単一フレームの遮蔽点群から変形可能な線状物体の3次元状態を推定する学習
Learning to Estimate 3-D States of Deformable Linear Objects from Single-Frame Occluded Point Clouds
ロープやワイヤーなどの変形可能な線状物体(DLO)の3次元状態を、遮蔽のある単一フレームの点群から推定する二分岐ネットワークを提案し、実世界のロボットマニピュレーションに応用可能であることを示した。
著者: Kangchen Lv, Mingrui Yu, Yifan Pu, Xin Jiang, Gao Huang, Xiang Li
分類: cs.RO
原文アブストラクト
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.
