 Aubiere cedex, France

This paper proposes an original and novel vision sensing method to be used in vision-based dynamic identification of parallel robots. Indeed, it is shown that in the latter problem one requires to estimate (to the least) or measure (to the best) the end-effector pose and its time derivatives. The sensor we propose, based on a clever modelling of CMOS rolling shutter camera, measures simultaneously the end-effector pose of a calibrated visual pattern and its Cartesian velocity using a single view. Although motivated by parallel robot identification, this low cost sensor does not make any assumption on the kinematics of the robot and can thus be used for other applications. Experimental results with real data confirm the relevance of the approach and show the sensor good practical measurement accuracy

An original method for computing instantaneous 3D pose and velocity of fast moving objects using a single view is presented. It exploits image deformations induced by rolling shutter in CMOS image sensors. First of all, a general perspective projection model of a moving 3D point is presented. A solution for the pose and velocity recovery problem is then described. The method is based on bundle adjustment and uses point correspondences. The resulting algorithm enables to transform a CMOS low cost and low power camera into an original velocity sensor. Finally, experimental results with real data confirm the relevance of the approach.

Parallel structure machines are a means to reach higher acceleration capacities, and thus significant reductions in machining time. But this contribution implies having light structures, leading to significant deformations in dynamic conditions. Based on a prototype built at IFMA and its numerical model, this article shows first the influence of elastic deformations on actuator efforts. In order to validate the numerical model or any other model which takes flexibilities into account, we propose the concept of a dynamic metrology tool, capable of measuring the deformations of the machine's different components during machining movements. This is a hybrid tool built around a camera and an accelerometer. Experimental progress is then described, particularly the work leading to the improvement of camera detection accuracy. The precision obtained to date is 0.02 mm with one target in a field of 300 mm. It can theoretically reach 0.01 mm with three targets instead of one

In this paper, a vision-based measuring device is proposed and experimentally demonstrated to be an accurate, flexible, and low-cost tool for the kinematic calibration of parallel mechanisms. The accuracy and ease of use of the proposed vision sensor are outlined, with the suppression of the need for an accurate calibration target, and adequacy to the kinematic calibration process is investigated. In particular, identifiability conditions with the use of such an exteroceptive sensor are derived, considering the calibration with inverse or implicit models. Extensive results are given, with the evaluation of the measuring device and the calibration of an H4 robot. Using the full-pose measurement, an experimental analysis of the optimal calibration model is achieved, with study of the kinematic behavior of the mechanism. The efficiency of the provided method is thus evaluated, and the applicability of vision-based measuring devices to the context of kinematic calibration of parallel mechanisms is discussed.

This paper studies the multi-image calibration of a camera and how to carry it out in an automatic way making use of a projector. To prepare for multi-image calibration, it is necessary to calibrate the camera. Next, calibrate the projector using that camera. Then calibrate any camera with similar focal length to the previous one. The system doesn't need to incorporate any position platform and is completely automatic once it is disposed. It is based on the generation of images of a virtual pattern. This virtually generated pattern is projected on a fixed blackboard or a wall. Different on-line generated virtual patterns are visualized to carry out the auto-calibration. It is simple and can be repeated for a number of cameras that have an appropriate focal length. Also an improved method for focus of expansion using the same ideas is presented to obtain the principal point for a zoom camera.

The article describes an industrial application of a vision based 3D scene reconstruction process. The images come from independent cameras whose locations are unknown and which are placed indifferently in two different media (i.e. underwater or in the air). This process is currently used in the Poseidon system (see http://www.poseidon-tech.com) to realize an automatic CAD model of a swimming pool. Poseidon is the first computer system in the world to help in the prevention of drownings in public swimming pools. We have evaluated the reconstruction accuracy based on synthetic scenes as well as real ones. In the case of real data, results are compared with measurements made by a surveyor (with a telemeter) and demonstrate high accuracy in 3D localization and reconstruction. Lastly, it proves that computer vision modeling can be efficiently used for real time applications.

The purpose of this article is the localization of the camera in a textured universe. This environment, described by a database, is composed of a trihedron. The method is structured around three phases. At first, points obtained by the Harris and Stephens operator (1988) are associated between two successive images. This association is based on a light intensity correlation followed by an homography correlation. Secondly, the camera is localized by minimizing a double criteria. The first part of this criteria illustrates a good projection of the textured model in the image. The second one illustrates the fact that the system composed of the scene and two successive images have to satisfy the epipolar constraint. The minimization criteria is symmetric in relation to time in order to not perturb the localization process by previous localization errors. Indeed, the method calls into question the previous localization, in relation to the new image, to localize at best the new camera attitude. Finally, because of the geometry of the scene, the camera attitude is only localized about a scale factor. So, the last step consists in determining the scale factor by exploiting the "close surrounding" of the trihedron.

Reprocessing cells of nuclear plants are constituted by a great number of specular metallic pipes. When they are illuminated, this kind of pipes generates areas of high light intensity, called highlights. The latter can produce erroneous results, especially in edge detection. The matter of this paper is to suppress and further to exploit the highlight effects. With this aim in view, we propose a system based on the polarization of light, which not only suppresses highlights in the scene but also uses them to extract relevant information like the pipes axes at the video rate.

Deals with the hand-eye calibration problem. The question is to find the relative position and orientation between a camera rigidly mounted on the robot's last joint and the gripper. Hand-eye calibration is useful in many cases as for example, grasping objects or reconstructing 3D scenes. Almost all existing solutions lead to solving for homogeneous transformation equations of the form AX=XB. We propose a new formulation. Our method determines simultaneously the hand-eye transformation and the location of a calibration object with respect to the robot world coordinate system. The main advantage is that the number of unknowns remains constant and the solution is constrained to be consistent with the whole set of calibration data. Results of simulation experiments and comparisons with classical techniques are reported and analysed. Real experiments with a Cartesian robot are described and the results accuracy discussed.

Zoom lens camera calibration is an important and difficult problem for two reasons at least. First, the intrinsic parameters of such a camera change over time, it is difficult to calibrate them on-line. Secondly, the pin-hole model for single lens system can not be applied directly to a zoom lens system. In this paper, we address some aspects of this problem, such as determining principal point by zooming, modeling and calibration of lens distortion and focal length, as well as some practical aspects. Experimental results on calibrating cameras with computer controlled zoom, focus and aperture are presented.

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.>

The paper presents a new technique to infer 3D information using a static camera provided with a zoom lens. The modelling process is straightforward and does not involve the computation of intrinsic camera parameters. The approach is based on the exploitation of images of accurate regular grids. The main idea is to compute a transformation which allows to obtain a relationship between the real grids (without distortion) located in front of the camera and their distorted images on the CCD matrix. This relationship takes automatically all the distortion phenomena into account. Previous work in reconstruction by zooming involved high quality optical systems. The principal aim of the present work is to generalize this kind of reconstruction in the case of a standard zoom lens.>

Abstract- This paper presents a new method that permits to estimate, in the viewer coordinate system, the spatial attitude of an articulated object from a single perspective image. Its principle is based on the interpretation of some image lines as the perspective projection of linear ridges of the object model, and on an iterative search of the model attitude consistent with these projections. The presented method doesn't locate separately the different parts of the object by using for each of them a technics devoted to the localization of rigid object but computes a global attitude which respects the mechanical articulations of the objet. In fact, the geometrical transformations applied to the model to bring it into the correct attitude are obtained in two steps. The first one is devoted to the estimation of the attitude parameters corresponding to a rotation and involves an iterative process. The second step permits by the resolution of a linear system to estimate the translation parameters. The presented experiments correspond to the localization of robot arms from synthetical and real images. The former case permits to appreciate the accuracy of the method since the final result of the pose estimation can be compared with the attitude parameters used to create the synthetical image. The latter case presents an experiment made in an industrial environment and involves the estimation of twelve paramaters since the observed robot arm owns six inner degrees of freedom. The presented method can be useful in some operation driven by remote control. 1

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.>

A system is presented for modelling solids of revolution from a set of a few monocular images. The reconstruction is obtained by assuming only that the viewed object belongs to this particular class of objects. The knowledge of the camera displacement between each image is not required. The modeling is based on the resolution of the inverse perspective problem for the contour points detected in the different images.>