Tracking Branched Deformable Linear Objects Using Particle Filtering on Depth Images

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.