Error modeling in stereo navigation

on scalar models of measurement error in triangulation. Using t h m-dimensional (3D) Gaussian distributions to model triangulation error is shown lo lead lo much better performance. How to compute the error model from image correspondences. estimate robot motion between frames, and update the global positions of the robot and the hndm8rks over time are discussed. Simulations show that, compared to scalar error models, the 3D Gaussian reduces the variance in robot position estimates and better distinguishes rotational from translational motion. A short indoor run with real images supported these conclusions and computed the final robot position to within two percent of distance and one degree of orientation. These results illustrate the importance of error modeling in stereo vision for this and other applications.