EKF pose estimation: How many filters and cameras to use?

The extended Kalman filter (EKF) is suitable for real-time pose estimation due its low computational demand and ability to handle the nonlinear perspective camera model. There are many EKF based approaches in the literature; some are very recent while others exist for about two decades. These methods differ in two main aspects: the number and arrangement of cameras, and the number and usage of filters. In this work, we will compare these approaches using simulations and real experiments. As far as we know, it is the first attempt to do this with such details. We will show which is suitable under different motion patterns, and explain the effect of the bas-relief ambiguity upon the accuracy of the different approaches. Additionally, we will discuss how to solve the scale factor ambiguity, and suggest the best strategy to deal with the features fed to the filter.