This repository contains the code to our paper. For questions feel free to open an issue or send an e-mail to [email protected].

This code was tested on Arch Linux with Python 3.10, PyTorch 2.2 and CUDA 12.3.

1. Clone the repository onto your local system.

2. Create a virtual environment and activate the created virtual environment(tested on Python 3.10).

3. Install the necessary packages from the requirements file with:

   python -m pip install -r requirements.txt

4. Follow main_EuRoC.py to build your own train test flow.

5. (Optinal) Download the dataset (EuRoC [1] and TUM-VI [2]) and decompress it in data folder.

If you find the project helpful, or use the code or paper from this repository in your research, please consider citing us:

@article{liu2023duet,
  author={H. {Liu} and X. {Wei} and M. {Perusquía-Hernández} and I. {Naoya} and H. {Uchiyama} and K. {Kiyokawa}},
  journal={IEEE Transactions on Instrumentation and Measurement},
  title={DUET: Improving Inertial-Based Odometry via Deep IMU Online Calibration},
  year={2023},
  volume={72},
  number={},
  pages={1-13},
}

[1] M. Burri, J. Nikolic, P. Gohl, T. Schneider, J. Rehder, S. Omari, M. W. Achtelik, and R. Siegwart, ``The EuRoC Micro Aerial Vehicle Datasets", The International Journal of Robotics Research, vol. 35, no. 10, pp. 1157–1163, 2016.

[2] D. Schubert, T. Goll, N. Demmel, V. Usenko, J. Stuckler, and D. Cremers, ``The TUM VI Benchmark for Evaluating Visual-Inertial Odometry", in International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 1680–1687, 2018.