Metadata-Version: 2.1
Name: td_kernel_dmvw
Version: 0.1.0
Summary: This project provides an algorithm for calculating gas distribution maps.
Home-page: https://gitlab.com/smueller18/TDKernelDMVW
Author: Stephan Müller
License: UNKNOWN
Project-URL: Documentation, https://smueller18.gitlab.com/TDKernelDMVW/
Project-URL: Source, https://gitlab.com/smueller18/TDKernelDMVW
Project-URL: Tracker, https://gitlab.com/smueller18/TDKernelDMVW/issues
Description: # TD Kernel DM+V/W
        
        [![pipeline status](https://gitlab.com/smueller18/TDKernelDMVW/badges/master/pipeline.svg)](https://gitlab.com/smueller18/TDKernelDMVW/commits/master)
        [![coverage](https://gitlab.com/smueller18/TDKernelDMVW/badges/master/coverage.svg)](https://smueller18.gitlab.io/TDKernelDMVW/coverage/)
        [![pylint](https://smueller18.gitlab.io/TDKernelDMVW/badges/pylint.svg)](https://smueller18.gitlab.io/TDKernelDMVW/lint/)
        
        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.
        
        ```bash
        pipenv install --dev
        pipenv run python simple_example.py
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
