To Örebro University

Branched deformable linear objects (BDLOs), such as wire harnesses, are important connecting components in manufacturing industries. However, due to deformability, a lack of distinct visual features, and complex branched structure, automating tasks involving these BDLOs remains a challenge. In this paper, we propose a particle-filter-based method to track the state of a BDLO. To circumvent the high cost of tracking the complex high-dimensional BDLO state, we instead track each branch as an individual B-spline. Our method learns a data-driven model to predict the likelihood of each particle conditioned on depth image observation. In contrast to current state-of-the-art approaches based on non-rigid registration, we do not require pre-segmenting the BDLO, thus alleviating a strong and limiting assumption. We train our approach on domain-randomized depth data from simulation and achieve zero-shot transfer to real-world BDLOs, achieving state-of-the- art tracking performance when the pre-segmentation fails.