Abstract

This paper describes a new method for object movement estimation using sequences of images taken from a monocular camera. The method integrates a Kalman filter to estimate the three dimensional parameters of the optical system and a lineal projective model to determine 3D point coordinates projected on the retinal plane. The method works with at least three distinctive points in the image, and they are updated using correlation methods. The result is an estimation of the rotation and translation parameters between successive images within the sequence and yield to the 3D coordinates of the points selected for correspondence. The method is tested with synthetic images to evaluate its accuracy and later an interesting application in autonomous navigation is presented.