RoNIN: Robust Neural Inertial Navigation in the Wild

Paper: ICRA 2020, arXiv
Website: http://ronin.cs.sfu.ca/
Demo: https://youtu.be/JkL3O9jFYrE

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Requirements

python3, numpy, scipy, pandas, h5py, numpy-quaternion, matplotlib, torch, torchvision, tensorboardX, numba, plyfile, tqdm, scikit-learn

tested environment

• Ubuntu 22.04, python 3.8, cudatoolkit 11.8, numpy 1.24.3

  conda create -n ronin python=3.8
  conda activate ronin

• Installed pytorch,torchvision,torchaudio using this command

  conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia

• Install requirements with requirements.txt

  pip install -r requirements.txt

Data

RoNIN Dataset

• Project Website
• FRDR

RoNIN Dataset Description

• Project Website

Custom iOS Recording Application

• [VIO] ARKit
• [IMU] CoreMotion

Usage:

1. Clone the repository.
2. (Optional) Download the dataset and the pre-trained models¹ from HERE.
3. Position Networks

   1. To train/test RoNIN ResNet model:

      • run source/ronin_resnet.py with mode argument. Please refer to the source code for the full list of command line arguments.

      • Example training command:

        python ronin_resnet.py --mode train --train_list <path-to-train-list> --root_dir <path-to-dataset-folder> --out_dir <path-to-output-folder>

      • Example testing command:

        python ronin_resnet.py --mode test --test_list <path-to-test-list> --root_dir <path-to-dataset-folder> --out_dir <path-to-output-folder> --model_path <path-to-model-checkpoint>

   2. To train/test RoNIN LSTM or RoNIN TCN model:

      • run source/ronin_lstm_tcn.py with mode (train/test) and model type. Please refer to the source code for the full list of command line arguments. Optionally you can specify a configuration file such as config/temporal_model_defaults.json with the data paths.
      • Example training command:

        python ronin_lstm_tcn.py train --type tcn --config <path-to-your-config-file> --out_dir <path-to-output-folder> --use_scheduler

      • Example testing command:

        python ronin_lstm_tcn.py test --type tcn --test_list <path-to-test-list> --data_dir <path-to-dataset-folder> --out_dir <path-to-output-folder> --model_path <path-to-model-checkpoint>

4. Heading Network
   • run source/ronin_body_heading.py with mode (train/test). Please refer to the source code for the full list of command line arguments. Optionally you can specify a configuration file such as config/heading_model_defaults.json with the data paths.
   • Example training command:

     python ronin_body_heading.py train --config <path-to-your-config-file> --out_dir <path-to-output-folder> --weights 1.0,0.2

   • Example testing command:

     python ronin_body_heading.py test --config <path-to-your-config-file> --test_list <path-to-test-list>  --out_dir <path-to-output-folder> --model_path <path-to-model-checkpoint>

¹ The models are trained on the entire dataset

Citation

Please cite the following paper is you use the code, paper or data:
Herath, S., Yan, H. and Furukawa, Y., 2020, May. RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, & New Methods. In 2020 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3146-3152). IEEE.