  This paper considers homography estimation in a Bayesian filtering framework
using rate gyro and camera measurements. The use of rate gyro measurements
facilitates a more reliable estimate of homography in the presence of
occlusions, while a Bayesian filtering approach generates both a homography
estimate along with an uncertainty. Uncertainty information opens the door to
adaptive filtering approaches, post-processing procedures, and safety
protocols. In particular, herein an iterative extended Kalman filter and an
interacting multiple model (IMM) filter are tested using both simulated and
experimental datasets. The IMM is shown to have good consistency properties and
better overall performance when compared to the state-of-the-art homography
nonlinear deterministic observer in both simulations and experiments.
