  Event cameras trigger events asynchronously and independently upon a
sufficient change of the logarithmic brightness level. The neuromorphic sensor
has several advantages over standard cameras including low latency, absence of
motion blur, and high dynamic range. Event cameras are particularly well suited
to sense motion dynamics in agile scenarios. We propose the continuous
event-line constraint, which relies on a constant-velocity motion assumption as
well as trifocal tensor geometry in order to express a relationship between
line observations given by event clusters as well as first-order camera
dynamics. Our core result is a closed-form solver for up-to-scale linear camera
velocity {with known angular velocity}. Nonlinear optimization is adopted to
improve the performance of the algorithm. The feasibility of the approach is
demonstrated through a careful analysis on both simulated and real data.
