Learning to Estimate 3-D States of Deformable Linear Objects from Single-Frame Occluded Point Clouds

The paper is accepted by IEEE ICRA 2023.

[arXiv]

Video

Abstract

Accurately and robustly estimating the state of deformable linear objects (DLOs), such as ropes and wires, is crucial for DLO manipulation and other applications. However, it remains a challenging open issue due to the high dimensionality of the state space, frequent occlusions, and noises. This paper focuses on learning to robustly estimate the states of DLOs from single-frame point clouds in the presence of occlusions using a data-driven method. We propose a novel two-branch network architecture to exploit global and local information of input point cloud respectively and design a fusion module to effectively leverage the advantages of both methods. Simulation and real-world experimental results demonstrate that our method can generate globally smooth and locally precise DLO state estimation results even with heavily occluded point clouds, which can be directly applied to real-world robotic manipulation of DLOs in 3-D space.

Citation

Please cite our paper if you find it helpful :)

@INPROCEEDINGS{lv2023learning,
  author={Lv, Kangchen and Yu, Mingrui and Pu, Yifan and Jiang, Xin and Huang, Gao and Li, Xiang},
  booktitle={2023 International Conference on Robotics and Automation (ICRA)}, 
  title={Learning to Estimate 3-D States of Deformable Linear Objects from Single-Frame Occluded Point Clouds}, 
  year={2023}
}

Contact

If you have any question, feel free to contact the authors: Kangchen Lv, lkc21@mails.tsinghua.edu.cn.