论文标题
CFV：用于6多型对象无形钉孔组件的粗到最新视觉伺服器
CFVS: Coarse-to-Fine Visual Servoing for 6-DoF Object-Agnostic Peg-In-Hole Assembly
论文作者
论文摘要
机器人钉孔组件由于其准确性的高度需求而仍然是一项具有挑战性的任务。以前的工作倾向于通过限制最终效果的自由度，或限制目标与初始姿势位置之间的距离，从而简化了问题，从而阻止了它们在现实世界中部署。因此，我们提出了一种粗到1的视觉致毒（CFV）钉孔法，基于3D视觉反馈实现了6-DOF最终效应器运动控制。 CFV可以通过在细化前快速姿势估计来处理任意倾斜角度和较大的初始对齐误差。此外，通过引入置信度图来忽略对象无关的轮廓，CFV可以抵抗噪声，并且可以处理训练数据以外的各种目标。广泛的实验表明，CFV的表现优于最先进的方法，并分别获得100％，91％和82％的平均成功率，分别为3-DOF，4-DOF和6-DOF-PEG-IN-HOLE。
Robotic peg-in-hole assembly remains a challenging task due to its high accuracy demand. Previous work tends to simplify the problem by restricting the degree of freedom of the end-effector, or limiting the distance between the target and the initial pose position, which prevents them from being deployed in real-world manufacturing. Thus, we present a Coarse-to-Fine Visual Servoing (CFVS) peg-in-hole method, achieving 6-DoF end-effector motion control based on 3D visual feedback. CFVS can handle arbitrary tilt angles and large initial alignment errors through a fast pose estimation before refinement. Furthermore, by introducing a confidence map to ignore the irrelevant contour of objects, CFVS is robust against noise and can deal with various targets beyond training data. Extensive experiments show CFVS outperforms state-of-the-art methods and obtains 100%, 91%, and 82% average success rates in 3-DoF, 4-DoF, and 6-DoF peg-in-hole, respectively.
