This repo contains a real-time approach for inertial navigation based only on an Inertial MeasurementUnit (IMU) for self-localizing wheeled robots. The approach builds upon two components: 1) a robust detector that uses deep neural networks to dynamically detects zero velocity; and 2) a state-of-the-art Kalman filter which incorporates this knowledge along with no lateral slip and vertical velocity as pseudo-measurements for localization.
Our implementation is done in Python a Pytorch for the adapter block of the system. The code was tested under Python 3.5.
-
Install pytorch. We perform all training and testing on its 1.5 version.
-
Install required packages, e.g. with the pip3 command
pip3 install requirements.txt
- Clone this repo
git clone https://github.com/mbrossar/RINS-W.git
Coming soon.
Coming soon.
This repo is mainly based on the paper "RINS-W: Robust Inertial Navigation System on Wheels", International Conference on Intelligent Robots and Systems (IROS), 2019 [IEEE paper, ArXiv paper]. The main differences with the paper are
- deep neural networks only estimates when zero velocity happens.
- deep neural networks is based on dilated convolutions and CNNs, which are much faster to train.
- the covariance of pseudo-measurement may depends on IMU inputs.
You can also see also the paper "AI-IMU Dead-Reckoning," IEEE Transactions on Intelligent Vehicles, 2020 [IEEE paper, ArXiv paper].
If you use this code in your research, please cite:
@INPROCEEDINGS{brossard2019,
author={M. {Brossard} and A. {Barrau} and S. {Bonnabel}},
booktitle={2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
title={{RINS-W: Robust Inertial Navigation System on Wheels}},
year={2019},
volume={},
number={},
pages={2068-2075},
}
@ARTICLE{9035481,
author={M. {Brossard} and A. {Barrau} and S. {Bonnabel}},
journal={IEEE Transactions on Intelligent Vehicles},
title={{AI-IMU Dead-Reckoning}},
year={2020},
volume={},
number={},
pages={},
}
Martin Brossard*, Axel Barrau* and Silvère Bonnabel*
*MINES ParisTech, PSL Research University, Centre for Robotics, 60 Boulevard Saint-Michel, 75006 Paris, France