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

  • Maja Rudinac
  • Berk Calli
  • Pieter Jonker


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.


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.


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Copyright information

© Springer-Verlag London Limited  2012

Authors and Affiliations

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

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