This repository corresponds to the work entitled "Unsupervised appearance map abstraction for indoor Visual Place Recognition with mobile robots", published at IEEE Robotics and Automation Letters.
Authors: Alberto Jaenal, Francisco-Angel Moreno and Javier Gonzalez-Jimenez
Video: Click Here
If you use this work in your research, please cite:
@article{jaenal2023sequential,
title={Sequential Monte Carlo localization in topometric appearance maps},
author={Jaenal, Alberto and Moreno, Francisco-Angel and Gonzalez-Jimenez, Javier},
journal={The International Journal of Robotics Research},
pages={02783649231197723},
year={2023},
publisher={SAGE Publications Sage UK: London, England}
}
- Check the jupyter called Appearance-based Localization with Local Observation Models.ipynb.
Optional. There is an available implementation of the Gaussian Process Particle Filter
This software employs built-in libs (see requeriments.txt), and has been tested with Python>=3.5 on Ubuntu 16.04, 18.04 and 20.04.
The geometry.py script is inspired in ProbFiltersVPR.
This repo reuses the Expectation-Maximization algorithm for Topological GAM generation, which is also available here.