Bundle adjustment and Kalman filtering for homography estimation

This document compares global bundle adjustment via the ubiquitous Levenberg-Marquardt to a Kalman filter to estimate parameters of a homographic transformation between two or more images starting from bad initial conditions. We show that the filtering technique outperforms sparse bundle adjustment in terms of projection error and computational costs. The techniques are tested on real world images of an indoor and outdoor scene.