Tight Fusion of Events and Inertial Measurements for Direct Velocity
  Estimation
Wanting Xu Xin Peng Laurent Kneip
Traditional visual-inertial state estimation targets absolute camera poses
and spatial landmark locations while first-order kinematics are typically
resolved as an implicitly estimated sub-state. However, this poses a risk in
velocity-based control scenarios, as the quality of the estimation of
kinematics depends on the stability of absolute camera and landmark coordinates
estimation. To address this issue, we propose a novel solution to tight
visual-inertial fusion directly at the level of first-order kinematics by
employing a dynamic vision sensor instead of a normal camera. More
specifically, we leverage trifocal tensor geometry to establish an incidence
relation that directly depends on events and camera velocity, and demonstrate
how velocity estimates in highly dynamic situations can be obtained over short
time intervals. Noise and outliers are dealt with using a nested two-layer
RANSAC scheme. Additionally, smooth velocity signals are obtained from a tight
fusion with pre-integrated inertial signals using a sliding window optimizer.
Experiments on both simulated and real data demonstrate that the proposed tight
event-inertial fusion leads to continuous and reliable velocity estimation in
highly dynamic scenarios independently of absolute coordinates. Furthermore, in
extreme cases, it achieves more stable and more accurate estimation of
kinematics than traditional, point-position-based visual-inertial odometry.