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Multiple and Gyro-Free Inertial Datasets

Zeev Yampolsky, Yair Stolero, Nitzan Pri-Hadash, Dan Solodar, Shira Massas, Itai Savin, Itzik Klein

TL;DR

GFINS and MIMU datasets are designed and recorded using 54 inertial sensors grouped in nine inertial measurement units that can be used to define and evaluate different types of MIMU and GFINS architectures.

Abstract

An inertial navigation system (INS) utilizes three orthogonal accelerometers and gyroscopes to determine platform position, velocity, and orientation. There are countless applications for INS, including robotics, autonomous platforms, and the internet of things. Recent research explores the integration of data-driven methods with INS, highlighting significant innovations, improving accuracy and efficiency. Despite the growing interest in this field and the availability of INS datasets, no datasets are available for gyro-free INS (GFINS) and multiple inertial measurement unit (MIMU) architectures. To fill this gap and to stimulate further research in this field, we designed and recorded GFINS and MIMU datasets using 54 inertial sensors grouped in nine inertial measurement units. These sensors can be used to define and evaluate different types of MIMU and GFINS architectures. The inertial sensors were arranged in three different sensor configurations and mounted on a mobile robot and a passenger car. In total, the dataset contains 35 hours of inertial data and corresponding ground truth trajectories. The data and code are freely accessible through our GitHub repository.

Multiple and Gyro-Free Inertial Datasets

TL;DR

GFINS and MIMU datasets are designed and recorded using 54 inertial sensors grouped in nine inertial measurement units that can be used to define and evaluate different types of MIMU and GFINS architectures.

Abstract

An inertial navigation system (INS) utilizes three orthogonal accelerometers and gyroscopes to determine platform position, velocity, and orientation. There are countless applications for INS, including robotics, autonomous platforms, and the internet of things. Recent research explores the integration of data-driven methods with INS, highlighting significant innovations, improving accuracy and efficiency. Despite the growing interest in this field and the availability of INS datasets, no datasets are available for gyro-free INS (GFINS) and multiple inertial measurement unit (MIMU) architectures. To fill this gap and to stimulate further research in this field, we designed and recorded GFINS and MIMU datasets using 54 inertial sensors grouped in nine inertial measurement units. These sensors can be used to define and evaluate different types of MIMU and GFINS architectures. The inertial sensors were arranged in three different sensor configurations and mounted on a mobile robot and a passenger car. In total, the dataset contains 35 hours of inertial data and corresponding ground truth trajectories. The data and code are freely accessible through our GitHub repository.
Paper Structure (15 sections, 10 equations, 14 figures, 6 tables, 1 algorithm)

This paper contains 15 sections, 10 equations, 14 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Scientific Background
  3. INS Equations of Motion
  4. MIMU Equations of Motion
  5. GFINS Equations of Motion
  6. Proposed Approach
  7. Sensor Types
  8. Sensor Configurations
  9. Platforms
  10. Recording Protocol
  11. Results
  12. Data Validation
  13. Dataset Structure
  14. Conclusion
  15. Acknowledgements

Figures (14)

  • Figure 1: The box-shaped geometry is common to all three configurations, C1-C3. Each green dot represents a DOT sensor.
  • Figure 2: \ref{['image_conf1_TOP']}C1 - top-left view showing the MRU-P fitting snugly inside the DOT structure, and \ref{['image_conf1_FRONT']} front view showing four out of the eight DOTs.
  • Figure 3: Dimensions of C1, sub Figure \ref{['conf1_TOP']} top view showing the length along the $x$-axis and the width along the $y$-axis, sub Figure \ref{['conf1_FRONT']} front view showing the width along the $y$-axis and the height along the $z$-axis, and sub Figure \ref{['conf1_FRONT_ninthDOT']} front view showing the height of the ninth DOT.
  • Figure 4: Dimensions of C2 sub Figure \ref{['conf2_TOP']} top view showing the length along the $x$-axis in red, and the width along the $y$-axis in green, and sub Figure \ref{['conf2_FRONT']} the front view showing the width along the $y$-axis in green and the height along the $z$-axis in blue.
  • Figure 5: Dimensions of C3, sub Figure \ref{['conf3_TOP']} top view showing the length along the $x$-axis in red, and the width along the $y$-axis in green, and sub Figure \ref{['conf3_FRONT']} front view showing the width along the $y$-axis in green and the height along the $z$-axis in blue.
  • ...and 9 more figures