  Event cameras are bio-inspired sensors that perform well in HDR conditions
and have high temporal resolution. However, different from traditional
frame-based cameras, event cameras measure asynchronous pixel-level brightness
changes and return them in a highly discretised format, hence new algorithms
are needed. The present paper looks at fronto-parallel motion estimation of an
event camera. The flow of the events is modeled by a general homographic
warping in a space-time volume, and the objective is formulated as a
maximisation of contrast within the image of unwarped events. However, in stark
contrast to prior art, we derive a globally optimal solution to this generally
non-convex problem, and thus remove the dependency on a good initial guess. Our
algorithm relies on branch-and-bound optimisation for which we derive novel,
recursive upper and lower bounds for six different contrast estimation
functions. The practical validity of our approach is supported by a highly
successful application to AGV motion estimation with a downward facing event
camera, a challenging scenario in which the sensor experiences fronto-parallel
motion in front of noisy, fast moving textures.
