  Event-based cameras asynchronously capture individual visual changes in a
scene. This makes them more robust than traditional frame-based cameras to
highly dynamic motions and poor illumination. It also means that every
measurement in a scene can occur at a unique time.
  Handling these different measurement times is a major challenge of using
event-based cameras. It is often addressed in visual odometry (VO) pipelines by
approximating temporally close measurements as occurring at one common time.
This grouping simplifies the estimation problem but, absent additional sensors,
sacrifices the inherent temporal resolution of event-based cameras.
  This paper instead presents a complete stereo VO pipeline that estimates
directly with individual event-measurement times without requiring any grouping
or approximation in the estimation state. It uses continuous-time trajectory
estimation to maintain the temporal fidelity and asynchronous nature of
event-based cameras through Gaussian process regression with a physically
motivated prior. Its performance is evaluated on the MVSEC dataset, where it
achieves 7.9e-3 and 5.9e-3 RMS relative error on two independent sequences,
outperforming the existing publicly available event-based stereo VO pipeline by
two and four times, respectively.
