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Cited by 91Robot Manipulation and LearningRobotics and Automated SystemsRobotics and Sensor-Based Localization

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RoboSherlock: Unstructured information processing for robot perception

Beetz, Michael, Balint-Benczedi, Ferenc, Blodow, Nico, Nyga, Daniel, Wiedemeyer, Thiemo, Marton, Zoltan-Csaba·2015·2015 IEEE International Conference on Robotics and Automation (ICRA)
DOI 10.1109/icra.2015.7139395
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Abstract

We present RoboSherlock, an open source software framework for implementing perception systems for robots performing human-scale everyday manipulation tasks. In RoboSherlock, perception and interpretation of realistic scenes is formulated as an unstructured information management (UIM) problem. The application of the UIM principle supports the implementation of perception systems that can answer task-relevant queries about objects in a scene, boost object recognition performance by combining the strengths of multiple perception algorithms, support knowledge-enabled reasoning about objects and enable automatic and knowledge-driven generation of processing pipelines. We demonstrate the potential of the proposed framework by three feasibility studies of systems for real-world scene perception that have been built on top of RoboSherlock.

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Research works printed but not linked: 22

  1. goron, 2012, Segmenting cylindrical and box-like objects in cluttered 3D scenes, Proc of the 7th German Conference on Robotics (ROBOTIK)
  2. 10.1109/ICCV.2011.6126326 · DOI
  3. 10.1109/IROS.2012.6385603 · DOI
  4. morwald, 2010, Blort-the blocks world robotic vision toolbox, Best Practice Algorithms in 3D Perception and Modeling for Mobile Manipulation Workshop - CD (in conjunction with the IEEE ICRA 2010)
  5. hu, 1962, Visual pattern recognition by moment invariants, Information Theory IRE Transactions on, 8, 179, 10.1109/TIT.1962.1057692 · DOI
  6. 10.1109/ICRA.2011.5980058 · DOI
  7. 10.1109/ICRA.2011.5980153 · DOI
  8. 10.1109/IROS.2007.4399245 · DOI
  9. 10.1177/0278364909356483 · DOI
  10. 10.1109/IROS.2010.5651280 · DOI
  11. beetz, 2015, Open-EASE - a knowledge processing service for robots and robotics/ai researchers, IEEE International Conference on Robotics and Automation (ICRA)
  12. 10.1109/ICRA.2014.6907427 · DOI
  13. 10.1109/IROS.2009.5354602 · DOI
  14. bradski, 2000, The OpenCV Library, Dr Dobb's Journal of Software Tools
  15. 10.1109/ICRA.2011.5980567 · DOI
  16. ferrucci, 2010, Building Watson: An overview of the DeepQA project, AI Magazine, 31, 59, 10.1609/aimag.v31i3.2303 · DOI
  17. romea, 2011, The MOPED framework: Object recognition and pose estimation for manipulation, International Journal of Robotics Research, 30, 1284, 10.1177/0278364911401765 · DOI
  18. 10.1109/MRA.2011.940996 · DOI
  19. 10.1080/08839510500484256 · DOI
  20. 10.1109/TAMD.2010.2090149 · DOI
  21. lysenkov, 2012, Recognition and Pose Estimation of Rigid Transparent Objects with a Kinect Sensor, Proceedings of Robotics Science and Systems
  22. 10.1109/MRA.2012.2206675 · DOI

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Metadata

Authors
Beetz, M., Balint-Benczedi, F., Blodow, N., Nyga, D., Wiedemeyer, T., & Marton, Z.-C.
Affiliations
  • University of Bremen (DE)
  • Technical University of Munich (DE)
ORCID
Venue
2015 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
IEEE
Published
2015-05
Pages
1549-1556
ISSN
1050-4729 (ISSN-L)
DOI
10.1109/icra.2015.7139395
OpenAlex
W1499297823
Cited by
91
References
0
Citations by year
2026: 3 · 2025: 7 · 2024: 5 · 2023: 3 · 2022: 5 · 2021: 10 · 2020: 13 · 2019: 8 · 2018: 14 · 2017: 10 · 2016: 7 · 2015: 6
FWCI
17.28
Type
conference paper
Language
en
Open access
closed
Locations
  • Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
  • elib (German Aerospace Center) (acceptedVersion)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
  • Physical Sciences › Engineering › Control and Systems Engineering › Robotics and Automated Systems
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
Keywords
robot perception, object recognition, scene understanding, open source software framework, multi-algorithm fusion

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CoChat. (2026). Citation check: RoboSherlock: Unstructured information processing for robot perception. https://app.cochat.ai/papers/10.1109/icra.2015.7139395
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Beetz, M., Balint-Benczedi, F., Blodow, N., Nyga, D., Wiedemeyer, T., & Marton, Z.-C. (2015). RoboSherlock: Unstructured information processing for robot perception. 2015 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra.2015.7139395