Multiple and Gyro-Free Inertial Datasets
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.
Keywords
Cite
@article{arxiv.2403.15494,
title = {Multiple and Gyro-Free Inertial Datasets},
author = {Zeev Yampolsky and Yair Stolero and Nitzan Pri-Hadash and Dan Solodar and Shira Massas and Itai Savin and Itzik Klein},
journal= {arXiv preprint arXiv:2403.15494},
year = {2024}
}
Comments
10 pages, 16 figures, 6 tables