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Integrating SLAM and gas distribution mapping (SLAM-GDM) for real-time gas source localization
Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis, Arau, Malaysia; School of Mechatronics Engineering, Universiti Malaysia Perlis (UniMAP), Arau, Malaysia.
Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis, Arau, Malaysia; School of Mechatronics Engineering, Universiti Malaysia Perlis (UniMAP), Arau, Malaysia.
Örebro University, School of Science and Technology. (Applied Autonomous Sensor Systems)ORCID iD: 0000-0001-5061-5474
Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis, Arau, Malaysia.
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2018 (English)In: Advanced Robotics, ISSN 0169-1864, E-ISSN 1568-5535, Vol. 32, no 17, p. 903-917Article in journal (Refereed) Published
Abstract [en]

Gas distribution mapping (GDM) learns models of the spatial distribution of gas concentrations across 2D/3D environments, among others, for the purpose of localizing gas sources. GDM requires run-time robot positioning in order to associate measurements with locations in a global coordinate frame. Most approaches assume that the robot has perfect knowledge about its position, which does not necessarily hold in realistic scenarios. We argue that the simultaneous localization and mapping (SLAM) algorithm should be used together with GDM to allow operation in an unknown environment. This paper proposes an SLAM-GDM approach that combines Hector SLAM and Kernel DM+V through a map merging technique. We argue that Hector SLAM is suitable for the SLAM-GDM approach since it does not perform loop closure or global corrections, which in turn would require to re-compute the gas distribution map. Real-time experiments were conducted in an environment with single and multiple gas sources. The results showed that the predictions of gas source location in all trials were often correct to around 0.5-1.5 m for the large indoor area being tested. The results also verified that the proposed SLAM-GDM approach and the designed system were able to achieve real-time operation.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2018. Vol. 32, no 17, p. 903-917
Keywords [en]
Gas source localization, gas distribution mapping, SLAM, mobile robot, gas sensing, metal oxide gas sensor
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:oru:diva-69553DOI: 10.1080/01691864.2018.1516568ISI: 000445798600001Scopus ID: 2-s2.0-85053600678OAI: oai:DiVA.org:oru-69553DiVA, id: diva2:1256339
Note

Funding Agency:

Universiti Malaysia Perlis  9001-00561

Available from: 2018-10-16 Created: 2018-10-16 Last updated: 2018-10-16Bibliographically approved

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Hernandez Bennetts, Victor

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