A camera calibration method based on iterated extended Kalman filter using planar target

Abstract

ABSTRACT It is proposed a method for camera calibration that could be used in stereo systems as well as in stereo head navigation in this paper. A pinhole camera model and two-dimensional planar target are considered. An Iterated Extended Kalman Filter (IEKF) is used to estimate camera parameters. The met hod takes the observed feature points of images as the filter input and the estimated value of the intrinsic and extrinsic camera parameters as the filter output. Both computer simulation and real data experiments have been used to test the proposed method, and good results have been obtained. The RMS error of absolute distance between reprojection feature points is about 0.09 pixels in real experiments. The experimental results show IEKF is also a feasible optimization algorithm for on-line camera calibration. Key words: Camera Calibration, Iterated Extended Kalman Filter, planar target 1. INTRODUCTION Camera calibration is a crucial phase in most vision systems and a first step in 3D reconstruction. It has been broadly applied in machine vision, virtual reality, and three-dimensional reconstruction and so on. Generally, in order to obtain higher calibration precision, in trinsic and extrinsic camera para meters are estimated through nonlinear optimization methods with information acquired from images. Starting from the simplest method we could mention the Least Square Error (LSE)