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Semantic-assisted 3D Normal Distributions Transform for scan registration in environments with limited structure
Lincoln Centre for Autonomous SysteLincoln Centre for Autonomous Systems (LCAS), University of Lincoln, Lincoln, UK.
Örebro University, School of Science and Technology. (AASS MRO Lab, Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden)ORCID iD: 0000-0001-8658-2985
Lincoln Centre for Autonomous Systems (LCAS), University of Lincoln, Lincoln, UK.
Lincoln Centre for Autonomous Systems (LCAS), University of Lincoln, Lincoln, UK.
2017 (English)In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE Robotics and Automation Society, 2017, p. 4064-4069Conference paper, Published paper (Refereed)
Abstract [en]

Point cloud registration is a core problem of many robotic applications, including simultaneous localization and mapping. The Normal Distributions Transform (NDT) is a method that fits a number of Gaussian distributions to the data points, and then uses this transform as an approximation of the real data, registering a relatively small number of distributions as opposed to the full point cloud. This approach contributes to NDT’s registration robustness and speed but leaves room for improvement in environments of limited structure.

To address this limitation we propose a method for the introduction of semantic information extracted from the point clouds into the registration process. The paper presents a large scale experimental evaluation of the algorithm against NDT on two publicly available benchmark data sets. For the purpose of this test a measure of smoothness is used for the semantic partitioning of the point clouds. The results indicate that the proposed method improves the accuracy, robustness and speed of NDT registration, especially in unstructured environments, making NDT suitable for a wider range of applications.

Place, publisher, year, edition, pages
IEEE Robotics and Automation Society, 2017. p. 4064-4069
Series
Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems, ISSN 2153-0858, E-ISSN 2153-0866
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-61904DOI: 10.1109/IROS.2017.8206262ISI: 000426978203143Scopus ID: 2-s2.0-85041962455ISBN: 978-1-5386-2682-5 (electronic)ISBN: 978-1-5386-2683-2 (print)OAI: oai:DiVA.org:oru-61904DiVA, id: diva2:1151032
Conference
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), Vancouver, Canada, September 24-28, 2017
Projects
ILIAD
Note

Funding Agency:

European Unions  732737

Available from: 2017-10-20 Created: 2017-10-20 Last updated: 2018-09-12Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf