By João Paulo Lima, Hideaki Uchiyama, Rin-ichiro Taniguchi.

This repository contains the code for the paper "End-to-End Learning Framework for IMU-Based 6-DOF Odometry". You can find a demonstration video here.

• Python 3
• TensorFlow
• Keras
• NumPy
• Matplotlib
• scikit-learn
• Pandas
• SciPy
• numpy-quaternion
• tfquaternion

We provide training code that can use OxIOD or EuRoC MAV datasets.

1. Download the desired dataset and unzip it into the project folder (the path should be "<project folder>/Oxford Inertial Odometry Dataset/handheld/data<id>/" for OxIOD and "<project folder>/<sequence name>/mav0/" for EuRoC MAV)
2. Run python train.py dataset output, where dataset is either oxiod or euroc and output is the model output name (output.hdf5).

Pretrained models can be downloaded here:

• OxIOD
• EuRoC MAV

We provide code for trajectory prediction and visual comparison with ground truth trajectories from OxIOD or EuRoC MAV datasets.

1. Download the desired dataset and unzip it into the project folder (the path should be "<project folder>/Oxford Inertial Odometry Dataset/handheld/data<id>/" for OxIOD and "<project folder>/<sequence name>/mav0/" for EuRoC MAV)
2. Run python test.py dataset model input gt, where:

• dataset is either oxiod or euroc;
• model is the trained model file path (e.g. 6dofio_oxiod.hdf5);
• input is the input sequence path (e.g. "Oxford Inertial Odometry Dataset/handheld/data4/syn/imu1.csv" for OxIOD, "MH_02_easy/mav0/imu0/data.csv\" for EuRoC MAV);
• gt is the ground truth path (e.g. "Oxford Inertial Odometry Dataset/handheld/data4/syn/vi1.csv" for OxIOD, "MH_02_easy/mav0/state_groundtruth_estimate0/data.csv" for EuRoC MAV).

We provide code for computing trajectory RMSE for testing sequences from OxIOD or EuRoC MAV datasets.

1. Download the desired dataset and unzip it into the project folder (the path should be "<project folder>/Oxford Inertial Odometry Dataset/handheld/data<id>/" for OxIOD and "<project folder>/<sequence name>/mav0/" for EuRoC MAV)
2. Run python evaluate.py dataset model, where dataset is either oxiod or euroc and model is the trained model file path (e.g. 6dofio_oxiod.hdf5).

If you use this method in your research, please cite:

@article{lima2019end,
        title={End-to-End Learning Framework for IMU-Based 6-DOF Odometry},
        author={Silva do Monte Lima, Jo{\~a}o Paulo and Uchiyama, Hideaki and Taniguchi, Rin-ichiro},
        journal={Sensors},
        volume={19},
        number={17},
        pages={3777},
        year={2019},
        publisher={Multidisciplinary Digital Publishing Institute}
}

BSD