论文标题
部分可观测时空混沌系统的无模型预测
Learning to Estimate 3-D States of Deformable Linear Objects from Single-Frame Occluded Point Clouds
论文作者
论文摘要
准确，鲁棒地估算可变形线性对象（DLOS）的状态，例如绳索和电线，对于DLO操纵和其他应用至关重要。但是，由于状态空间的高维度，频繁的遮挡和噪音，这仍然是一个具有挑战性的公开问题。本文着重于学习使用数据驱动方法在遮挡的情况下从单帧点云中稳健估计DLO的状态。我们提出了一种新颖的两分支网络体系结构，以分别利用输入点云的全局和本地信息，并设计一个融合模块，以有效利用这两种方法的优势。模拟和现实世界实验结果表明，即使使用严重遮挡的点云，我们的方法也可以生成全球平滑且局部精确的DLO状态估计结果，这可以直接应用于3-D空间中DLOS的现实世界机器人操纵。
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.
