论文标题
在执行几何约束时跟踪部分封闭的变形对象
Tracking Partially-Occluded Deformable Objects while Enforcing Geometric Constraints
论文作者
论文摘要
为了在非结构化的环境中操纵可变形的物体，例如绳索或布，机器人需要一种估计其当前形状的方法。但是，由于对象的高灵活性，（自我）阻塞以及与障碍物的相互作用，跟踪可变形物体的形状可能具有挑战性。对于新颖的环境，建立高保真物理模拟以帮助跟踪很难进行跟踪。取而代之的是，我们专注于基于RGBD图像和几何运动估计和障碍物跟踪对象。我们对以前工作的主要贡献是：1）通过使用运动模型正规化跟踪估计值来处理严重阻塞的更好方法； 2）\ textit {convex}几何约束的配方，这使我们能够通过后处理步骤来防止自我解干和渗透到已知的障碍中。这些贡献使我们在严重阻塞和障碍的情况下的准确性方面可以超越先前的方法。
In order to manipulate a deformable object, such as rope or cloth, in unstructured environments, robots need a way to estimate its current shape. However, tracking the shape of a deformable object can be challenging because of the object's high flexibility, (self-)occlusion, and interaction with obstacles. Building a high-fidelity physics simulation to aid in tracking is difficult for novel environments. Instead we focus on tracking the object based on RGBD images and geometric motion estimates and obstacles. Our key contributions over previous work in this vein are: 1) A better way to handle severe occlusion by using a motion model to regularize the tracking estimate; and 2) The formulation of \textit{convex} geometric constraints, which allow us to prevent self-intersection and penetration into known obstacles via a post-processing step. These contributions allow us to outperform previous methods by a large margin in terms of accuracy in scenarios with severe occlusion and obstacles.
