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.

翻译：准确且鲁棒地估计可变形线性物体（如绳索和电线）的状态，对于可变形线性物体的操作及其他应用至关重要。然而，由于状态空间的高维度、频繁的遮挡以及噪声干扰，这一问题仍是一个具有挑战性的开放课题。本文聚焦于通过数据驱动方法，从存在遮挡的单帧点云中鲁棒地学习估计可变形线性物体的状态。我们提出了一种新颖的双分支网络架构，分别提取输入点云的全局与局部信息，并设计了一个融合模块以有效利用两种方法的优势。仿真与真实实验结果表明，即使面对严重遮挡的点云，我们的方法也能生成全局平滑且局部精确的可变形线性物体状态估计结果，可直接应用于三维空间中可变形线性物体的真实机器人操作。