Abstract not found

Abstract not found

This paper presents an algorithm that uses visual input to perform homing for an autonomous mobile robot. An image captured at the target pose (position and orientation) is compared with the currently viewed image to determine the parameters of the next move of the robot toward the target (rotation and translation). The visual data is captured using an omnidirectional camera and images are compared using the Manhattan distance function to determine both the translation and rotation angle. Results, limitations and successes are presented and discussed. 1

Abstract—This paper focuses on the way to achieve accurate visual servoing tasks when the shape of the object being observed as well as the desired image are unknown. More precisely, we want to control the camera orientation with respect to the tangent plane at a certain object point corresponding to the center of a region of interest. We also want to observe this point at the principal point to fulfil a fixation task. A 3-D reconstruction phase must, therefore, be performed during the camera motion. Our approach is then close to the structure-from-motion problem. The reconstruction phase is based on the measurement of the 2-D motion in a region of interest and on the measurement of the camera velocity. Since the 2-D motion depends on the shape of the objects being observed, we introduce a unified motion model to cope both with planar and nonplanar objects. However, since this model is only an approximation, we propose two approaches to enlarge its domain of validity. The first is based on active vision, coupled with a 3-D reconstruction based on a continuous approach, and the second is based on statistical techniques of robust estimation, coupled with a 3-D reconstruction based on a discrete approach. Theoretical and experimental results compare both approaches. Index Terms—Active vision, 3-D reconstruction, dynamic vision, M-estimators, robust estimation, structure-from-motion,

In this paper, we describe a modular software package, named VISP, that allows fast development of visual servoing applications. Visual servoing consists in specifying a task as the regulation of a set of visual features. Various issues have thus to be considered in the design of such applications: among these issues we consider the control of robots motion, visual features modeling, and the tracking of the visual measurements. Our environment features a wide class of control skills, a library for real-time tracking and a simulation toolkit.