State estimation for deformable objects by point registration and dynamic simulation

Abstract

To enhance the robotic manipulation of deformable objects, a robust state estimator is proposed to track the object configuration in real time. A Gaussian mixture model (GMM) is constructed to register the object nodes towards the noisy point cloud. To deal with occlusion, the coherent point drift (CPD) regularization is applied on the mixture model, so as to maintain the topological structure from previous sequences of data and to infer the object states in occluded area. The state estimation is further refined by running a dynamic simulation in parallel, which guarantees the estimates to satisfy the object's physical constraints. A series of rope tracking experiments are performed to evaluate the proposed state estimator. It is shown that the object can be tracked robustly with sensor noise, outliers and massive occlusion.