Shape Estimation for Elongated Deformable Object using B-spline Chained Multiple Random Matrices Model
This work addresses shape estimation for robotics and manipulation tasks involving flexible objects, but it is incremental as it builds on existing deformable object modeling techniques.
The paper tackles shape estimation for elongated deformable objects, such as ropes and tubes, by proposing a B-spline chained multiple random matrices model and an EM algorithm with EMST-based initialization, achieving evaluation through accuracy metrics like IoU and execution time.
In this paper, a B-spline chained multiple random matrices representation is proposed to model geometric characteristics of an elongated deformable object. The hyper degrees of freedom structure of the elongated deformable object make its shape estimation challenging. Based on the likelihood function of the proposed model, an expectation-maximization (EM) method is derived to estimate the shape of the elongated deformable object. A split and merge method based on the Euclidean minimum spanning tree (EMST) is proposed to provide initialization for the EM algorithm. The proposed algorithm is evaluated for the shape estimation of the elongated deformable objects in scenarios, such as the static rope with various configurations (including configurations with intersection), the continuous manipulation of a rope and a plastic tube, and the assembly of two plastic tubes. The execution time is computed and the accuracy of the shape estimation results is evaluated based on the comparisons between the estimated width values and its ground-truth, and the intersection over union (IoU) metric.