A mobile robot can identify its own position relative to a global environment model using triangulation based on measuring angular separation between three landmarks in the environment. Multiple views from different locations of a smaller set of landmarks can also be used though. Alternatively the current position estimates can be updated using heading and distance measurements to a single landmark. Using these different strategies 8 position estimating techniques have been designed, analyzed and compared. These are based on viewing 1, 2 and 3 landmarks from one or two different viewpoints. It is shown that these procedures may be very sensitive to noise depending on the spatial landmark configuration, and relative position between robot and landmarks. A general analysis is presented which permits prediction of the uncertainty in the triangulated position. The uncertainty measure can be used to determine which of the light different techniques is the most suitable in specific situations. The entire analysis is based on a basic statistical approach, and verified experimentally. In addition to the evaluation of the individual techniques, an algorithm is presented for automatic selection of optimal landmarks. This algorithm enables a robot to continuously estimate its current position from the set of landmarks which provides the most stable solution. It is demonstrated that using this algorithm can result in more than one order of magnitude reduction in position uncertainty.

A mobile robot can identify its own position relative to a global environment model by using triangulation based on three landmarks in the environment. It is shown that this procedure may be very sensitive to noise depending on spatial landmark configuration, and relative position between robot and landmarks. A general analysis is presented which permits prediction of the uncertainty in the triangulated position.In addition an algorithm is presented for automatic selection of optimal landmarks. This algorithm enables a robot to continuously base its position computation on the set of available landmarks, which provides the least noise sensitive position estimate. It is demonstrated that using this algorithm can result in more than one order of magnitude reduction in uncertainty. (C) 1998 Elsevier Science B.V. All rights reserved.

Three depth recovery methods are presented. The methods are based on focusing, zooming, and motion stereo, using a single camera. An introduction to the individual methods is given, followed by an analysis of the error functions. The analysis is used to determine whether the methods are applicable for integration into a single system, enabling recovery of exible and robust depth estimates. Experimental results are presented. The focus and zoom technique have been found to be of little use, due to restrictions within the methods. The motion stereo technique can be used as a single mean to obtain depth information , even in natural images. Using this method combined with conventional stereo will enable robust and exible depth recovery .

We describe a highly modular vision svstem enabling a mobile robot to navigate in a lab environment. The system uses a coarse model of the la, motion information from the mobile robot, and input from a single video camera to achieve this goal. The system consists of the following modules: Filtering of the input. Image to remove noise. Edge extraction. Line linking. Prediction of lines from t he model. Matching of extracted and predicted lines. Estimation of robot position based on rnatched lines. The system has been implemented in order to have a testbed facilitating experiments with either improvements to existing modules, or completely new modules. In its present state the vision system is able to determine an accurate estimate of the position of the robot thus correcting the inevitable errors of the motion sensors of the mobile robot.

To achieve continuous operation and thus facilitate use of vision in a dynamic scenario, it is necessary to introduce a purpose for the visual processing. This provides information that may control the visual processing and thus limits the amount of resources needed to obtain the required results. A proposed architecture for vision systems is presented, along with an architecture for visual modules. This architecture enables both goal and data driven processing, with a potentially changing balance between the two modes. To illustrate the potential of the proposed architecture, a sample system for recovery of scene depth is presented, with experimental results which demonstrate a scalable performance.