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IPPE-PCR: a novel 6D pose estimation method based on point cloud repair for texture-less and occluded industrial parts

Author

Listed:
  • Wei Qin

    (Shanghai Jiao Tong University)

  • Qing Hu

    (Shanghai Jiao Tong University)

  • Zilong Zhuang

    (Shanghai Jiao Tong University)

  • Haozhe Huang

    (Shanghai Jiao Tong University)

  • Xiaodan Zhu

    (China Mobile (Shanghai) ICT Co. Ltd)

  • Lin Han

    (China Mobile (Shanghai) ICT Co. Ltd)

Abstract

Fast and accurate 6D pose estimation can help a robot arm grab industrial parts efficiently. The previous 6D pose estimation algorithms mostly target common items in daily life. Few algorithms are aimed at texture-less and occluded industrial parts and there are few industrial parts datasets. A novel method called the Industrial Parts 6D Pose Estimation framework based on point cloud repair (IPPE-PCR) is proposed in this paper. A synthetic dataset of industrial parts (SD-IP) is established as the training set for IPPE-PCR and an annotated real-world, low-texture and occluded dataset of industrial parts (LTO-IP) is constructed as the test set for IPPE. To improve the estimation accuracy, a new loss function is used for the point cloud repair network and an improved ICP method is proposed to optimize template matching. The experiment result shows that IPPE-PCR performs better than the state-of-the-art algorithms on LTO-IP.

Suggested Citation

  • Wei Qin & Qing Hu & Zilong Zhuang & Haozhe Huang & Xiaodan Zhu & Lin Han, 2023. "IPPE-PCR: a novel 6D pose estimation method based on point cloud repair for texture-less and occluded industrial parts," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2797-2807, August.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:6:d:10.1007_s10845-022-01965-6
    DOI: 10.1007/s10845-022-01965-6
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    References listed on IDEAS

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    1. Ercan Oztemel & Samet Gursev, 2020. "Literature review of Industry 4.0 and related technologies," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 127-182, January.
    2. Xiang T. R. Kong & Hao Luo & George Q. Huang & Xuan Yang, 2019. "Industrial wearable system: the human-centric empowering technology in Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2853-2869, December.
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