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Object recognition and localization for robust grasping with a dexterous gripper in the context of container unloading
Jacobs University, Bremen, Germany.
Jacobs University, Bremen, Germany.
University of Pisa, Pisa, Italy.
University of Pisa, Pisa, Italy.
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2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The work presented here is embedded in research on an industrial application scenario, namely autonomous shipping-container unloading, which has several challenging constraints: the scene is very cluttered, objects can be much larger than in common table-top scenarios; the perception must be highly robust, while being as fast as possible. These contradicting goals force a compromise between speed and accuracy. In this work, we investigate a state of the art perception system integrated with a dexterous gripper. In particular, we are interested in pose estimation errors from the recognition module and whether these errors can be handled by the abilities of the gripper.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014. p. 1270-1277
Keywords [en]
containers;control engineering computing;dexterous manipulators;goods distribution;grippers;industrial robots;logistics;object recognition;autonomous shipping-container unloading;dexterous gripper;object recognition;perception system;pose estimation errors;table-top scenarios;Educational institutions;Grasping;Grippers;Robot sensing systems;Thumb
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-39849DOI: 10.1109/CoASE.2014.6899490OAI: oai:DiVA.org:oru-39849DiVA, id: diva2:772382
Conference
2014 IEEE International Conference on Automation Science and Engineering (CASE). August 18-22, 2014. Taipei, China.
Available from: 2014-12-16 Created: 2014-12-16 Last updated: 2018-06-14Bibliographically approved

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Stoyanov, TodorLilienthal, Achim J.

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  • apa
  • ieee
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  • nn-NO
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Output format
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