Semi-Supervised Place Categorization Of 2D Occupancy Grid Maps
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README.md

Place-Categorization-2D-OGM

Semi-Supervised Place Categorization of Occupancy Grid Maps (OGM) In 2D

This repository conatins a python implementation of place categorization method, explained in this paper (link, pdf):

  • Shahbandi, Saeed Gholami, Björn Åstrand, and Roland Philippsen. "Semi-supervised semantic labeling of adaptive cell decomposition maps in well-structured environments." Mobile Robots (ECMR), 2015 European Conference on. IEEE, 2015.

The feature set employed in this work is inspired by (link):

  • Oscar Martinez Mozos "Semantic labeling of places with mobile robots" 2010, Springer Berlin Heidelberg.

Image below shows a simple demo of place categorization, with Kmean clustering where number of categories are set to two. For more details and more examples see the abovementioned paper. cover

Dependencies

To install dependencies:

git clone https://github.com/saeedghsh/Place-Categorization-2D.git
cd Place-Categorization-2D
pip install -r requirements.txt % opencv must be installed separately

Notes

  • This repository only contains the core method for place categorization, not the full method from the paper above. For instance:
    • It is not adaptive to environment types, and paramters must be set according to input maps (e.g. resolution). For a better performance, one need to tweak parameters of the clustering algorithm manually (or adaptively).
    • The decomposition of the 2D plane from the abovementioned paper ("Semi-supervised ...") is carried out by the arrangement package.

To be documented soon

  • Setting Paramters
  • Scripts
  • NumPy vectorization VS. multi-processing, and handling memory issue

License

Distributed with a GNU GENERAL PUBLIC LICENSE; see LICENSE.

Copyright (C) Saeed Gholami Shahbandi