We have developed a far superior algorithm based on deep learning, which is simpler, more accurate and more generalizable:

RoNIN: Robust Nueral Inertial Navigation http://ronin.cs.sfu.ca

We highly recommend you try the new algorithm instead of RIDI. It is still beneficial to read the RIDI paper for our initial idea of data-driven inertial navigation, but I would NOT suggest you dive too deep into the code.

• Python: numpy, scipy, opencv-python (>3.0), numpy-quaternion, plyfile
• C++: Glog, Gflags, OpenCV (>3.0), Eigen (>3.3), Ceres-Solver, OpenMesh

The dataset used by this project is collected by a specific Tango App: https://github.com/higerra/TangoIMURecorder Please read the README.md file of this repository for detailed file format. If you do not have a Tango phone, you can use the alternative app: https://github.com/higerra/AndroidIMURecorder Note that without a Tango phone, the ground truth trajectory will not be available.

1. Clone the repository.
2. (Optional) Download the dataset from HERE and the pre-trained model from HERE. Note that the pre-trained model is trained from a small group of people. For the best result please train your own model.
3. For newly captured dataset, run python/gen_dataset.py(with Tango phone) or python/gen_dataset_nopose.py(without Tango phone) to preprocess the dataset. Please refer to the source code for command line arguments.
4. To train a model, run python/regression_cascade.py. Please refer to the source code for command line arguments. One possible call is: * python/regression_cascade.py --list <path-to-dataset-list> --model_output <path-to-output-folder>.
5. Compile the C++ source code: * cd <project-root> * mkdir cpp/build & cd cpp/build * cmake .. * make -j4
6. After the compilation finishes, run the following executable to perform RIDI: * cd <project-root>/cpp/build/imu_localization * ./IMULocalization_cli <path-to-dataset> --model_path <path-to-model>
7. The result will be written to <path-to-dataset>/result_full

Please cite the following paper is you use the code:

Yan, Hang, Qi Shan, and Yasutaka Furukawa. "RIDI: Robust IMU Double Integration." arXiv preprint arXiv:1712.09004 (2017).