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License CC BY-NC-SA 4.0 Python 3.6

!!!UPDATE on 23/04/2022:

This repository maintains the original implemenation of milliEgo and require some dependency on Keras. A more updated version purly with the lastest Tensorflow 2 implemenation can be found in this new repository. Thank Xiangyu for the great code migration.

Youtube | Paper

Simplified docker version for the implementation of our 6-DOF Egomotion Estimation method via a single-chip mmWave radar (TI AWR1843) and a commercial-grade IMU. Our method is the first-of-its-kind DNN based odometry approach that can estimate the egomotion from the sparse and noisy data returned by a single-chip mmWave radar.

milliEgo: Single-chip mmWave Aided Egomotion Estimation with Deep Sensor Fusion
Chris Xiaoxuan Lu, Muhamad Risqi U. Saputra, Peijun Zhao, Yasin Almalioglu, Pedro P. B. de Gusmao, Changhao Chen, Ke Sun, Niki Trigoni, Andrew Markham In SenSys 2020.

Prerequisites

  • Linux
  • Docker
  • Python 3.6.8
  • CUDNN 9.0

Getting Started

Docker Installation

Make a tensorflow 1.9.0 docker environment. Install nvidia-docker with https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker Simply install docker on your machine and pull the correct version of tensorflow docker:

docker pull tensorflow/tensorflow:1.9.0-gpu-py3

Pre-trained mmWave Radar Feature Extractor and milliEgo model

  • After git clone this repository, enter the project directory,
mkdir -p models/cross-mio
  • Download the pre-trained CNN model 'cnn.h5' (dropbox link) for mmWave feature extraction and put it under ./models/
  • Download the trained milliEgo model '140' and the respective config file nn_opt.json from here (dropbox link). Put both of them in ./models/cross-mio/.

Dataset

  • To train and test a model, please go to this repository and find the link for downloading.

  • After downloading and unzip, please put the dataset folder in <host dataset dir path> of your host machine.

Start the docker container

Suppose dataset is stored in host machine under <host dataset dir path>. Run docker with:

docker run --gpus all -it --rm -v <host dataset dir>:/datasets/multi_gap_5 tensorflow/tensorflow:1.9.0-gpu-py3 bash

Note the 'multi_gap_5' is dummy directory name but essentially it implies the down-sampling intervals of the mmWave radar data - you should have enough parallax for a good visual odometry.

Install dependencies in docker container:

pip install tensorflow-estimator==1.14.0
pip install keras==2.1.6
apt-get update -y
apt-get install python3-tk

Testing

In host machine, copy code folder into docker container.

docker cp <host code dir> <container ID>:/code

In the docker container, run testing on pre-trained model (cross-attention):

cd /code
python test_trajectory.py

Check the generated trajectories in /code/figs and results to be quantitatively evaluated in /code/results.

Training

In docker container, run training:

python /code/train_cross_att.py

Citation

If you find this useful for your research, please use the following.

@inproceedings{lu2020milliego,
  title={milliEgo: single-chip mmWave radar aided egomotion estimation via deep sensor fusion},
  author={Lu, Chris Xiaoxuan and Saputra, Muhamad Risqi U and Zhao, Peijun and Almalioglu, Yasin and de Gusmao, Pedro PB and Chen, Changhao and Sun, Ke and Trigoni, Niki and Markham, Andrew},
  booktitle={Proceedings of the 18th Conference on Embedded Networked Sensor Systems (SenSys)},
  year={2020}
}