Advertisement

Unknown Object Detection by Punching: An Impacting-Based Approach to Picking Novel Objects

  • Yusuke MaedaEmail author
  • Hideki Tsuruga
  • Hiroyuki Honda
  • Shota Hirono
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 867)

Abstract

In this paper, a method for unknown object detection based on impacting and keypoint tracking is presented. In this method, a robot perturbs object positions by punching the floor on which the objects are placed, to detect each of the objects individually from camera images before and after the punching. The detection method utilizes consistent movements of the keypoints of each object according to its rigid-body motion. After the detection, a grasp of each of the detected objects is planned based on extracting its two parallel edges. The proposed method is successfully applied to picking up of mahjong tiles by an industrial manipulator.

Keywords

Interactive perception Segmentation Picking 

References

  1. 1.
  2. 2.
    Arun, K.S., Huang, T.S., Blostein, S.D.: Least-squares fitting of two 3-D point sets. IEEE Trans. Pattern Anal. Mach. Intell. 9(5), 698–700 (1987)CrossRefGoogle Scholar
  3. 3.
    Bohg, J., Hausman, K., Sankaran, B., Brock, O., Kragic, D., Schaal, S., Sukhatme, G.S.: Interactive perception: leveraging action in perception and perception in action. IEEE Trans. Robotic. 33(6), 1273–1291 (2017)CrossRefGoogle Scholar
  4. 4.
    Chang, L., Smith, J.R., Fox, D.: Interactive singulation of objects from a pile. In: Proceedings of 2012 IEEE International Conference on Robotics and Automation, pp. 3875–3882 (2012)Google Scholar
  5. 5.
    Edelsbrunner, H., Kirkpatrick, D., Seidel, R.: On the shape of a set of points in the plane. IEEE Trans. Inf. Theor. 29(4), 551–559 (1983)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Gupta, M., Müller, J., Sukhatme, G.S.: Using manipulation primitives for object sorting in cluttered environments. IEEE Trans. Autom. Sci. Eng. 12(2), 608–614 (2015)CrossRefGoogle Scholar
  7. 7.
    Harada, K., Tsuji, T., Nagata, K., Yamanobe, N., Maruyama, K., Nakamura, A., Kawai, Y.: Grasp planning for parallel grippers with flexibility on its grasping surface. In: Proceedings of IEEE International Conference on Robotics and Biomimetics, pp. 1540–1546 (2011)Google Scholar
  8. 8.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of Fourth Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  9. 9.
    Hermans, T., Rehg, J.M., Bobick, A.: Guided pushing for object singulation. In: Proceedings of 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4783–4790 (2012)Google Scholar
  10. 10.
    Katz, D., Venkatraman, A., Kazemi, M., Bagnell, J.A., Stentz, A.: Perceiving, learning, and exploiting object affordances for autonomous pile manipulation. Auton. Robots 37(4), 369–382 (2014)CrossRefGoogle Scholar
  11. 11.
    Lowe, D.G.: Object recognition from local scale invariant features. In: Proceedings of 1999 IEEE International Conference on Computer Vision, pp. 1150–1157 (1999)Google Scholar
  12. 12.
    Metta, G., Fitzpatrick, P.: Better vision through manipulation. Adapt. Behav. 11(2), 109–128 (2003)CrossRefGoogle Scholar
  13. 13.
    Schiebener, D., Ude, A., Asfour, T.: Physical interaction for segmentation of unknown textured and non-textured rigid objects. In: Proceedings of 2014 IEEE International Conference on Robotics and Automation, pp. 4959–4966 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yusuke Maeda
    • 1
    Email author
  • Hideki Tsuruga
    • 2
  • Hiroyuki Honda
    • 2
  • Shota Hirono
    • 2
  1. 1.Faculty of EngineeringYokohama National UniversityYokohamaJapan
  2. 2.Graduate School of EngineeringYokohama National UniversityYokohamaJapan

Personalised recommendations