Jong-Kyu Oh

Development of structured light based bin picking system using primitive models

As a part of factory automation, bin picking systems perform pick-and-place tasks for randomly oriented parts from bins or boxes. Conventional bin picking systems can estimate the pose of an object only if the system has complete knowledge of the object (e.g., as a result of the geometric features of the object being provided by an image or a computer-aided design model). However, these systems require the features visible in an image to calculate the pose of an object, and they require additional setup time for an operator to register the reference model every time that the workpiece changed. In this article, we propose a structured light based bin picking system that makes use of primitive models that involve a small amount of prior knowledge. To obtain a reliable 3D range image for comparison with conventional systems, we use a structured light sensor with gray-coded patterns. With the 3D range image, the pose of the object is estimated with the use of primitive segmentation, rotational symmetric object modeling, and recognition. Through experiments that involve an industrial robot, we validated that the proposed method could be employed for a bin picking system.

Development of a stereo vision system for industrial robots

Machine vision systems become to play an important role in robot based factory automation, because they enable robots to recognize the environment around the workspace like human visual systems and make the manufacturing process flexible. So a large number of 2-dimensional (2D) vision systems have been successfully applied for industrial robots, but there are increasing needs for 3-dimensional (3D) vision systems providing accurate 3D position of work-pieces. For the 3D vision system, the stereo vision based position measurement systems have been applied in various fields. However the stereo vision systems which have been applied were specific for their particular tasks only. This paper introduces a general-purpose stereo vision system applicable to wide range of tasks with industrial robots.

Development of a structured-light sensor based bin-picking system using ICP algorithm

This paper proposes a general-purpose structured-light sensor based bin-picking system. At first, to determine the workpiece to be picked, a geometric pattern matching (GPM) method with respect to the 2D image is applied. A structured-light sensor with gray-coded patterns is employed to get the reliable 3D range image for the pick-up candidate. The pose of the object is acquired by just comparing the 3D point cloud between models in database and a range image acquired from the structured-light sensor through iterative closest point (ICP) algorithm in contrast with the conventional bin-picking systems which require complete knowledge of the object. Through experiments on an industrial workpiece, we validate that the proposed vision system accurately measures the 3D pose of complex objects.

Improved industrial part pose determination based on 3D closed-loop boundaries

Although various pose estimation methods for industrial robots have been studied, there are few methods that can be applied to real industrial environments. The reason is that previous methods cannot be used for a wide range of industrial parts, or the methods have many limitations in a real industrial factory. In this paper, we break through the weakness of the previous 3D closed-loop boundary based industrial part pose estimation method, which can be extracted in the range image provided by a 3D scanner. After extracting the 3D closed-loop boundaries, we make a group with 3D closed-loop boundary sets. Each 3D closed-loop boundary generates the three principal components (eigenvalues and eigenvectors), the centroid of the boundary and topological relationship in the set. Since this proposed method can be applied to the perfect circle 3D closed-loop boundary in industrial parts, it can be employed for more various general-purpose applications. Through experiments on industrial objects and industrial robots, we validate that the proposed method is feasible for real factory environments.

The 3D Geometric Information Acquisition Algorithm using Virtual Plane Method

This paper presents an algorithm to acquire 3D geometric information using a virtual plane method. The method to measure 3D information on the plane is easy, because it’s not concerning value on the z-axis. A plane can be made by arbitrary three points in the 3D space, so the algorithm is able to make a number of virtual planes from feature points on the target object. In this case, these geometric relations between the origin of each virtual plane and the origin of the target object coordinates should be expressed as known homogeneous matrices. To include this idea, the algorithm could induce simple matrix formula which is only concerning unknown geometric relation between the origin of target object and the origin of camera coordinates. Therefore, it’s more fast and simple than other methods. For achieving the proposed method, a regular pin-hole camera model and a perspective projection matrix which is defined by a geometric relation between each coordinate system is used. In the final part of this paper, we demonstrate the techniques for a variety of applications, including measurements in industrial parts and known patches images.