We provide the equivalent Matlab code for designing UKF on (parallelizable) manifolds, which is wholly independent from the python code.
The Matlab code has been tested with version R2019a without requiring any particular toolbox. To install:
- Download the repo
git clone https://github.com/CAOR-MINES-ParisTech/ukfm.git
-
Go to /my/directory/ukfm/matlab at the Matlab prompt and execute
importukfm. -
You may save this path for your next Matlab sessions (via
savepath).
Follow the 2D robot localization example (tutorial): in the Matlab prompt execute
main_localization
In contrast to Python, the code is implemented without class and has only functions.
The file for designing an UKF are given in the ukfm folder and useful
geometry (Lie groups) functions are provided in the geometry folder.
We provide scripts for reproducing the examples and benchmarks respectively in
the examples and benchmarks folders. Models functions are organizedin
subfolder of the example folder: for e.g. the 2D robot localization model, see
in examples/localization. You can use the Matlab publish tool for better
rendering and obtain the published scripts
here.
Function naming mimics the dot operator of class. To get the exponential of
$SE(3)` or the propagation function of the localization example, call
respectively se3_exp and ``localization_f``.
If you use this project for your research, please please cite
@article{brossard2019Code,
author={Martin Brossard and Axel Barrau and Silvère Bonnabe},
title={{A Code for Unscented Kalman Filtering on Manifolds (UKF-M)}},
year={2019},
}