Recursive least squares approach to calculate motion parameters for a moving camera

Abstract

The increase in quality and the decrease in price of digital camera equipment have led to growing interest in reconstructing 3-Dimensional (3D) objects from sequences of 2-Dimensional (2D) images. The accuracy of the models obtained depends on two sets of parameter estimates. The first is the set of lens parameters -focal length, principal point, and distortion parameters. The second is the set of motion parameters that allows the comparison of a moving camera's desired location to a theoretical location.In this paper, we address the latter problem, i.e. the estimation of the set of 3D motion parameters from data obtained with a moving camera. We propose a method that uses Recursive Least Squares (RLS) for camera motion parameter estimation with observation noise. We accomplish this by calculation of hidden information through camera projection and minimization of the estimation error. We then show how a filter based on the motion parameters estimates may be designed to correct for the errors in the camera motion. The validity of the approach is illustrated by the presentation of experimental results obtained using the methods described in the paper.