Multiple and Gyro-Free Inertial Datasets

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

Research output: Contribution to journalArticlepeer-review

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, a passenger car and a turntable. In total, the dataset contains 45 hours of inertial data and corresponding ground truth trajectories. The data is freely accessible through our figshare repository.

Original languageEnglish
Pages (from-to)1080
Number of pages1
JournalScientific data
Volume11
Issue number1
DOIs
StatePublished - 3 Oct 2024

Bibliographical note

Publisher Copyright:
© 2024. The Author(s).

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences

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