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

TL;DR

This paper presents a novel visual-inertial sensor calibration method that uses a graphical interface and information theory to guide non-experts in collecting optimal calibration data, resulting in faster and more accurate calibration.

Contribution

It introduces an interactive calibration pipeline with Next-Best-View and Next-Best-Trajectory suggestions, improving calibration speed and accuracy for VI sensors.

Findings

Our method outperforms state-of-the-art calibration techniques in speed and accuracy.

Calibrations using our approach lead to higher accuracy in VI odometry and VI-SLAM.

The software implementation is publicly available for use and further research.

Abstract

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.