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Visual-Inertial (VI) sensors are popular in robotics, self-driving vehicles,and augmented and virtual reality applications. In order to use them for anycomputer vision or state-estimation task, a good calibration is essential.However, collecting informative calibration data in order to render thecalibration parameters observable is not trivial for a non-expert. In thiswork, we introduce a novel VI calibration pipeline that guides a non-expertwith the use of a graphical user interface and information theory in collectinginformative calibration data with Next-Best-View and Next-Best-Trajectorysuggestions to calibrate the intrinsics, extrinsics, and temporal misalignmentof a VI sensor. We show through experiments that our method is faster, moreaccurate, and more consistent than state-of-the-art alternatives. Specifically,we show how calibrations with our proposed method achieve higher accuracyestimation results when used by state-of-the-art VI Odometry as well as VI-SLAMapproaches. The source code of our software can be found on:https://github.com/chutsu/yac.

Accurate and Interactive Visual-Inertial Sensor Calibration with Next-Best-View and Next-Best-Trajectory Suggestion