AI-IMU++ Dead-Reckoning [IEEE paper, ArXiv paper]

Overview

🔹In the context of intelligent vehicles, robust and accurate dead reckoning based on the Inertial Measurement Unit (IMU) may prove useful to correlate feeds from imaging sensors, to safely navigate through obstructions, or for safe emergency stop in the extreme case of other sensors failure.

This the repo of the Sk "Perception in robotics" project. Our goal was to improve filter performance adding new pseudo-measurement model.

You can inside the code you can find our "new" measurement function. For derivation and technical details check the paper.

Method

The above figure illustrates the approach which consists of two main blocks summarized as follows:

1. the filter integrates the inertial measurements with exploits zero lateral and vertical velocity as measurements with covariance matrix to refine its estimates, see the figure below;
2. the noise parameter adapter determines in real-time the most suitable covariance noise matrix. This deep learning based adapter converts directly raw IMU signals into covariance matrices without requiring knowledge of any state estimate nor any other quantity.

Code

Our implementation is done in Python. We use Pytorch for the adapter block of the system. The code was tested under Python 3.5.

Installation & Prerequies

1. Install pytorch. We perform all training and testing on its development branch.

2. Install the following required packages, matplotlib, numpy, termcolor, scipy, navpy, e.g. with the pip3 command

pip3 install matplotlib numpy termcolor scipy navpy

or with conda:

conda env create -f environment.yml
conda activate ai-imu

or with Docker:

docker build -t ai-imu .  
docker run --gpus all -it -v ($pwd):/project --rm ai-imu

inside container to activate env:

source /opt/conda/etc/profile.d/conda.sh
conda activate ai-imu

4. Clone this repo

git clone https://github.com/mbrossar/ai-imu-dr.git

The origianal code contained error, for details check the paper

Testing

1. Download reformated pickle format of the 00-11 KITTI IMU raw data at this url, extract and copy then in the data folder.

wget "https://github.com/user-attachments/files/17930695/data.zip"
mkdir ai-imu-dr/results
unzip data.zip -d ai-imu-dr
rm data.zip

These file can alternatively be generated after download the KITTI raw data and setting read_data = 1 in the main.py file.

2. Download training parameters at this url(low number of epochs), extract and copy in the temp folder.

wget "https://github.com/DragonEmperorG/ai-imu-dr/raw/refs/heads/master/temp/temp.zip"
unzip temp.zip -d ai-imu-dr/temp
rm temp.zip

4. Test the filters !

mkdir ai-imu-dr/results
cd ai-imu-dr/src
python3 main_kitti.py

This first launches the filters for the all sequences. Then, results are plotted. Note that the parameters are trained on sequences 00, 01, 04-11, so only sequence 02 is a test sequence.

Training

You can train for testing another sequence (we do not find difference in the results) or for our own sequence by modifying the dataset class.

Uncomment some strings, below:

# choose what to do

Paper

The paper M. Brossard, A. Barrau and S. Bonnabel, "AI-IMU Dead-Reckoning," in IEEE Transactions on Intelligent Vehicles, 2020, relative to this repo is available at this url.

Citation

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

@article{brossard2019aiimu,
  author = {Martin Brossard and Axel Barrau and Silv\`ere Bonnabel},
  journal={IEEE Transactions on Intelligent Vehicles}, 
  title = {{AI-IMU Dead-Reckoning}},
  year = {2020}
}

Authors

Xuan Nguyen*

Alymov Maksim*

*Skolkovo Institute of Science and Technology

Authors of the origial method

Martin Brossard*, Axel Barrau° and Silvère Bonnabel*

*MINES ParisTech, PSL Research University, Centre for Robotics, 60 Boulevard Saint-Michel, 75006 Paris, France

°Safran Tech, Groupe Safran, Rue des Jeunes Bois-Châteaufort, 78772, Magny Les Hameaux Cedex, France

Steps to improve

• To adapt code to train it on cuda device
• To tune initial parameters of the new measurements model