幾何制約を課しながら部分的に遮蔽された変形物体を追跡する手法
Tracking Partially-Occluded Deformable Objects while Enforcing Geometric Constraints
RGBD画像と運動モデルを用いて、ひもや布などの変形物体が遮蔽や障害物に囲まれた環境でも形状を追跡する手法を提案。凸幾何制約により自己交差や障害物への貫入を防ぎ、従来法を大きく上回る精度を実現した。
著者: Yixuan Wang, Dale McConachie, Dmitry Berenson
分類: cs.RO, cs.CV
原文アブストラクト
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.
