  The motion of planar ground vehicles is often non-holonomic, and as a result
may be modelled by the 2 DoF Ackermann steering model. We analyse the
feasibility of estimating such motion with a downward facing camera that exerts
fronto-parallel motion with respect to the ground plane. This turns the motion
estimation into a simple image registration problem in which we only have to
identify a 2-parameter planar homography. However, one difficulty that arises
from this setup is that ground-plane features are indistinctive and thus hard
to match between successive views. We encountered this difficulty by
introducing the first globally-optimal, correspondence-less solution to
plane-based Ackermann motion estimation. The solution relies on the
branch-and-bound optimisation technique. Through the low-dimensional
parametrisation, a derivation of tight bounds, and an efficient implementation,
we demonstrate how this technique is eventually amenable to accurate real-time
motion estimation. We prove its property of global optimality and analyse the
impact of assuming a locally constant centre of rotation. Our results on real
data finally demonstrate a significant advantage over the more traditional,
correspondence-based hypothesise-and-test schemes.
