Object recognition and localization for robust grasping with a dexterous gripper in the context of container unloading

Narunas Vaskevicius; Christian A. Mueller; Manuel Bonilla; Vinicio Tincani; Todor Stoyanov; Gualtiero Fantoni; Kaustubh Pathak; Achim Lilienthal; Antonio Bicchi; Andreas Birk

doi:10.1109/CoASE.2014.6899490

Object recognition and localization for robust grasping with a dexterous gripper in the context of container unloading

Narunas Vaskevicius
, Christian A. Mueller
, Manuel Bonilla
, Vinicio Tincani
, Todor Stoyanov
, Gualtiero Fantoni
, Kaustubh Pathak
, Achim Lilienthal
, Antonio Bicchi
, Andreas Birk

Jacobs University Bremen
University of Pisa
Örebro University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

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.

Original language	English
Title of host publication	2014 IEEE International Conference on Automation Science and Engineering, CASE
Publisher	IEEE Computer Society
Pages	1270-1277
Number of pages	8
ISBN (Electronic)	9781479952830
DOIs	https://doi.org/10.1109/CoASE.2014.6899490
State	Published - 15 Sep 2014
Externally published	Yes
Event	2014 IEEE International Conference on Automation Science and Engineering, CASE 2014 - Taipei, Taiwan, Province of China Duration: 18 Aug 2014 → 22 Aug 2014

Publication series

Name	IEEE International Conference on Automation Science and Engineering
ISSN (Print)	2161-8070
ISSN (Electronic)	2161-8089

Conference

Conference	2014 IEEE International Conference on Automation Science and Engineering, CASE 2014
Country/Territory	Taiwan, Province of China
City	Taipei
Period	18/08/14 → 22/08/14

Access to Document

10.1109/CoASE.2014.6899490

Cite this

Vaskevicius, N., Mueller, C. A., Bonilla, M., Tincani, V., Stoyanov, T., Fantoni, G., Pathak, K., Lilienthal, A., Bicchi, A., & Birk, A. (2014). Object recognition and localization for robust grasping with a dexterous gripper in the context of container unloading. In 2014 IEEE International Conference on Automation Science and Engineering, CASE (pp. 1270-1277). Article 6899490 (IEEE International Conference on Automation Science and Engineering). IEEE Computer Society. https://doi.org/10.1109/CoASE.2014.6899490

Vaskevicius, Narunas ; Mueller, Christian A. ; Bonilla, Manuel et al. / Object recognition and localization for robust grasping with a dexterous gripper in the context of container unloading. 2014 IEEE International Conference on Automation Science and Engineering, CASE. IEEE Computer Society, 2014. pp. 1270-1277 (IEEE International Conference on Automation Science and Engineering).

@inproceedings{b352fb37994b40df8f8fb50f4f069b90,

title = "Object recognition and localization for robust grasping with a dexterous gripper in the context of container unloading",

abstract = "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.",

author = "Narunas Vaskevicius and Mueller, \{Christian A.\} and Manuel Bonilla and Vinicio Tincani and Todor Stoyanov and Gualtiero Fantoni and Kaustubh Pathak and Achim Lilienthal and Antonio Bicchi and Andreas Birk",

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year = "2014",

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doi = "10.1109/CoASE.2014.6899490",

language = "English",

series = "IEEE International Conference on Automation Science and Engineering",

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Vaskevicius, N, Mueller, CA, Bonilla, M, Tincani, V, Stoyanov, T, Fantoni, G, Pathak, K, Lilienthal, A, Bicchi, A & Birk, A 2014, Object recognition and localization for robust grasping with a dexterous gripper in the context of container unloading. in 2014 IEEE International Conference on Automation Science and Engineering, CASE., 6899490, IEEE International Conference on Automation Science and Engineering, IEEE Computer Society, pp. 1270-1277, 2014 IEEE International Conference on Automation Science and Engineering, CASE 2014, Taipei, Taiwan, Province of China, 18/08/14. https://doi.org/10.1109/CoASE.2014.6899490

Object recognition and localization for robust grasping with a dexterous gripper in the context of container unloading. / Vaskevicius, Narunas; Mueller, Christian A.; Bonilla, Manuel et al.
2014 IEEE International Conference on Automation Science and Engineering, CASE. IEEE Computer Society, 2014. p. 1270-1277 6899490 (IEEE International Conference on Automation Science and Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Object recognition and localization for robust grasping with a dexterous gripper in the context of container unloading

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AU - Mueller, Christian A.

AU - Bonilla, Manuel

AU - Tincani, Vinicio

AU - Stoyanov, Todor

AU - Fantoni, Gualtiero

AU - Pathak, Kaustubh

AU - Lilienthal, Achim

AU - Bicchi, Antonio

AU - Birk, Andreas

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AB - 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.

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ER -

Vaskevicius N, Mueller CA, Bonilla M, Tincani V, Stoyanov T, Fantoni G et al. Object recognition and localization for robust grasping with a dexterous gripper in the context of container unloading. In 2014 IEEE International Conference on Automation Science and Engineering, CASE. IEEE Computer Society. 2014. p. 1270-1277. 6899490. (IEEE International Conference on Automation Science and Engineering). doi: 10.1109/CoASE.2014.6899490

Object recognition and localization for robust grasping with a dexterous gripper in the context of container unloading

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