• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.16-22
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• 2019

Off-line programming and robot calibration through simulation are essential when setting up a robot in a robot automation production line. In this study, we developed a new robot calibration method to match the CAD data of the production line with the measurement data on the site using 3D scanner. The proposed method calibrates the robot using 3D point cloud data through Iterative Closest Point algorithm. Registration is performed in three steps. First, vertices connected by three planes are extracted from CAD data as feature points for registration. Three planes are reconstructed from the scan point data located around the extracted feature points to generate corresponding feature points. Finally, the transformation matrix is calculated by minimizing the distance between the feature points extracted through the ICP algorithm. As a result of applying the software to the automobile welding robot installation, the proposed method can calibrate the required accuracy to within 1.5mm and effectively shorten the set-up time, which took 5 hours per robot unit, to within 40 minutes. By using the developed system, it is possible to shorten the OLP working time of the car body assembly line, shorten the precision teaching time of the robot, improve the quality of the produced product and minimize the defect rate.

• Journal of KIISE
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• v.44 no.4
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• pp.392-398
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• 2017

The point-cloud representation of an object is performed by scanning a space through a laser scanner that is extracting a set of points, and the points are then integrated into the same coordinate system through a registration. The set of the completed registration-integrated point clouds is classified into meaningful regions, shapes, and noises through a mathematical analysis. In this paper, the aim is the matching of a curved area like a cylinder shape in 3D point-cloud data. The matching procedure is the attainment of the center and radius data through the extraction of the cylinder-shape candidates from the sphere that is fitted through the RANdom Sample Consensus (RANSAC) in the point cloud, and completion requires the matching of the curved region with the Catmull-Rom spline from the extracted center-point data using the Principal Component Analysis (PCA). Not only is the proposed method expected to derive a fast estimation result via linear and curved cylinder estimations after a center-axis estimation without constraint and segmentation, but it should also increase the work efficiency of reverse engineering.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.10
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• pp.1961-1970
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• 2002

A laser scanner is widely used fur a device fur acquiring point data in reverse engineering. It is more efficient to generate a surface automatically from the line-typed data than scattered data of points clouds. In the case of a compound model, it is hard to represent all the scanned data into one surface maintaining its original line characteristics. In this paper, a method is presented to generate a surface by the segmentation of measured point data. After forming triangular net, the segmentation is done by the user input such as the angle between triangles, the number of facets to be considered as small segment, and the angle for combining small segment. B-spline fitting is implemented to the point data in each segment. The surface generation through segmentation shows a reliable result when it is applied to the models with curvature deviation regions. An useful algorithm for surface reconstruction is developed and verified by applying an practical model and shows a good tools fur reverse engineering in design modification.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.11-17
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• 2013

Nowadays 3D models are used in diverse sectors. The 3D maps provide better reality than existing plane maps as well as diverse pieces of information that cannot be expected from the limited plane maps. A process proposed in this paper enables easy and quick production by replacing the expensive laser scanners for modeling by an improved digital camera stereo matching algorithm. The algorithm used in this study was a SURF algorithm contained in the OpenCV library. The unconformity points of the algorithm were eliminated using the homography conversion and epipolar lines. In addition, the improved algorithm was compared with the commercial program, and it showed a better performance than the commercial program. It is expected that the proposed method can contribute to the digital maps and 3D virtual reality because it enables easy and quick 3D modeling provided that the stereo matching conditions are met.