A statistical approach to gas distribution modelling with mobile robots - The Kernel DM+V algorithm

Achim J. Lilienthal, Matteo Reggente, Marco Trinca, Jose Luis Blanco, Javier Gonzalez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

136 Scopus citations

Abstract

Gas distribution modelling constitutes an ideal application area for mobile robots, which - as intelligent mobile gas sensors - offer several advantages compared to stationary sensor networks. In this paper we propose the Kernel DM+V algorithm to learn a statistical 2-d gas distribution model from a sequence of localized gas sensor measurements. The algorithm does not make strong assumptions about the sensing locations and can thus be applied on a mobile robot that is not primarily used for gas distribution monitoring, and also in the case of stationary measurements. Kernel DM+V treats distribution modelling as a density estimation problem. In contrast to most previous approaches, it models the variance in addition to the distribution mean. Estimating the predictive variance entails a significant improvement for gas distribution modelling since it allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. Estimating the predictive variance also provides the means to learn meta parameters and to suggest new measurement locations based on the current model. We derive the Kernel DM+V algorithm and present a method for learning the hyper-parameters. Based on real world data collected with a mobile robot we demonstrate the consistency of the obtained maps and present a quantitative comparison, in terms of the data likelihood of unseen samples, with an alternative approach that estimates the predictive variance.

Original languageEnglish
Title of host publication2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
Pages570-576
Number of pages7
DOIs
StatePublished - 11 Dec 2009
Externally publishedYes
Event2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009 - St. Louis, MO, United States
Duration: 11 Oct 200915 Oct 2009

Publication series

Name2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009

Conference

Conference2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
Country/TerritoryUnited States
CitySt. Louis, MO
Period11/10/0915/10/09

Fingerprint

Dive into the research topics of 'A statistical approach to gas distribution modelling with mobile robots - The Kernel DM+V algorithm'. Together they form a unique fingerprint.

Cite this