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}
}

1. 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.