This paper describes the use of vision for navigation of mobile robots floating in 3D space. The problem addressed is that of automatic station keeping relative to some naturally textured environmental region. Due to the motion disturbances in the environment (currents), these tasks are important to keep the vehicle stabilized relative to an external reference frame. Assuming short range regions in the environment, vision can be used for local navigation, so that no global positioning methods are required. A planar environmental region is selected as a visual landmark and tracked throughout a monocular video sequence. For a camera moving in 3D space, the observed deformations of the tracked image region are according to planar projective transformations and reveal information about the robot relative position and orientation w.r.t. the landmark. This information is then used in a visual feedback loop so as to realize station keeping. Both the tracking system and the control design are discussed. Two robotic platforms are used for experimental validation, namely an indoor aerial blimp and a remote operated underwater vehicle. Results obtained from these experiments are described. 2002 Elsevier Science B.V. All rights reserved. Keywords: Visual tracking; Optic flow; Planar projective motion models; Visual servoing; Underwater robots; Blimp; Station keeping 1.

This paper describes a general, nonanalytical method for deriving Fourier series coefficients using a genetic algorithm. Nonanalytical methods are often needed in problems where lost portions of a complex signal require restoration. We discuss some of the difficulties involved in working with the associated trigonometric polynomials and propose an alternative solution for adapting genetic algorithms for this class of problems. We demonstrate the efficacy of our approach with a case study. Our particular case study features the processing of data that has been collected by a novel optical waveslope instrument, which measures the topography of water surfaces. I. INTRODUCTION The study of orthogonal functions has long existed in the genetic algorithm (GA) literature. Bethke was among the earliest to study GAswith orthogonal functions in his dissertation [1]. De Jong referenced Bethke's work at the First International Conference on Genetic Algorithms [6] and Goldberg als...

Magnetic resonance spectroscopic imaging (MRSI) is a type of MRI in which both spatial and spectral information are gathered. Unfortunately, the time required to acquire a high-resolution image is prohibitive. Thus, we desire to gather the most informative data possible in the limited time available. The particular choice of a limited set of k-space samples has a tremendous impact on the quality of the reconstructed image. In previous work, we demonstrated a technique for choosing k-space samples under the assumption of a leastsquares reconstruction given knowledge of the region of support of the image. In this work, we extend our criterion to a Wiener filter reconstruction. Furthermore, we exploit the properties of the Wiener filter criterion to derive a new optimization strategy. This optimization strategy allows us to begin the k-space acquisition process before the choice of k-space samples is complete. Results are demonstrated on an acquired MRI phantom. 1. INTRODUCTION In this p...

A new stopping rule is proposed for linear, iterative signal restoration using the gradient descent and conjugate gradient algorithms. The stopping rule attempts to minimize MSE under the assumption that the signal arises from a white noise process. This assumption is appropriate for many coherent imaging applications. The stopping rule is trivial to compute, and for fixed relaxation parameters, can be computed prior to starting the iteration. The utility of the stopping rule is demonstrated through the restoration of MR imagery. I. Introduction In this correspondence we consider signal and image restoration problems which may be reduced to solving a set of linear equations Ax = b (1) where A is a full-rank linear degradation matrix, x is an n \Theta 1 vector of unknown signal samples, and b is an m \Theta 1 vector of measured (degraded) data. This restoration problem may be solved directly using a variety of regularized least squares and pseudoinverse techniques. In some cases, partic...

Abstract — In this paper we discuss the use of vision for underwater vehicles. The work described here has been developed in the context of the European Research Project NAR-