Accurate and Interactive Visual-Inertial Sensor Calibration with
  Next-Best-View and Next-Best-Trajectory Suggestion
Christopher L. Choi Binbin Xu Stefan Leutenegger
Visual-Inertial (VI) sensors are popular in robotics, self-driving vehicles,
and augmented and virtual reality applications. In order to use them for any
computer vision or state-estimation task, a good calibration is essential.
However, collecting informative calibration data in order to render the
calibration parameters observable is not trivial for a non-expert. In this
work, we introduce a novel VI calibration pipeline that guides a non-expert
with the use of a graphical user interface and information theory in collecting
informative calibration data with Next-Best-View and Next-Best-Trajectory
suggestions to calibrate the intrinsics, extrinsics, and temporal misalignment
of a VI sensor. We show through experiments that our method is faster, more
accurate, and more consistent than state-of-the-art alternatives. Specifically,
we show how calibrations with our proposed method achieve higher accuracy
estimation results when used by state-of-the-art VI Odometry as well as VI-SLAM
approaches. The source code of our software can be found on:
https://github.com/chutsu/yac.