Real-Time Occlusion-Robust Deformable Linear Object Tracking With Model-Based Gaussian Mixture Model.

ABSTRACT: Tracking and manipulating deformable linear objects (DLOs) 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 physical properties of different objects. 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 approx

SUBMITTER: Wang T 

PROVIDER: S-EPMC9136076 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature