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.

翻译：机器人在洞中的连接组装因其精度要求高,仍是一项艰巨的任务。 先前的工作倾向于简化问题,限制终端效应的自由度,或限制目标与初始表面位置之间的距离,从而阻止它们被部署在现实世界的制造业中。 因此,我们展示了一个粗到法的视觉孔内嵌入法(CFVS),在3D视觉反馈的基础上实现6-DoF终端效应运动控制。 CFVS可以通过快速的配置前估计处理任意倾斜角度和大的初步调整错误。 此外,通过引入信任图,忽略与对象无关的轮廓,CFVS对噪音非常强大,可以处理超出培训数据之外的各种目标。 广泛的实验显示CFVS优于最新水平的方法,在3DoF、4DoF和6-DoF洞中分别获得100%、91%和82%的平均成功率。