TD Kernel DM+V/W

The algorithm implements the theoretical research of the following papers:

• S. Asadi and A. Lilienthal, "Approaches to time-dependent gas distribution modelling," 2015 European Conference on Mobile Robots (ECMR), Lincoln, 2015, pp. 1-6.
• Asadi, Sahar & Reggente, Matteo & Stachniss, Cyrill & Plagemann, Christian & Lilienthal, Achim. (2011). Statistical Gas Distribution Modelling Using Kernel Methods. Intelligent Systems for Machine Olfaction: Tools and Methodologies. 153-179.
• A. J. Lilienthal, M. Reggente, M. Trincavelli, J. L. Blanco and J. Gonzalez, "A statistical approach to gas distribution modelling with mobile robots - The Kernel DM+V algorithm," 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO, 2009, pp. 570-576.
• M. Reggente and A. J. Lilienthal, "Using local wind information for gas distribution mapping in outdoor environments with a mobile robot," 2009 IEEE Sensors, Christchurch, 2009, pp. 1715-1720.
• Neumann, Patrick. (2013). BAM-Dissertationsreihe. Bd. 109: Gas Source Localization and Gas Distribution Mapping with a Micro-Drone. Berlin : Bundesanstalt für Materialforschung und -prüfung (BAM)

Besides the root algorithm (KernelDM), it contains the proposed extensions:

• time dependency (TD)
• variance (V)
• wind dependency (W)

Thanks to Achim Lilienthal, Patrick Neumann and Victor Hernandez for providing Matlab implementations for the extensions V and W.

Requirements

• Python 3
• pipenv

Run demo

Run the following code to generate the different maps. The mean map, variance map and confidence map are being plotted.

pipenv install --dev
pipenv run python simple_example.py