Fast Extrinsic Calibration for Multiple Inertial Measurement Units in Visual-Inertial System

TL;DR

This paper introduces a rapid extrinsic calibration technique for multiple IMUs in visual-inertial systems, enhancing localization accuracy and robustness by independently estimating sensor parameters and propagating virtual IMUs.

Contribution

The paper presents a novel non-linear least-squares calibration method for multiple IMUs that is fast, independent of external sensors, and improves visual-inertial odometry accuracy.

Findings

Fusing two IMUs with the proposed calibration rivals nine IMUs in accuracy.

The method outperforms existing techniques in speed, accuracy, and robustness.

Real-world experiments confirm improved localization accuracy with the new calibration.

Abstract

In this paper, we propose a fast extrinsic calibration method for fusing multiple inertial measurement units (MIMU) to improve visual-inertial odometry (VIO) localization accuracy. Currently, data fusion algorithms for MIMU highly depend on the number of inertial sensors. Based on the assumption that extrinsic parameters between inertial sensors are perfectly calibrated, the fusion algorithm provides better localization accuracy with more IMUs, while neglecting the effect of extrinsic calibration error. Our method builds two non-linear least-squares problems to estimate the MIMU relative position and orientation separately, independent of external sensors and inertial noises online estimation. Then we give the general form of the virtual IMU (VIMU) method and propose its propagation on manifold. We perform our method on datasets, our self-made sensor board, and board with different IMUs, validating the superiority of our method over competing methods concerning speed, accuracy, and robustness. In the simulation experiment, we show that only fusing two IMUs with our calibration method to predict motion can rival nine IMUs. Real-world experiments demonstrate better localization accuracy of the VIO integrated with our calibration method and VIMU propagation on manifold.