Continuous Event-Line Constraint for Closed-Form Velocity Initialization

TL;DR

This paper introduces a novel continuous event-line constraint for event cameras, enabling closed-form velocity initialization under known angular velocity, leveraging trifocal tensor geometry and nonlinear optimization for improved motion estimation.

Contribution

It presents a new continuous event-line constraint and a closed-form solver for linear camera velocity with known angular velocity, enhancing motion estimation from event camera data.

Findings

Effective velocity initialization demonstrated on simulated data

Improved accuracy with nonlinear optimization on real data

Applicable to agile motion scenarios with high dynamic range

Abstract

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.