Real-Time Occlusion-Robust Deformable Linear Object Tracking With Model-Based Gaussian Mixture Model - Wang, Taohan; Yamakawa, Yuji (Frontiers in Neurorobotics, 2022)
Tracking and manipulating deformable linear objects (DLO) has great potential in the industrial world. However, estimating the object's state is crucial and challenging, especially when dealing with heavy occlusion situations and different objects' physical properties. To address these problems, we introduce a novel tracking algorithm to observe and estimate the states of DLO. The proposed tracking algorithm is based on the Coherent Point Drift (CPD), which registers the observed point cloud, and the Finite Element Method (FEM) model encodes physical properties. The Gaussian mixture model with CPD regularization generates constraints to deform a given FEM model into desired shapes. The FEM model encodes the local structure, the global topology, and the material property to better approximate the deformation process in the real world without using simulation software. A series of simulations and real data tracking experiments have been conducted on deformable objects, such as rope and iron wire, to demonstrate our method's robustness and accuracy in the presence of occlusion.