Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Minimum Volume Bounding Box decomposition for shape approximation in robot grasping
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0003-2965-2953
2008 (English)In: 2008 IEEE International Conference on Robotics and Automation, ICRA 2008: Vols 1-9, 2008, 1628-1633 p.Conference paper, Published paper (Refereed)
Abstract [en]

Thinking about intelligent robots involves consideration of how such systems can be enabled to perceive, interpret and act in arbitrary and dynamic environments. While sensor perception and model interpretation focus on the robot's internal representation of the world rather passively, robot grasping capabilities are needed to actively execute tasks, modify scenarios and thereby reach versatile goals. These capabilities should also include the generation of stable grasps to safely handle even objects unknown to the robot. We believe that the key to this ability is not to select a good grasp depending on the identification of an object (e.g. as a cup), but on its shape (e.g. as a composition of shape primitives). In this paper, we envelop given 3D data points into primitive box shapes by a fit-and-split algorithm that is based on an efficient Minimum Volume Bounding Box implementation. Though box shapes are not able to approximate arbitrary data in a precise manner, they give efficient clues for planning grasps on arbitrary objects. We present the algorithm and experiments using the 3D grasping simulator GraspIt! [1].

Place, publisher, year, edition, pages
2008. 1628-1633 p.
Series
IEEE International Conference On Robotics And Automation, ISSN 1050-4729
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-38392DOI: 10.1109/ROBOT.2008.4543434ISI: 000258095001046Scopus ID: 2-s2.0-51649096480ISBN: 978-1-4244-1646-2 (print)OAI: oai:DiVA.org:kth-38392DiVA: diva2:437759
Conference
IEEE International Conference on Robotics and Automation Location: Pasadena, CA Date: MAY 19-23, 2008
Available from: 2011-08-30 Created: 2011-08-25 Last updated: 2012-01-23Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Authority records BETA

Kragic, Danica

Search in DiVA

By author/editor
Hübner, KaiRuthotto, SteffenKragic, Danica
By organisation
Computer Vision and Active Perception, CVAP
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 28 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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