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

Continuous Event-Line Constraint for Closed-Form Velocity Initialization