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

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

The dataset used by this project is collected using an App for Google Tango Device and an App for any Android Device, and pre_processed to the data format specified here Please refer to our paper for more details on data collection.

You can download the RoNIN dataset from our project website or HERE. Unfortunately, due to security concerns we were unable to publish 50% of our dataset.

Optionally, you can write a custom dataloader (E.g: soure/data_ridi.py) to load a different dataset.

1. Clone the repository.
2. (Optional) Download the dataset from HERE 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-train-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

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.