  This paper presents an online initialization method for bootstrapping the
optimization-based monocular visual-inertial odometry (VIO). The method can
online calibrate the relative transformation (spatial) and time offsets
(temporal) among camera and IMU, as well as estimate the initial values of
metric scale, velocity, gravity, gyroscope bias, and accelerometer bias during
the initialization stage. To compensate for the impact of time offset, our
method includes two short-term motion interpolation algorithms for the camera
and IMU pose estimation. Besides, it includes a three-step process to
incrementally estimate the parameters from coarse to fine. First, the extrinsic
rotation, gyroscope bias, and time offset are estimated by minimizing the
rotation difference between the camera and IMU. Second, the metric scale,
gravity, and extrinsic translation are approximately estimated by using the
compensated camera poses and ignoring the accelerometer bias. Third, these
values are refined by taking into account the accelerometer bias and the
gravitational magnitude. For further optimizing the system states, a nonlinear
optimization algorithm, which considers the time offset, is introduced for
global and local optimization. Experimental results on public datasets show
that the initial values and the extrinsic parameters, as well as the sensor
poses, can be accurately estimated by the proposed method.
