Cylindrical object reconstruction from a sequence of images

This paper presents a method intended to reconstruct a scene composed of cylindrical objects, and to simultaneously estimate the position of the moving camera used to acquire the image sequence. The iterated extended Kalman filter, used to perform this task, is supplied with the discrete sequence of monocular images of the scene and a poor a priori knowledge of the camera motion between successive shooting positions. Simulations performed on synthetic scenes show a good filter behavior when a 20% camera motion uncertainty, and a 2 pixels Gaussian noise in the image are assumed. A real scene test is also presented, that shows accurate results.