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Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-7631-002x
Department of Computer Science, University of Massachusetts Lowell, MA, USA.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-9992-7791
Jet Propulsion Laboratory California Institute of Technology Pasadena, CA, 91109.
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2020 (English)In: 2020 28th Mediterranean Conference on Control and Automation (MED), IEEE, 2020, p. 802-807Conference paper, Published paper (Refereed)
Abstract [en]

This article proposes a novel unsupervised learning framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds. The implementation of the framework provides valuable information for high level mission planners to navigate an aerial platform in unknown areas or robot homing missions. The  framework utilizes spectral clustering, which is capable of uncovering hidden structures from connected data points lying on non-linear manifolds. The spectral clustering algorithm computes a spectral embedding of the original 2D point cloud by utilizing the eigen decomposition of a matrix that is derived from the pairwise similarities of these points. We validate the developed framework using multiple data-sets, collected from multiple realistic simulations, as well as from real flights in underground environments, demonstrating the performance and merits of the proposed methodology. 

Place, publisher, year, edition, pages
IEEE, 2020. p. 802-807
Series
Mediterranean Conference on Control and Automation (MED), ISSN 2325-369X, E-ISSN 2473-3504
National Category
Control Engineering Other Civil Engineering
Research subject
Control Engineering; Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-79107DOI: 10.1109/MED48518.2020.9183337ISI: 000612207700130Scopus ID: 2-s2.0-85092158501OAI: oai:DiVA.org:ltu-79107DiVA, id: diva2:1433843
Conference
2020 28th Mediterranean Conference on Control and Automation (MED), 15-18 September, 2020, Saint-Raphaël, France
Note

ISBN för värdpublikation: 978-1-7281-5742-9, 978-1-7281-5743-6

Available from: 2020-06-01 Created: 2020-06-01 Last updated: 2021-03-04Bibliographically approved

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Mansouri, Sina SharifCastano, MiguelNikolakopoulos, George

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