We focus on the problem of estimating the ground plane orientation and
location in monocular video sequences from a moving observer. Our only
assumptions are that the 3D ego motion t and the ground plane normal n
are orthogonal, and that n and t are smooth over time. We formulate
the problem as a state-continuous Hidden Markov Model (HMM) where the
hidden state contains t and n and may be estimated by sampling and
decomposing homographies. We show that using blocked Gibbs sampling,
we can infer the hidden state with high robustness towards outliers,
drifting trajectories, rolling shutter and an imprecise intrinsic
calibration. Since our approach does not need any initial orientation
prior, it works for arbitrary camera orientations in which the ground
is visible.
