Advertisement

Grasping Region Identification in Novel Objects Using Microsoft Kinect

  • Akshara Rai
  • Prem Kumar Patchaikani
  • Mridul Agarwal
  • Rohit Gupta
  • Laxmidhar Behera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7666)

Abstract

We present a novel solution to the problem of robotic grasping of unknown objects using a machine learning framework and a Microsoft Kinect sensor. Using only image features, without the aid of a 3D model of the object, we implement a learning algorithm that identifies grasping regions in 2D images, and generalizes well to objects not encountered previously. Thereafter, we demonstrate the algorithm on the RGB images taken by a Kinect sensor of real life objects. We obtain the 3D world coordinates utilizing the depth sensor of the Kinect. The robot manipulator is then used to grasp the object at the grasping point.

Keywords

Depth Image Kinect Sensor Kinect Camera Grasp Planning Grasp Point 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Saxena, A., Driemeyer, J., Kearns, J., Ng, A.Y.: Robotic Grasping of Novel Objects Using Vision. International Journal of Robotics Research (2008)Google Scholar
  2. 2.
    Milller, A.T., Knoop, S., Allen, P.K., Christensen, H.I.: Automatic grasp planning using shape primitives. In: Proceedings of the International Conference on Robotics and Automation (2003)Google Scholar
  3. 3.
    Morales, A., Sanz, P.J., del Pobil, A.P.: Vision based computation of the three finger grasps on unknown planar objects. In: Proceedings of the IEEE/RSJ International Robots and System Conference (2002a)Google Scholar
  4. 4.
    Morales, A., Sanz, P.J., del Pobil, A.P., Fagg, A.H.: An experiment in constraining vision-based finger contact selection with gripper geometry. In: Proceedings of the IEEE/RSJ Intelligent Robots and Systems Conference (2002b)Google Scholar
  5. 5.
    Glover, J., Rus, D., Roy, N.: Probabilistic Models of Object Geometry for Grasp Planning. Robotics: Science and Systems IV (2008)Google Scholar
  6. 6.
    Geidenstam, S., Huebner, K., Banksell, D., Kragic, D.: Learning of 2D Grasping Strategies from Box-Based 3D Object Approximations. Robotics: Science and Systems V (2008)Google Scholar
  7. 7.
    Nevatia, R., Babu, K.R.: Linear Feature Extraction and Description. Computer Graphics and Image Processing 13, 257–269 (1980)CrossRefGoogle Scholar
  8. 8.
    Laws, K.I.: Textured image segmentation. Ph.D. Thesis, University of Southern California (1980)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Akshara Rai
    • 1
  • Prem Kumar Patchaikani
    • 1
  • Mridul Agarwal
    • 1
  • Rohit Gupta
    • 1
  • Laxmidhar Behera
    • 1
  1. 1.Department of Electrical EngineeringIndian Institute of Technology KanpurIndia

Personalised recommendations