Multiple 3D Object Recognition using RGB-D Data and Physical Consistency for Automated Warehousing Robots

Shuichi Akizuki, Manabu Hashimoto

Abstract

In this research, we propose a method to recognize multiple objects in the shelves of automated warehouses. The purpose of this research is to enhance the reliability of the Hypothesis Verification (HV) method that simultaneously recognizes layout of multiple objects. The proposed method have employed not only the RGB-D consistency between the input scene and the scene hypothesis but also the physical consistency. By considering the physical consistency of the scene hypothesis, the proposed HV method can efficiently reject false one. Experiment results for object which are used at Amazon Picking Challenge 2015 have been confirmed that the recognition success rate of the proposed method is higher than the previous HV method.

References

  1. Akizuki, S. and Hashimoto, M. (2015a). A proposal of the global reference frame for surface flatnessindependent 3d object detection. In Proc. Joint Conference of IWAIT and IFMIA.
  2. Akizuki, S. and Hashimoto, M. (2015b). Stable position and pose estimation of industrial parts using evaluation of observability of 3d vector pairs. 27(2):174-181.
  3. Aldoma, A., Tombari, F., di Stefano, L., and Vincze, M. (2012a). A global hypotheses verification method for 3d object recognition. In Computer Vision - ECCV 2012 - 12th European Conference on Computer Vision, pages 511-524.
  4. Aldoma, A., Tombari, F., Prankl, J., Richtsfeld, A., di Stefano, L., and Vincze, M. (2013). Multimodal cue integration through hypotheses verification for RGB-D object recognition and 6dof pose estimation. In IEEE International Conference on Robotics and Automation, pages 2104-2111.
  5. Aldoma, A., Tombari, F., Rusu, R. B., and Vincze, M. (2012b). OUR-CVFH - oriented, unique and repeatable clustered viewpoint feature histogram for object recognition and 6dof pose estimation. In Pattern Recognition - Joint 34th DAGM and 36th OAGM Symposium, pages 113-122.
  6. Chen, H. and Bhanu, B. (2007). 3d free-form object recognition in range images using local surface patches. 28(10):1252-1262.
  7. Drost, B., Ulrich, M., Navab, N., and Ilic, S. (2010). Model globally, match locally: Efficient and robust 3d object recognition. In The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition CVPR, pages 998-1005.
  8. Fuji, T., Kimura, N., and Ito, K. (2015). Architecture for recognizing stacked box objects for automated warehousing robot system. In Proceedings of the 17th Irish Machine Vision and Image Processing conference, pages 50-56.
  9. Gottschalk, S., Lin, M. C., and Manocha, D. (1996). Obbtree: A hierarchical structure for rapid interference detection. In SIGGRAPH, pages 171-180.
  10. Hashimoto, M., Sumi, K., and Usami, T. (1999). Recognition of multiple objects based on global image consistency. In Proceedings of the British Machine Vision Conference, pages 1-10.
  11. Rusu, R. B. and Cousins, S. (2011). 3d is here: Point cloud library (PCL). In IEEE International Conference on Robotics and Automation, ICRA. IEEE.
  12. Tombari, F., Salti, S., and di Stefano, L. (2010). Unique signatures of histograms for local surface description. In European Conference on Computer Vision ECCV, pages 356-369.
  13. Tombari, F. and Stefano, L. D. (2010). Object recognition in 3d scenes with occlusions and clutter by hough voting. In Proc. Fourth Pacific-Rim Symposium on Image and Video Technology, pages 349-355.
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Paper Citation


in Harvard Style

Akizuki S. and Hashimoto M. (2016). Multiple 3D Object Recognition using RGB-D Data and Physical Consistency for Automated Warehousing Robots . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 605-609. DOI: 10.5220/0005723806050609


in Bibtex Style

@conference{visapp16,
author={Shuichi Akizuki and Manabu Hashimoto},
title={Multiple 3D Object Recognition using RGB-D Data and Physical Consistency for Automated Warehousing Robots},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={605-609},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005723806050609},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - Multiple 3D Object Recognition using RGB-D Data and Physical Consistency for Automated Warehousing Robots
SN - 978-989-758-175-5
AU - Akizuki S.
AU - Hashimoto M.
PY - 2016
SP - 605
EP - 609
DO - 10.5220/0005723806050609