Robust Estimation of 3D Trajectories from a Monocular Image Sequence

摘要

This article describes a new method for object trajectory estimation that uses sequences of images taken from a monocular camera. The method integrates a Kalman filter to estimate the three-dimensional (3D) parameters of the optical system and a lineal pro-jective model to determine 3D point coordinates projected on the retinal plane. It works with at least three distinctive points in the image, and they are updated with 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 scaling problem related to 3D reconstruction is tackled via a priori information of the objects being observed. The method is tested with synthetic images to evaluate its accuracy, and later, an interesting application in autonomous navigation is presented.