用软体机器人表面纹理做视觉标记，实现无需训练的实时形状追踪。
AFT: Appearance-Based Feature Tracking for Markerless and Training-Free Shape Reconstruction of Soft Robots
- 利用机器人自身表面纹理作为隐式标记，分层匹配实现局部对齐与全局优化解耦。
- 实测平均末端误差仅2.6%，在多种视角和遮挡下仍保持稳定追踪。
- 无需额外标记或训练数据，适合低成本、动态环境中的实际部署。
精确的形状重建对软体机器人的精准控制与可靠运行至关重要。相比依赖传感器的方法，基于视觉的方案在成本、简便性和部署便捷性上更具优势。然而，现有视觉方法常依赖复杂的相机设置、特定背景或大规模训练数据，限制了其在真实场景中的实用性。本文提出一种基于视觉、无标记且无需训练的软体机器人形状重建框架，直接利用机器人自然表面外观作为特征。这些表面特征充当隐式视觉标记，支持分层匹配策略，将局部区域对齐与全局运动学优化解耦。仅需初始3D重建和运动学对齐，该方法即可在多种环境中实现实时形状追踪，并对遮挡和相机视角变化具有鲁棒性。在连续型软体机器人上的实验验证表明，实时运行时平均末端误差为2.6%，且在闭环控制任务中表现稳定。结果表明该方法具备在动态真实场景中可靠、低成本部署的潜力。
原文摘要 · Abstract (English)
Accurate shape reconstruction is essential for precise control and reliable operation of soft robots. Compared to sensor-based approaches, vision-based methods offer advantages in cost, simplicity, and ease of deployment. However, existing vision-based methods often rely on complex camera setups, specific backgrounds, or large-scale training datasets, limiting their practicality in real-world scenarios. In this work, we propose a vision-based, markerless, and training-free framework for soft robot shape reconstruction that directly leverages the robot's natural surface appearance. These surface features act as implicit visual markers, enabling a hierarchical matching strategy that decouples local partition alignment from global kinematic optimization. Requiring only an initial 3D reconstruction and kinematic alignment, our method achieves real-time shape tracking across diverse environments while maintaining robustness to occlusions and variations in camera viewpoints. Experimental validation on a continuum soft robot demonstrates an average tip error of 2.6% during real-time operation, as well as stable performance in practical closed-loop control tasks. These results highlight the potential of the proposed approach for reliable, low-cost deployment in dynamic real-world settings.
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