# Multiple and Gyro-Free Inertial Datasets

arXiv: https://arxiv.org/abs/2403.15494 · HTML: https://mlsanity.com/paper/2403.15494/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 12 of 12)

- [RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, and New Methods](https://mlsanity.com/paper/1905.12853/) (2019)
- [TLIO: Tight Learned Inertial Odometry](https://mlsanity.com/paper/2007.01867/) (2020)
- [RIDI: Robust IMU Double Integration](https://mlsanity.com/paper/1712.09004/) (2017)
- [NTU VIRAL: A Visual-Inertial-Ranging-Lidar Dataset, From an Aerial Vehicle Viewpoint](https://mlsanity.com/paper/2202.00379/) (2022)
- [Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimation](https://mlsanity.com/paper/2002.10718/) (2020)
- [ADVIO: An authentic dataset for visual-inertial odometry](https://mlsanity.com/paper/1807.09828/) (2018)
- [OxIOD: The Dataset for Deep Inertial Odometry](https://mlsanity.com/paper/1809.07491/) (2018)
- [BeamsNet: A data-driven Approach Enhancing Doppler Velocity Log Measurements for Autonomous Underwater Vehicle Navigation](https://mlsanity.com/paper/2206.13603/) (2022)
- [Deep IMU Bias Inference for Robust Visual-Inertial Odometry with Factor Graphs](https://mlsanity.com/paper/2211.04517/) (2022)
- [VIO-DualProNet: Visual-Inertial Odometry with Learning Based Process Noise Covariance](https://mlsanity.com/paper/2308.11228/) (2023)
- [A Lightweight and Accurate Localization Algorithm Using Multiple Inertial Measurement Units](https://mlsanity.com/paper/1909.04869/) (2019)
- [IO-VNBD: Inertial and Odometry Benchmark Dataset for Ground Vehicle Positioning](https://mlsanity.com/paper/2005.01701/) (2020)

## Built on by (most cited first, 2 of 2)

- [PiDR: Physics-Informed Inertial Dead Reckoning for Autonomous Platforms](https://mlsanity.com/paper/2601.03040/) (2026)
- [Data-Driven Gyroscope Calibration](https://mlsanity.com/paper/2410.12485/) (2024)

## Abstract (arXiv)

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.