Vision-Based State and Pose Estimation for Robotic Bin Picking of Cables

Abstract

This paper deals with the challenging task of picking semi-deformable linear objects (SDLOs) from a bin. SDLOs are deformable elements, such as cables, joined to a rigid part as a connector. We propose a vision-based strategy to detect, classify and estimate the pose and the state (free or occluded) of connectors belonging to an unspecified number of SDLOs, arranged in an unknown configuration in the bin. The connectors can then be grasped and manipulated by a dual-arm robot through a set of manipulation primitives. In this way, a single SDLO can be extracted from the bin and laid on the worktable. A subsequent association between the connectors and the extracted SDLOs is performed, allowing to firmly grasp a SDLO at its ends to further manipulate it. The procedure is tested in bin picking operations with several kinds of SDLOs and is applied to a use case involving a collaborative wire harnesses assembly task.