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 and curvatures with high variance. Furthermore, our method achieves a significant performance improvement of approximately 40 FPS compared to the 15 FPS of prior algorithms, which enables realtime applications.

翻译：摘要：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。这对于可变形线性目标（DLOs）或“杆状物”尤其如此，其细长且柔性的结构使得有效的跟踪与检测变得困难。为应对这一挑战，我们提出mBEST，一种鲁棒的实时DLO检测算法，能够生成每个DLO中心线的有序像素序列及分割掩码。算法首先获取DLO的二值掩码，然后将其细化以生成骨架像素表示。在对骨架进行细化以确保拓扑正确性后，通过遍历像素生成沿每个独立DLO的路径。本算法的核心假设是：通过选择使DLO总弯曲能量最小化的路径组合，可以鲁棒地处理交叉点。我们证明，这一简单直观的公式在检测具有大量随机交叉点和高曲率变异性的DLO时，性能优于现有方法。此外，我们的方法实现了约40 FPS的性能提升，显著优于先前算法的15 FPS，从而支持实时应用。