The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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Pose Estimation and Image Matching for Tidy-up Task using a Robot Arm

로봇 팔을 활용한 정리작업을 위한 물체 자세추정 및 이미지 매칭

  • Received : 2021.06.02
  • Accepted : 2021.08.10
  • Published : 2021.11.30
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Abstract

In this study, the task of robotic tidy-up is to clean the current environment up exactly like a target image. To perform a tidy-up task using a robot, it is necessary to estimate the pose of various objects and to classify the objects. Pose estimation requires the CAD model of an object, but these models of most objects in daily life are not available. Therefore, this study proposes an algorithm that uses point cloud and PCA to estimate the pose of objects without the help of CAD models in cluttered environments. In addition, objects are usually detected using a deep learning-based object detection. However, this method has a limitation in that only the learned objects can be recognized, and it may take a long time to learn. This study proposes an image matching based on few-shot learning and Siamese network. It was shown from experiments that the proposed method can be effectively applied to the robotic tidy-up system, which showed a success rate of 85% in the tidy-up task.

Keywords

Acknowledgement

This work was supported by IITP grant funded by the Korea government (MSIT) (No. 2018-0-00622)

References

  1. J. Yin, K. G. S. Apuroop, Y. K. Tamilselvam, R. E. Mohan, B. Ramalingam, and A. V. Le, "Table Cleaning Task by Human Support Robot Using Deep Learning Technique," Sensors, vol. 20, no. 6, 2020, DOI: 10.3390/s20061698.
  2. B. Tekin, S. N. Sudipta, and P. Fua, "Real-time seamless single shot 6d object pose prediction," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, DOI: 10.1109/CVPR.2018.00038.
  3. S. Peng, Y. Liu, Q. Huang, X. Zhou, and H. Bao, "Pvnet: Pixel-wise voting network for 6dof pose estimation," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, DOI: 10.1109/CVPR.2019.00469.
  4. K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, DOI: 10.1109/CVPR.2016.90.
  5. B. M. T. Islam, M. N. K. Siddique, S. Rahman, and T. Jabid, "Image Recognition with Deep Learning," 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Bangkok, Thailand, 2018, DOI:10.1109/ICIIBMS.2018.8550021.
  6. F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. S. Torr, and T. M. Hospedales, "Learning to Compare: Relation Network for Few-Shot Learning," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, DOI: 10.1109/CVPR.2018.00131.
  7. S. Wold, K. Esbensen, and P. Geladi, "Principal component analysis," Chemometrics and Intelligent Laboratory Systems, vol. 2, no. 1-3, August, 1987, DOI: 10.1016/0169-7439(87)80084-9.
  8. G. Koch, R. Zemel, and R. Salakhutdinov, "Siamese Neural Networks for One-shot Image Recognition," International Conference on Machine Learning (ICML), 2015, [Online], http://www.cs.toronto.edu/~gkoch/files/msc-thesis.pdf.
  9. J. Papon, A. Abramov, M. Schoeler, and F. Worgotter, "Voxel Cloud Connectivity Segmentation - Supervoxels for Point Clouds," 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 2013, DOI: 10.1109/CVPR.2013.264.
  10. S. Christoph Stein, M. Schoeler, J. Papon, and F. Worgotter, "Object Partitioning Using Local Convexity," 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014, DOI: 10.1109/CVPR.2014.46.
  11. J. Snell, K. Swersky, and R. S. Zemel, "Prototypical Networks for Few-shot Learning," Neural Information Processing Systems (NIPS), 2017, [Online], https://arxiv.org/abs/1703.05175.
  12. V. Garcia and J. Bruna, "Few-Shot Learning with Graph Neural Networks," International Conference on Learning Representations (ICLR), 2018, [Online], https://arxiv.org/abs/1711.04043.
  13. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning," Thirty-first AAAI Conference on Artificial Intelligence (AAAI-17), 2017, [Online], https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/viewFile/14806/14311.
  14. U. Muhammad, W. Wang, S. P. Chattha, and S. Ali, "Pretrained VGGNet Architecture for Remote-Sensing Image Scene Classification," 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China, 2018, DOI: 10.1109/ICPR.2018.8545591.