git clone --recursive https://github.com/MAPIRlab/igdm.git
We contribute with an efficient exploration algorithm for 2D gas-distribution mapping with an autonomous mobile robot. Our proposal combines a Gaussian Markov random field estimator based on gas and wind measurements that is devised for very sparse sample sizes and indoor environments, with a partially observable Markov decision process to close the robot's control loop.
The simplest starting point is running our test example as follows:
python igdm/pomdp/test.py
It will run a simulated exploration of a indoor-like environment and report both to terminal as well as to disk.
This project is only possible thanks to the effort of many, including mentors, innovators, developers, and of course, our beloved coffe vending machine. If you like this project and want to contribute in any way, you are most welcome.
Yoy can find a detailed list of everyone involved involved in the development of this software in AUTHORS.md. Thanks to all of you!
This software was developed in a collaboration between the Machine Perception and Intelligent Robotics (MAPIR) research group, University of Malaga, and the Distributed Intelligent Systems and Algorithms Laboratory (DISAL) research group, École Polytechnique Fédérale de Lausanne (EPFL).
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This software is released under a GPLv3 license. Read license-GPLv3.txt, or if not present, http://www.gnu.org/licenses/.
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If you need a closed-source version of this software for commercial purposes, please contact the authors.
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If you use this software in an academic work, please cite the most relevant publication associated by reading the individual README files of each technique (some are implementation of GDM techniques proposed by third parties) or visiting http://mapir.uma.es, or if any, please cite the authors of the software directly.