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：摘要：可变形材料的机器人操控是一项具有挑战性的任务，通常需要实时视觉反馈。对于可变形线性目标（DLOs）或“杆件”而言尤为如此，其纤细且灵活的结构使得精确跟踪与检测变得困难。为应对这一挑战，我们提出mBEST算法，这是一种鲁棒的实时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。该算法首先获取DLO的二值掩码，然后对其进行细化以生成骨架像素表示。在优化骨架确保拓扑正确性后，遍历像素以生成沿每个独立DLO的路径。算法的核心在于：我们假设通过选择最小化DLO累积弯曲能量的路径组合，能够鲁棒地处理交叉点。实验表明，这一简洁直观的方案在处理具有大量随机交叉（从高曲率变化到近乎平行构型）的DLO检测时，性能优于现有最先进方法。此外，本方法在运行速度上实现了约50%的显著提升，且扩展性优于现有技术。