We describe a new system for estimating road shape ahead of a vehicle for the purpose of driver assistance. The method utilises a single on board colour camera, together with inertial and velocity information, to estimate both the position of the host car with respect to the lane it is following and also the width and curvature of the lane ahead ut distances of up t0.80 metres. The system's image processing extracts a variety of different styles of lane markings from road imugery, and is able to compensate for a range of lighting conditions. Road shape and car position are estimated using a particle filter: The system, which runs at 10.5 frames per second, has been applied with some success to several hours' worth of data captured from highways under varying imaging conditions.

This paper considers the problem of vision-based control of a nonholonomic mobile robot. We describe the design and implementation of real-time estimation and control algorithms on a car-like robot platform using a single omni-directional camera as a sensor without explicit use of odometry. We provide experimental results for each of these vision-based control objects. The algorithms are packaged as control modes and can be combined hierarchically to perform higher level tasks involving multiple robots. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. Author(s)Aveek

Abstract

Abstract

Kalman's optimum linear filter has proved to be immensely popular in the field of computer vision. A less often quoted contribution of Kalman's to the control theory literature is that of the concepts of controllability and observability which may be used to analyse the state transition and observation equations and give insights into the filter's viability. This paper aims to highlight the usefulness of these two ideas during the design stage of the filter and, as well as presenting the standard solutions for linear systems, uses a practical vision application (that of tracking plants for an autonomous crop protection vehicle) to illustrate a useful special case where these methods may be applied to a non-linear system. The application of tests for controllability and observability to the practical non-linear system give not only confirmation that the filter will be able to produce stable estimates, but also gives a lower bound on the number of features required from each image for it to do so.