Learning to Estimate 3-D States of Deformable Linear Objects from Single-Frame Occluded Point Clouds
Kangchen Lv,Mingrui Yu,Yifan Pu,Xin Jiang,Gao Huang,Xiang Li,Kangchen Lv,Mingrui Yu,Yifan Pu,Xin Jiang,Gao Huang,Xiang Li
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...
