Advertisement

Item Recognition, Learning, and Manipulation in a Warehouse Input Station

  • Maja Rudinac
  • Berk Calli
  • Pieter Jonker
Chapter

Abstract

One of the challenges of future retail warehouses is automating the order-picking process. To achieve this, items in an order tote must be automatically detected and grasped under various conditions. An inexpensive and flexible solution, presented in this chapter, is using vision systems to locate and identify items to be automatically grasped by a robot system in a bin-picking workstation. Such a vision system requires a single camera to be placed above an order tote, and software to perform the detection, recognition, and manipulation of products using robust image processing and pattern recognition techniques. In order to efficiently and robustly grasp a product by such a robot, both visual and grasping models of each item should be learnt off-line in a product input station. In current warehouse practice, all different types of products entering the warehouse are first measured manually in an input station and stored in the database of the warehouse management system. In this chapter, a method to automate this product input process is proposed: a system for automatic learning, measuring, and storing visual and grasping characteristics of the products is presented.

Keywords

Input Station Scale Invariant Feature Transform Salient Object Saliency Detection Visual Servoing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Calli B, Wisse M, Jonker P (2011) Grasping of unknown objects via curvature maximization using active vision, IEEE/RSJ international conference on intelligent robots and systemsGoogle Scholar
  2. 2.
    Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. In: Computer vision and pattern recognition, 2007 CVPR ’07 IEEE conference, pp 1–8Google Scholar
  3. 3.
    Kuhl FP, Giardina CR (1982) Elliptic Fourier features of a closed contour. Comput Graph Image Process 18:236–258CrossRefGoogle Scholar
  4. 4.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comp Vis 60:91–110CrossRefGoogle Scholar
  5. 5.
    Matas J, Chum O, Urban M, Pajdla T (2004) Robust wide-baseline stereo from maximally stable extremal regions. Image Vis Comput 22:761–767CrossRefGoogle Scholar
  6. 6.
    Nene SA, Nayar SK, Murase H (1996) Columbia object image library (COIL-100). Technical Report CUCS-006-96. Department of Computer Science, Columbia University, New YorkGoogle Scholar
  7. 7.
    Nof SY (1999) Handbook of industrial robotics. 2nd edn. Wiley, New YorkCrossRefGoogle Scholar
  8. 8.
    Rudinac M, Jonker PP (2010) Saliency detection and object localization in indoor environments. In: Pattern recognition (ICPR), 2010 20th International Conference, pp 404–407Google Scholar
  9. 9.
    Rudinac M, Jonker PP (2010) A fast and robust descriptor for multiple-view object recognition. In: Control automation robotics & vision (ICARCV), 2010 11th International Conference, pp 2166–2171Google Scholar
  10. 10.
    Rudinac M, Lenseigne B, Jonker P (2009) Keypoints extraction and selection for recognition. In: Proceedings of the eleventh IAPR conference on machine vision applicationsGoogle Scholar
  11. 11.
    Saxena A, Driemeyer J, Ng AY (2008) Robotic grasping of novel objects using vision. Int J Rob Res 27:157–173CrossRefGoogle Scholar
  12. 12.
    Srinivasa S, Ferguson D, Vandeweghe JM, Diankov R, Berenson D, Helfrich C, Strasdat K (2008) The robotic busboy: steps towards developing a mobile robotic home assistant. In: Proceedings of the 10th international conference on intelligent autonomous systemsGoogle Scholar
  13. 13.
    Yip RKK, Tam PKS, Leung DNK (1994) Application of elliptic Fourier descriptors to symmetry detection under parallel projection. IEEE Trans Pattern Anal Mach Intel 16:277–286MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited  2012

Authors and Affiliations

  1. 1.Faculty of Mechanical, Maritime and Material EngineeringDelft University of TechnologyDelftThe Netherlands

Personalised recommendations