oru.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
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
Fast and accurate scan registration through minimization of the distance between compact 3D NDT Representations
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems ( AASS ))ORCID iD: 0000-0002-6013-4874
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems ( AASS ))ORCID iD: 0000-0001-8658-2985
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems ( AASS ))ORCID iD: 0000-0003-0217-9326
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems ( AASS ))ORCID iD: 0000-0002-2953-1564
2012 (English)In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 31, no 12, p. 1377-1393Article in journal (Refereed) Published
Abstract [en]

Registration of range sensor measurements is an important task in mobile robotics and has received a lot of attention. Several iterative optimization schemes have been proposed in order to align three-dimensional (3D) point scans. With the more widespread use of high-frame-rate 3D sensors and increasingly more challenging application scenarios for mobile robots, there is a need for fast and accurate registration methods that current state-of-the-art algorithms cannot always meet. This work proposes a novel algorithm that achieves accurate point cloud registration an order of a magnitude faster than the current state of the art. The speedup is achieved through the use of a compact spatial representation: the Three-Dimensional Normal Distributions Transform (3D-NDT). In addition, a fast, global-descriptor based on the 3D-NDT is defined and used to achieve reliable initial poses for the iterative algorithm. Finally, a closed-form expression for the covariance of the proposed method is also derived. The proposed algorithms are evaluated on two standard point cloud data sets, resulting in stable performance on a par with or better than the state of the art. The implementation is available as an open-source package for the Robot Operating system (ROS).

Place, publisher, year, edition, pages
Sage Publications, 2012. Vol. 31, no 12, p. 1377-1393
Keywords [en]
point set registration; mapping; normal distributions transform
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-28854DOI: 10.1177/0278364912460895ISI: 000311643300003Scopus ID: 2-s2.0-84870446009OAI: oai:DiVA.org:oru-28854DiVA, id: diva2:618701
Note

Funding Agencies:

European Union FP7 - 270350

Kunskaps och Kompetensutveckling Stiftelsen project SAUNA 20100315

Available from: 2013-04-29 Created: 2013-04-29 Last updated: 2018-01-11Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Stoyanov, TodorMagnusson, MartinLilienthal, Achim J.Andreasson, Henrik

Search in DiVA

By author/editor
Stoyanov, TodorMagnusson, MartinLilienthal, Achim J.Andreasson, Henrik
By organisation
School of Science and Technology
In the same journal
The international journal of robotics research
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 623 hits
CiteExportLink to record
Permanent link

Direct link
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