MovingCables: Moving Cable Segmentation Method and Dataset

Abstract

Manipulating cluttered cables, hoses or ropes is challenging for both robots and humans. Humans often simplify these perceptually challenging tasks by pulling or pushing tangled cables and observing the resulting motions. We propose to use a similar interactive perception principle to aid robotic cable manipulation. A fundamental building block of such an endeavor is a cable motion segmentation method that densely labels moving cable image pixels. This letter presents MovingCables, a moving cable dataset, which we hope will motivate the development and evaluation of cable motion segmentation algorithms. The dataset consists of real-world image sequences automatically annotated with ground truth segmentation masks and optical flow. In addition, we propose a cable motion segmentation method and evaluate its performance on the new dataset.