  We present a method that leverages the complementarity of event cameras and
standard cameras to track visual features with low-latency. Event cameras are
novel sensors that output pixel-level brightness changes, called "events". 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,
because the same scene pattern can produce different events depending on the
motion direction, establishing event correspondences across time is
challenging. By contrast, standard cameras provide intensity measurements
(frames) that do not depend on motion direction. Our method extracts features
on frames and subsequently tracks them asynchronously using events, thereby
exploiting the best of both types of data: the frames provide a photometric
representation that does not depend on motion direction and the events provide
low-latency updates. In contrast to previous works, which are based on
heuristics, this is the first principled method that uses raw intensity
measurements directly, based on a generative event model within a
maximum-likelihood framework. As a result, our method produces feature tracks
that are both more accurate (subpixel accuracy) and longer than the state of
the art, across a wide variety of scenes.
