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

Youwei Yu, Yanqing Liu, Fengjie Fu, Sihan He, Dongchen Zhu, Lei Wang, Xiaolin Zhang, Jiamao Li

TL;DR

A fast extrinsic calibration method for fusing multiple inertial measurement units (MIMU) to improve visual-inertial odometry (VIO) localization accuracy and real-world experiments demonstrate better localization accuracy of the VIO integrated with the calibration method and VIMU propagation on manifold.

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.

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

TL;DR

A fast extrinsic calibration method for fusing multiple inertial measurement units (MIMU) to improve visual-inertial odometry (VIO) localization accuracy and real-world experiments demonstrate better localization accuracy of the VIO integrated with the calibration method and VIMU propagation on manifold.

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.
Paper Structure (18 sections, 22 equations, 5 figures, 3 tables)

This paper contains 18 sections, 22 equations, 5 figures, 3 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. MIMU Data Fusion
  4. MIMU Extrinsic Calibration
  5. FAST EXTRINSIC CALIBRATION FOR MIMU
  6. State Variables and Frames
  7. IMU Measurement Model
  8. Problem Statement
  9. Calibration Solution Approach
  10. VIRTUAL IMU METHOD ON MANIFOLD
  11. Virtual IMU General Model
  12. Virtual IMU Propagation on Manifold
  13. EXPERIMENTS
  14. Real-World Extrinsic Calibration Experiments
  15. Simulation Experiments of IMU Integration
  16. ...and 3 more sections

Figures (5)

  • Figure 1: The binocular and two-IMU sensor board used for extrinsic calibration comparison and VIO experiments. Stereo images are synchronized, and inertial measurements have exact timestamps.
  • Figure 2: The RealSense T265 and D435i sensor board.
  • Figure 3: The simulated IMU array. [Red]: X axis; [Green]: Y axis; [Blue]: Z axis.
  • Figure 4: RMSE in velocity, orientation angle, and position with IMU preintegration. The numbers 2, 4, and 9 have tiny extrinsic errors, plotted with the error bar. [Top]: predicted velocity RMSE; [Middle]: predicted orientation angle RMSE; [Bottom]: predicted position RMSE.
  • Figure 5: Examples of images in real-world experiments captured with our self-made sensor board.