Title: Continuous-Time Visual-Inertial Odometry for Event Cameras,
Abstract: Event cameras are bio-inspired vision sensors that output pixel-level
brightness changes instead of standard intensity frames. They offer significant
advantages over standard cameras, namely a very high dynamic range, no motion
blur, and a latency in the order of microseconds. However, due to the
fundamentally different structure of the sensor's output, new algorithms that
exploit the high temporal resolution and the asynchronous nature of the sensor
are required. Recent work has shown that a continuous-time representation of
the event camera pose can deal with the high temporal resolution and
asynchronous nature of this sensor in a principled way. In this paper, we
leverage such a continuous-time representation to perform visual-inertial
odometry with an event camera. This representation allows direct integration of
the asynchronous events with micro-second accuracy and the inertial
measurements at high frequency. The event camera trajectory is approximated by
a smooth curve in the space of rigid-body motions using cubic splines. This
formulation significantly reduces the number of variables in trajectory
estimation problems. We evaluate our method on real data from several scenes
and compare the results against ground truth from a motion-capture system. We
show that our method provides improved accuracy over the result of a
state-of-the-art visual odometry method for event cameras. We also show that
both the map orientation and scale can be recovered accurately by fusing events
and inertial data. To the best of our knowledge, this is the first work on
visual-inertial fusion with event cameras using a continuous-time framework.