Abstract — Moments are generic (and usually intuitive) descriptors that can be computed from several kinds of objects defined either from closed contours or from a set of points. In this paper, we present improvements in image-based visual servo using image moments. First, the analytical form of the interaction matrix related to the moments computed from a set of coplanar points is derived, and we show that it is different of the form obtained previously using coplanar closed contours. Six visual features are selected to design a decoupled control scheme when the object is parallel to the image plane. This nice property is then generalized to the case where the desired object position is not parallel to the image plane. Finally, experimental results are presented to illustrate the validity of our approach and its robustness with respect to modeling errors. Index Terms — Visual servoing, image moment, invariant. I.

This paper proposes a new way to achieve robotic tasks by visual servoing. Instead of using geometric features (points, straight lines, pose, homography, etc.) as it is usually done, we use directly the luminance of all pixels in the image. Since most of the classical control laws fail in this case, we turn the visual servoing problem into an optimization problem leading to a new control law. Experimental results validate the proposed approach and show its robustness regarding to approximated depths, non Lambertian objects and partial occlusions.

The paper presents a method for estimating the inverse Jacobian matrix of a function, without computing the direct Jacobian matrix. The resulting inverse Jacobian matrix is shown to perform much better in modelling a relation # = f (x) than the classical Moore-Penrose inverse J f . Theoretical insight as well as comparisons in the domain of visual servoing are provided to demonstrate this assertion.

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In this work we propose a new way to achieve visual servoing using directly the information (as defined by Shannon) of the image. A metric derived from information theory, mutual information, is considered. Mutual information is widely used in multi-modal image registration (medical applications) since it is insensitive to changes in the lighting condition and to a wide class of non-linear image transformation. In this paper mutual-information is used as a new visual feature for visual servoing and allows us to build a new control law to control the 6 dof of the robot. Among various advantages, this approach does not require any matching nor tracking step, is robust to large illumination variation and allows to consider, within the same task, different image modalities. Experiments that demonstrate these advantages conclude the paper.

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In this paper we demonstrate the use of a dynamic, six-degree-offreedom (6DOF) laser tracker to empirically evaluate the performance of a real-time visual servoing implementation, with the objective of establishing a general method for evaluating realtime 6DOF dimensional measurements. The laser tracker provides highly accurate ground truth reference measurements of position and orientation of an object under motion, and can be used as an objective standard for calibration and evaluation of visual servoing and robot control algorithms. The real-time visual servoing implementation used in this study was developed at the Purdue Robot Vision Lab with a subsumptive, hierarchical, and distributed vision-based architecture. Data were taken simultaneously from the laser tracker and visual servoing implementation, enabling comparison of the data streams.

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Abstract. Motion segmentation is a fundamental aspect of tracking in a scene with multiple moving objects. In this paper we present a novel approach to clustering individual image pixels associated with different 3D rigid motions. The basic idea is that the change of the intensity of a pixel can be locally approximated as a linear function of the motion of the corresponding imaged surface. To achieve appearance-based 3D motion segmentation we capture a sequence of local image samples at nearby poses, and assign for each pixel a vector that represents the intensity changes for that pixel over the sequence. We call this vector of intensity changes a pixel “intensity trajectory”. Similar to 2D feature trajectories, the intensity trajectories of pixels corresponding to the same motion span a local linear subspace. Thus the problem of motion segmentation can be cast as that of clustering local subspaces. We have tested this novel approach using some real image sequences. We present results that demonstrate the expected segmentation, even in some challenging cases. 1

This chapter is dedicated to a new way to achieve robotic tasks by 2D visual servoing. Contrary to most of related works in this domain where geometric visual features are usually used, we directly here consider the luminance of all pixels in the image. We call this new visual servoing scheme photometric visual servoing. The main advantage of this new approach is that it greatly simplifies the image processing required to track geometric visual features all along the camera motion or to match the initial visual features with the desired ones. However, as it is required in classical visual servoing, the computation of the so-called interaction matrix is required. In our case, this matrix links the time variation of the luminance to the camera motions. We will see that this computation is based on a illumination model able to describe complex luminance changes. However, since most of the classical control laws fail when considering the luminance as a visual feature, we turn the visual servoing problem into an optimization one leading to a new control law. Experimental results on positioning tasks validate the feasibility of photometric visual servoing and show its robustness regarding to approximated depths, Lambertian and