Abstract not found

Abstract not found

Abstract — In this paper we propose a new way to achieve a navigation task for a non-holonomic vehicle. We consider an image-based navigation process. We show that it is possible to navigate along a visual path without relying on the extraction, matching and tracking of geometric visual features such as keypoint. The new proposed approach relies directly on the information (entropy) contained in the image signal. We show that it is possible to build a control law directly from the maximisation of the shared information between the current image and the next key image in the visual path. The shared information between those two images are obtained using mutual information that is known to be robust to illumination variations and occlusions. Moreover the generally complex task of features extraction and matching is avoided. Both simulations and experiments on a real vehicle are presented and show the possibilities and advantages offered by the proposed method. I.

Abstract. In minimally invasive surgery or needle insertion procedures, the ultrasound imaging can easily and safely be used to visualize the target to reach. However the manual stabilization of the view of this target, which undergoes the physiological motions of the patient, can be a challenge for the surgeon. In this paper, we propose to perform this stabilization with a robotic arm equipped with a 2D ultrasound probe. The six degrees of freedom of the probe are controlled by an image-based approach, where we choose as visual feedback the image intensity. The accuracy of the control law is ensured by the consideration of the periodicity of the physiological motions in a predictive controller. Tracking tasks performed on a realistic abdominal phantom validate the proposed approach and its robustness to deformation is assessed on a gelatin-made deformable phantom.

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

c t i v it y e p o r t 2008 Table of contents

mutual information for appearance-based visual

Abstract — In this paper, we propose a new type of visual features for visual servoing: photometric moments. These global features do not require any segmentation, matching or tracking steps. The analytical form of the interaction matrix is developed in closed form for these features. Results from experiments carried out with photometric moments have been presented. The results validate our modelling and the control scheme. They perform well for large camera displacements and are endowed with a large convergence domain. From the properties exhibited, photometric moments hold promise as better candidates for IBVS over currently existing geometric and pure luminance features. I.