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Bayesian Spatial Event Distribution Grid Maps for Modeling the Spatial Distribution of Gas Detection Events
Örebro University, School of Science and Technology. (AASS Research Centre, Mobile Robotics and Olfaction Lab)
Örebro University, School of Science and Technology. (AASS Research Centre, Mobile Robotics and Olfaction Lab)ORCID iD: 0000-0003-0195-2102
Örebro University, School of Science and Technology. (AASS Research Centre, Mobile Robotics and Olfaction Lab)ORCID iD: 0000-0003-0217-9326
2014 (English)In: Sensor Letters, ISSN 1546-198X, E-ISSN 1546-1971, Vol. 12, no 6-7, 1142-1146 p.Article in journal (Refereed) Published
Abstract [en]

In this paper we introduce a novel gas distribution mapping algorithm, Bayesian Spatial Event Distribution (BASED), that, instead of modeling the spatial distribution of a quasi-continuous gas concentration, models the spatial distribution of gas events, for example detection and non-detection of a target gas. The proposed algorithm is based on the Bayesian Inference framework and models the likelihood of events at a certain location with a Bernoulli distribution. In order to avoid overfitting, a Bayesian approach is used with a beta distribution prior for the parameter μ that governs the Bernoulli distribution. In this way, the posterior distribution maintains the same form of the prior, i.e., will be a beta distribution as well, enabling a simple approach for sequential learning. To learn a map composed of beta distributions, we discretize the inspection area into a grid and extrapolate from local measurements using Gaussian kernels. We demonstrate the proposed algorithm for MOX sensors and a photo ionization detector mounted on a mobile robot and show how qualitatively similar maps are obtained from very different gas sensors.

Place, publisher, year, edition, pages
Valencia, California, US: American Scientific Publishers, 2014. Vol. 12, no 6-7, 1142-1146 p.
Keyword [en]
BERNOULLI DISTRIBUTION; BETA DISTRIBUTION; GAS DISTRIBUTION MAPPING; STATISTICAL MODELING
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-41205DOI: 10.1166/sl.2014.3189Scopus ID: 2-s2.0-84911444121OAI: oai:DiVA.org:oru-41205DiVA: diva2:779893
Available from: 2015-01-13 Created: 2015-01-13 Last updated: 2017-10-03Bibliographically approved

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Schaffernicht, ErikTrincavelli, MarcoLilienthal, Achim J.
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