Computer vision system for an autonomous mobile robot

The purpose of this paper is to compare three methods for three-dimensional measurements of line position used for the vision guidance to navigate an autonomous mobile robot. A model is first developed to map three dimensional ground points into image points to be developed using homogeneous coordinates. Then using the ground plane constraint, the inverse transformation that maps image points into three dimensional ground points is determined. And then the system identification problem is solved using a calibration device. Calibration data is used to determine the model parameters by minimizing the mean square error between model and calibration points. A novel simplification is then presented which provides surprisingly accurate results. This method is called the magic matrix approach and uses only the calibration data. A more standard variation of this approach is also considered. The significance of this work is that it shows that three methods that are based on three-dimensional measurements may be used for mobile robot navigation and that a simple method can achieve accuracy to a fraction of an inch which is sufficient in some applications.