# Vision-Based Robotic Grasping of Reels for Automatic Packaging Machines
## 1 Introduction
### Related Works  
When it comes to part manipulation using a robotic arm, CV may play a major role in increasing the flexibility and productivity of the industrial process in which the robot is employed. Robust and efficient grasping tasks (often called \"bin-picking\" tasks) have been studied for many years for this purpose.  
Currently, many bin-picking systems utilize binocular vision methods that combine feature point matching and projection geometry to estimate the location of an item. Successful work in this context is presented by [PERSON] et al. [13] who propose a geometric pattern matching method to find the correspondence points in stereo images, and improve the selection of pick-up candidates. Standard geometric features such as circles and squares are common in the manufacturing field. For this reason, a lot of research has been dedicated to solving these particular problems. An example related to circular holes can be found in [14], wherein the authors improve the work by [PERSON] and [PERSON] [15] by providing a more robust method to find edges in the presence of interference from the inner wall of a hole, and by introducing epipolar constraints to fit ellipses in the two views simultaneously.  
Three-dimensional sensors, however, still have some limitations, such as the higher cost with respect to traditional 2D cameras, their high sensitivity to calibration, and the narrow field of view shared by the stereo cameras, which makes it difficult to have the same feature points always captured by both cameras, especially during the grasping process, in which there can be an occlusion due to the picked object. In [16], the authors propose an alternative by using a 2D monocular vision for part localization and grasping, leveraging a highly engineered calibration and matching algorithm. In [17], a single-shot multi-box detector is used to quickly define the region of interest in which to look for contour features and estimate the pose of the known object by perspective-n-point (this pertains to the problem of estimating the pose of a calibrated camera given a set of \(n\) 3D points in the real world and their corresponding 2D projections in an image). With reference to circular object localization, to which this article mainly relates, we mention the work from [PERSON] et al. [18], in which the authors present a continuous edge detector inspired by the Canny detector, and a fast ellipse detector by using the randomized Hough transform (RHT). A work based on more recent DL techniques presented by [PERSON] et al. [19], finally, claims to outperform state-of-the-art approaches in ellipse detection in terms of precision (the ratio between the number of true positive results and the number of all positive results (including false positive results), recall (the ratio between the number of true positive results and the number of all samples that should have been identified as positive), and F-measure (a measure of a test's accuracy calculated from the precision and the recall of the test) on industrial images.  
Despite the many successful examples in the literature, there is still a gap between the implementation in a controlled environment, such as a research laboratory, and the implementation in an actual industrial scenario. Manufacturing companies are often reluctant to invest in generic vision systems that claim to adapt to a large number of conditions and prefer to adopt a more specific solution that can guarantee a success rate close to 100% even if it is less flexible or elegant. By following this reasoning, this paper aims at bridging the gap by bringing together a set of solutions that, when combined, attain the desired level of industrial reliability. Rather than presenting a novel method to detect an ellipse through monocular vision, our goal is to show how a suitable combination of existing techniques can be put together to achieve impressive results in a real-case scenario.