Semi-Supervised Place Categorization of Occupancy Grid Maps (OGM) In 2D
- 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.
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
- This repository only contains the core method for place categorization, not the full method from the paper above.
- 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
- NumPy vectorization VS. multi-processing, and handling memory issue
Distributed with a GNU GENERAL PUBLIC LICENSE; see LICENSE.
Copyright (C) Saeed Gholami Shahbandi