论文标题
插入整孔组件的姿势估计和PIN检查
In-Hand Pose Estimation and Pin Inspection for Insertion of Through-Hole Components
论文作者
论文摘要
插入整孔组件是一项艰巨的任务。由于孔的公差很小，插入中的较小错误将导致故障。这些故障会损坏组件，并且需要手动干预才能恢复。错误可能来自不精确的对象grasps and Bent Pins。因此，重要的是，系统必须准确地确定对象的位置并拒绝用弯曲引脚的组件。通过利用对象固有的约束，使用模板匹配的方法可以获得非常精确的姿势估计。还实施了销检查方法，并显示了成功的方法。该设置是自动执行的，具有两个新颖的贡献。对引脚进行深度学习分割，并通过模拟发现检查姿势。从检查姿势和分段引脚中，生成了姿势估计和引脚检查的模板。为了训练深度学习方法，创建了分段整个孔组件的数据集。该网络在测试集上显示了97.3％的精度。还对插入CAD模型测试了PIN分割网络，并成功地分割了引脚。在三个不同的对象上测试了完整的系统，实验表明该系统能够成功插入所有对象。通过手动纠正错误和本色销钉拒绝对象。
The insertion of through-hole components is a difficult task. As the tolerances of the holes are very small, minor errors in the insertion will result in failures. These failures can damage components and will require manual intervention for recovery. Errors can occur both from imprecise object grasps and bent pins. Therefore, it is important that a system can accurately determine the object's position and reject components with bent pins. By utilizing the constraints inherent in the object grasp a method using template matching is able to obtain very precise pose estimates. Methods for pin-checking are also implemented, compared, and a successful method is shown. The set-up is performed automatically, with two novel contributions. A deep learning segmentation of the pins is performed and the inspection pose is found by simulation. From the inspection pose and the segmented pins, the templates for pose estimation and pin check are then generated. To train the deep learning method a dataset of segmented through-hole components is created. The network shows a 97.3 % accuracy on the test set. The pin-segmentation network is also tested on the insertion CAD models and successfully segment the pins. The complete system is tested on three different objects, and experiments show that the system is able to insert all objects successfully. Both by correcting in-hand grasp errors and rejecting objects with bent pins.
