  Event cameras are motion-activated sensors that capture pixel-level
illumination changes instead of the intensity image with a fixed frame rate.
Compared with the standard cameras, it can provide reliable visual perception
during high-speed motions and in high dynamic range scenarios. However, event
cameras output only a little information or even noise when the relative motion
between the camera and the scene is limited, such as in a still state. While
standard cameras can provide rich perception information in most scenarios,
especially in good lighting conditions. These two cameras are exactly
complementary. In this paper, we proposed a robust, high-accurate, and
real-time optimization-based monocular event-based visual-inertial odometry
(VIO) method with event-corner features, line-based event features, and
point-based image features. The proposed method offers to leverage the
point-based features in the nature scene and line-based features in the
human-made scene to provide more additional structure or constraints
information through well-design feature management. Experiments in the public
benchmark datasets show that our method can achieve superior performance
compared with the state-of-the-art image-based or event-based VIO. Finally, we
used our method to demonstrate an onboard closed-loop autonomous quadrotor
flight and large-scale outdoor experiments. Videos of the evaluations are
presented on our project website: https://b23.tv/OE3QM6j
