Real-time Visual-Inertial Odometry for Event Cameras using Keyframe-based Nonlinear Optimization


We propose a novel, accurate tightly-coupled visual-inertial odometry pipeline for such cameras that leverages the outstanding properties of event cameras to estimate the camera ego-motion in challenging conditions, such as high-speed motion or high dynamic range scenes. The method tracks a set of features (extracted on the image plane) through time. To achieve that, we consider events in overlapping spatio-temporal windows and align them using the current camera motion and scene structure, yielding motion-compensated event frames. We then combine these feature tracks in a keyframe-based, visual-inertial odometry algorithm based on nonlinear optimization to estimate the camera’s 6-DOF pose and velocity.

In British Machine Vision Conference (BMVC), IEEE.