Localization and Tracking of Deformable Linear Objects with Self Organizing Maps

Handling of deformable linear objects (DLOs) is one of the most challenging tasks in robotics. This is because DLOs change their shape continuously during manipulation, which makes perception difficult. Therefore, this paper proposes a method for localization and tracking of DLOs. The proposed approach uses self organizing maps (SOMs) to register a model of the DLO to a point cloud obtained from a stereo vision system observing the scene. The registration is performed in two steps. First, a rigid registration roughly aligns the model with the acquired sensor data without allowing deformation. Second, a non-rigid registration accurately tracks the configuration of the observed DLO by allowing deformation. The model is simulated using a physics engine which is iteratively updated with each step of the non-rigid registration. The approach is evaluated in a series of experiments on a single rope, as well as a wire harness. It is shown that the approach is able to successfully track several configurations of the single rope and the wire harness, without prior knowledge of a previous configuration. The method is evaluated and compared to state-of-the-art tracking algorithms with regards to accuracy and robustness towards an uncertain initial guess.