  Direct methods for event-based visual odometry solve the mapping and camera
pose tracking sub-problems by establishing implicit data association in a way
that the generative model of events is exploited. The main bottlenecks faced by
state-of-the-art work in this field include the high computational complexity
of mapping and the limited accuracy of tracking. In this paper, we improve our
previous direct pipeline \textit{Event-based Stereo Visual Odometry} in terms
of accuracy and efficiency. To speed up the mapping operation, we propose an
efficient strategy of edge-pixel sampling according to the local dynamics of
events. The mapping performance in terms of completeness and local smoothness
is also improved by combining the temporal stereo results and the static stereo
results. To circumvent the degeneracy issue of camera pose tracking in
recovering the yaw component of general 6-DoF motion, we introduce as a prior
the gyroscope measurements via pre-integration. Experiments on publicly
available datasets justify our improvement. We release our pipeline as an
open-source software for future research in this field.
