  We present a solution to the problem of spatio-temporal calibration for event
cameras mounted on an onmi-directional vehicle. Different from traditional
methods that typically determine the camera's pose with respect to the
vehicle's body frame using alignment of trajectories, our approach leverages
the kinematic correlation of two sets of linear velocity estimates from event
data and wheel odometers, respectively. The overall calibration task consists
of estimating the underlying temporal offset between the two heterogeneous
sensors, and furthermore, recovering the extrinsic rotation that defines the
linear relationship between the two sets of velocity estimates. The first
sub-problem is formulated as an optimization one, which looks for the optimal
temporal offset that maximizes a correlation measurement invariant to arbitrary
linear transformation. Once the temporal offset is compensated, the extrinsic
rotation can be worked out with an iterative closed-form solver that
incrementally registers associated linear velocity estimates. The proposed
algorithm is proved effective on both synthetic data and real data,
outperforming traditional methods based on alignment of trajectories.
