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A Least Squares approach for learning gas distribution maps from a set of integral gas concentration measurements obtained with a TDLAS sensor
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS MRO Lab)ORCID iD: 0000-0003-0195-2102
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS MRO Lab)ORCID iD: 0000-0001-5061-5474
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS MRO Lab)ORCID iD: 0000-0003-0217-9326
2012 (English)In: Proceedings of the IEEE Sensors Conference, 2012, IEEE Sensors Council, 2012, 550-553 p.Conference paper, Published paper (Refereed)
Abstract [en]

Applications related to industrial plant surveillance and environmental monitoring often require the creation of gas distribution maps (GDM). In this paper an approach for creating a gas distribution map using a Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor and a laser range scanner mounted on a pan tilt unit is presented. The TDLAS sensor can remotely sense the target gas, in this case methane, requiring novel GDM algorithms compared to the ones developed for traditional in-situ chemical sensors. The presented setup makes it possible to create a 3D model of the environment and to calculate the path travelled by the TDLAS beam. The knowledge of the beam path is of crucial importance since a TDLAS sensor provides an integral measurement of the gas concentration over that path. An efficient GDM algorithm based on a quadratic programming formulation is proposed. The approach is tested in an indoor scenario where transparent bottles filled with methane are successfully localized.

Place, publisher, year, edition, pages
IEEE Sensors Council, 2012. 550-553 p.
Series
IEEE Sensors, ISSN 1930-0395
Keyword [en]
Absorption, Gas lasers, Laser beams, Measurement by laser beam, Noise, Noise measurement, Robot sensing systems
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-28809DOI: 10.1109/ICSENS.2012.6411118ISI: 000315671100136Scopus ID: 2-s2.0-84873973113ISBN: 978-1-4577-1766-6 (print)OAI: oai:DiVA.org:oru-28809DiVA: diva2:617890
Conference
11th IEEE Sensors Conference, Taipei, Taiwan, October 28-31, 2012
Projects
GASBOT
Available from: 2013-04-24 Created: 2013-04-24 Last updated: 2017-02-17Bibliographically approved

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Trincavelli, MarcoHernandez Bennetts, VictorLilienthal, Achim J.
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CiteExportLink to record
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Citation style
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