Robotic manipulation of deformable materials is a challenging task that often requires realtime visual feedback. This is especially true for deformable linear objects (DLOs) or "rods", whose slender and flexible structures make proper tracking and detection nontrivial. To address this challenge, we present mBEST, a robust algorithm for the realtime detection of DLOs that is capable of producing an ordered pixel sequence of each DLO's centerline along with segmentation masks. Our algorithm obtains a binary mask of the DLOs and then thins it to produce a skeleton pixel representation. After refining the skeleton to ensure topological correctness, the pixels are traversed to generate paths along each unique DLO. At the core of our algorithm, we postulate that intersections can be robustly handled by choosing the combination of paths that minimizes the cumulative bending energy of the DLO(s). We show that this simple and intuitive formulation outperforms the state-of-the-art methods for detecting DLOs with large numbers of sporadic crossings ranging from curvatures with high variance to nearly-parallel configurations. Furthermore, our method achieves a significant performance improvement of approximately 50% faster runtime and better scaling over the state of the art.

翻译：可变形材料的机器人操作是一项具有挑战性的任务，通常需要实时视觉反馈。对于可变形线性物体（DLOs）或“杆件”而言尤其如此，其细长且柔性的结构使得精确跟踪与检测绝非易事。为应对这一挑战，我们提出了mBEST，一种鲁棒的DLO实时检测算法，能够生成每个DLO中心线的有序像素序列以及分割掩码。我们的算法首先获取DLO的二值掩码，然后对其进行细化以得到骨架像素表示。在优化骨架以确保拓扑正确性后，遍历像素以生成每条独立DLO的路径。该算法的核心在于我们提出：通过选择能最小化DLO累积弯曲能量的路径组合，可以鲁棒地处理交叉点。研究表明，这一简洁直观的公式在检测具有大量零散交叉（从高方差曲率到近乎平行构型）的DLOs时，显著优于现有最先进方法。此外，我们的方法在运行速度上实现了约50%的大幅提升，且扩展性优于现有技术。