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Robotic manipulation of deformable materials is a challenging task that oftenrequires realtime visual feedback. This is especially true for deformablelinear objects (DLOs) or "rods", whose slender and flexible structures makeproper tracking and detection nontrivial. To address this challenge, we presentmBEST, a robust algorithm for the realtime detection of DLOs that is capable ofproducing an ordered pixel sequence of each DLO's centerline along withsegmentation masks. Our algorithm obtains a binary mask of the DLOs and thenthins it to produce a skeleton pixel representation. After refining theskeleton to ensure topological correctness, the pixels are traversed togenerate paths along each unique DLO. At the core of our algorithm, wepostulate that intersections can be robustly handled by choosing thecombination of paths that minimizes the cumulative bending energy of theDLO(s). We show that this simple and intuitive formulation outperforms thestate-of-the-art methods for detecting DLOs with large numbers of sporadiccrossings ranging from curvatures with high variance to nearly-parallelconfigurations. Furthermore, our method achieves a significant performanceimprovement of approximately 50% faster runtime and better scaling over thestate of the art.

mBEST: Realtime Deformable Linear Object Detection Through Minimal Bending Energy Skeleton Pixel Traversals