  Event cameras are an interesting visual exteroceptive sensor that reacts to
brightness changes rather than integrating absolute image intensities. Owing to
this design, the sensor exhibits strong performance in situations of
challenging dynamics and illumination conditions. While event-based
simultaneous tracking and mapping remains a challenging problem, a number of
recent works have pointed out the sensor's suitability for prior map-based
tracking. By making use of cross-modal registration paradigms, the camera's
ego-motion can be tracked across a large spectrum of illumination and dynamics
conditions on top of accurate maps that have been created a priori by more
traditional sensors. The present paper follows up on a recently introduced
event-based geometric semi-dense tracking paradigm, and proposes the addition
of inertial signals in order to robustify the estimation. More specifically,
the added signals provide strong cues for pose initialization as well as
regularization during windowed, multi-frame tracking. As a result, the proposed
framework achieves increased performance under challenging illumination
conditions as well as a reduction of the rate at which intermediate event
representations need to be registered in order to maintain stable tracking
across highly dynamic sequences. Our evaluation focuses on a diverse set of
real world sequences and comprises a comparison of our proposed method against
a purely event-based alternative running at different rates.
