Continuous Event-Line Constraint for Closed-Form Velocity Initialization
This work addresses motion sensing in agile scenarios for robotics or autonomous systems, but it is incremental as it builds on existing constant-velocity and trifocal tensor assumptions.
The authors tackled the problem of estimating camera velocity from event camera data by proposing a continuous event-line constraint, resulting in a closed-form solver for up-to-scale linear camera velocity with known angular velocity, validated on simulated and real data.
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.