• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.33-39
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• 2020

This paper presents an Around View Monitoring (AVM) stop-line detection based longitudinal position correction algorithm for automated driving on urban roads. Poor positioning accuracy of low-cost GPS has many problems for precise path tracking. Therefore, this study aims to improve the longitudinal positioning accuracy of low-cost GPS. The algorithm has three main processes. The first process is a stop-line detection. In this process, the stop-line is detected using Hough Transform from the AVM camera. The second process is a map matching. In the map matching process, to find the corrected vehicle position, the detected line is matched to the stop-line of the HD map using the Iterative Closest Point (ICP) method. Third, longitudinal position of low-cost GPS is updated using a corrected vehicle position with Kalman Filter. The proposed algorithm is implemented in the Robot Operating System (ROS) environment and verified on the actual urban road driving data. Compared to low-cost GPS only, Test results show the longitudinal localization performance was improved.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1123-1124
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• 2008

In this paper, we develop a program that recognition of the object 3D pose using stereo camera. In order to detect the object, this paper is applied to canny edge detection algorithm and also used stereo camera to get the 3D point about the object and applied to recognize the pose of the object using iterative closest point(ICP) algorithm.

• 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 the Computational Structural Engineering Institute of Korea
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• v.29 no.2
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• pp.123-130
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• 2016

With the development of fast construction mode in shipbuilding market, the demand on accuracy management of hull is becoming higher and higher in shipbuilding industry. In order to enhance production efficiency and reduce manufacturing cycle time in shipbuilding industry, it is important for shipyards to have the accuracy of ship components evaluated efficiently during the whole manufacturing cycle time. In accurate shipbuilding process, block accuracy is the key part, which has significant meaning in shortening the period of shipbuilding process, decreasing cost and improving the quality of ship. The key of block accuracy control is to create a integrate block accuracy controlling system, which makes great sense in implementing comprehensive accuracy controlling, increasing block accuracy, standardization of proceeding of accuracy controlling, realizing "zero-defect transferring" and advancing non-allowance shipbuilding. Generally, managers of accuracy control measure the vital points at section surface of block by using the heavy total station, which is inconvenient and time-consuming for measurement of vital points. In this paper, a new measurement method based on point clouds technique has been proposed. This method is to measure the 3D coordinates values of vital points at section surface of block by using 3D scanner, and then compare the measured point with design point based on ICP algorithm which has an allowable error check process that makes sure that whether or not the error between design point and measured point is within the margin of error.

• Journal of the Korea Computer Graphics Society
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• v.24 no.5
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• pp.11-19
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• 2018

In this paper, we propose an algorithm for improving the accuracy and rate of convergence when two point clouds with noise and a low overlapping area are registered to each other. We make the most use of the geometric information of the underlying geometry of the point clouds with noise for better accuracy. We select a reasonable region from the noisy point cloud for registration and combine a modified acceleration algorithm to improve its speed. The conventional accuracy improvement method was not possible in a lot of noise, this paper resolves the problem by selecting the reasonable region for the registration. And this paper applies acceleration algorithm for a clone to low overlap point cloud pair. A simple algorithm is added to the conventional method, which leads to 3 or 4 times faster speed. In conclusion, this algorithm was developed to improve both the speed and accuracy of point cloud registration in noisy and low overlap case.

• Journal of Computational Design and Engineering
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• v.1 no.3
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• pp.202-212
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• 2014

To survive in the current shipbuilding industry, it is of vital importance for shipyards to have the ship components' accuracy evaluated efficiently during most of the manufacturing steps. Evaluating components' accuracy by comparing each component's point cloud data scanned by laser scanners and the ship's design data formatted in CAD cannot be processed efficiently when (1) extract components from point cloud data include irregular obstacles endogenously, or when (2) registration of the two data sets have no clear direction setting. This paper presents reformative point cloud data processing methods to solve these problems. K-d tree construction of the point cloud data fastens a neighbor searching of each point. Region growing method performed on the neighbor points of the seed point extracts the continuous part of the component, while curved surface fitting and B-spline curved line fitting at the edge of the continuous part recognize the neighbor domains of the same component divided by obstacles' shadows. The ICP (Iterative Closest Point) algorithm conducts a registration of the two sets of data after the proper registration's direction is decided by principal component analysis. By experiments conducted at the shipyard, 200 curved shell plates are extracted from the scanned point cloud data, and registrations are conducted between them and the designed CAD data using the proposed methods for an accuracy evaluation. Results show that the methods proposed in this paper support the accuracy evaluation targeted point cloud data processing efficiently in practice.

• Journal of Cadastre & Land InformatiX
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• v.51 no.1
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• pp.67-77
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• 2021

Spatial data is becoming very important as a medium that connects various data produced in smart cities, digital twins, autonomous driving, smart construction, and other applications. In addition, the rapid construction and update of spatial information is becoming a hot topic to satisfy the diverse needs of consumers in this field. This study developed a software prototype that can match the position of an image-based 3D building model produced without Ground Control Points using existing spatial data. As a result of applying this software to the test area, the 3D building model produced based on the image and the existing spatial data show a high positional matching rate, so that it can be widely used in applications requiring the latest 3D spatial data.

• Journal of Computational Design and Engineering
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• v.1 no.4
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• pp.223-232
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• 2014

This paper describes a variant of the extended Gaussian image based registration algorithm for point clouds with surface color information. The method correlates the distributions of surface normals for rotational alignment and grid occupancy for translational alignment with hue filters applied during the construction of surface normal histograms and occupancy grids. In this method, the size of the point cloud is reduced with a hue-based down sampling that is independent of the point sample density or local geometry. Experimental results show that use of the hue filters increases the registration speed and improves the registration accuracy. Coarse rigid transformations determined in this step enable fine alignment with dense, unfiltered point clouds or using Iterative Common Point (ICP) alignment techniques.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.134-136
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• 2000

진화 알고리즘은 자원 관리, 스케줄링, 퍼지 논리 재어기의 설계 등의 다양한 문제들에 적용되는, 일반적이고 효율적인 최적화 방법이다. 그러나 이러한 진화 알고리즘의 문제점은 탐색해야할 변수의 증가에 따라 차원의 증가로 인하여 탐색공간이 기하급수적으로 늘어난다는 것이다. 이러한 문제점을 해결하기 위해 Potter와 Dejong은 개개의 종족을 독립적으로 진화시킴으로써 탐색공간을 대폭 줄인, 협력 공진화 알고리즘을 제안하였다. 그러나 이것 또한 변수 의존성이 강한 문제들에 대해서는 비효율적인 탐색을 하는 문제점이 있다. 본 논문에서는 종족의 분할과 병합을 이용한 효율적인 공진화 알고리즘을 제안한다. 이 알고리즘은 최적화 하려는 변수들이 서로 의존성이 없는 경우에는 종족의 분할을 통하여 탐색공간의 축소의 이점을 얻고, 최적화 하려는 변수들이 서로 의존성이 있는 경우에는 종족의 병합을 통하여 전역탐색을 하도록 한다. 제안하는 알고리즘을 상품재고 제어 문제(ICP)로 실험하여 현존하는 어떤 공진화 알고리즘보다도 효율적인 결과를 보여준다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.2
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• pp.137-148
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• 2004

In this paper, we propose a new camera calibration method for rotating multi-view cameras to generate image-based panoramic 3D Virtual Environment. Since calibration accuracy worsens with an increase in distance between camera and calibration pattern, conventional camera calibration algorithms are not proper for panoramic 3D VE generation. To remedy the problem, a geometric relationship among all lenses of a multi-view camera is used for intra-camera calibration. Another geometric relationship among multiple cameras is used for inter-camera calibration. First camera parameters for all lenses of each multi-view camera we obtained by applying Tsai's algorithm. In intra-camera calibration, the extrinsic parameters are compensated by iteratively reducing discrepancy between estimated and actual distances. Estimated distances are calculated using extrinsic parameters for every lens. Inter-camera calibration arranges multiple cameras in a geometric relationship. It exploits Iterative Closet Point (ICP) algorithm using back-projected 3D point clouds. Finally, by repeatedly applying intra/inter-camera calibration to all lenses of rotating multi-view cameras, we can obtain improved extrinsic parameters at every rotated position for a middle-range distance. Consequently, the proposed method can be applied to stitching of 3D point cloud for panoramic 3D VE generation. Moreover, it may be adopted in various 3D AR applications.