  Despite the promise of superior performance under challenging conditions,
event-based motion estimation remains a hard problem owing to the difficulty of
extracting and tracking stable features from event streams. In order to
robustify the estimation, it is generally believed that fusion with other
sensors is a requirement. In this work, we demonstrate reliable, purely
event-based visual odometry on planar ground vehicles by employing the
constrained non-holonomic motion model of Ackermann steering platforms. We
extend single feature n-linearities for regular frame-based cameras to the case
of quasi time-continuous event-tracks, and achieve a polynomial form via
variable degree Taylor expansions. Robust averaging over multiple event tracks
is simply achieved via histogram voting. As demonstrated on both simulated and
real data, our algorithm achieves accurate and robust estimates of the
vehicle's instantaneous rotational velocity, and thus results that are
comparable to the delta rotations obtained by frame-based sensors under normal
conditions. We furthermore significantly outperform the more traditional
alternatives in challenging illumination scenarios. The code is available at
\url{https://github.com/gowanting/NHEVO}.
