• Journal of KIISE
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• v.43 no.5
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• pp.562-568
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• 2016

Point cloud data can be expressed in a specific coordinate system of a data set with a large number of points, to represent any form that generally has different characteristics in the three-dimensional coordinate space. This paper is aimed at finding a cylindrical pipe in the point cloud of the three-dimensional coordinate system using RANSAC, which is faster than the conventional Hough Transform method. In this study, the proposed cylindrical pipe is estimated by combining the results of parameters based on two mathematical models. The two kinds of mathematical models include a sphere and line, searching the sphere center point and radius in the cylinder, and detecting the cylinder with straightening of center. This method can match cylindrical pipe with relative accuracy; furthermore, the process is rapid except for normal estimation and segmentation. Quick cylinders matching could benefit from laser scanning and reverse engineering construction sectors that require pipe real-time estimates.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.314-324
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• 2018

This paper proposes a linear distributed target tracking filter for multiple unmanned aerial vehicles(UAVs) sharing their passive sensor measurements through communication channels. Different from the conventional nonlinear filtering schemes, the distributed passive target tracking problem is newly formulated within the framework of a linear robust state estimation theory incorporated with a linear uncertain measurement equation including the coordinate transform uncertainty. To effectively cope with the performance degradation due to the coordinate transform uncertainty, a linear consistent robust Kalman filter(CRKF) theory is devised and applied for designing a distributed passive target tracking filter. Through the simulations for typical UAV surveillance mission, the superior performance of the proposed method over the existing schemes of distributed passive target tracking are demonstrated.

• Industrial Engineering and Management Systems
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• v.6 no.2
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• pp.94-105
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• 2007

Measurement technology plays an important role in discrete manufacturing industry. Probe-type coordinate measuring machines (CMMs) are normally used to capture the geometry of part features. The measured points are then fit to verify a specified geometry by using the least squares method (LSQ). However, it occasionally overestimates the tolerance zone, which leads to the rejection of some good parts. To overcome this drawback, minimum zone approaches defined by the ANSI Y14.5M-1994 standard have been extensively pursued for zone fitting in coordinate form literature for such basic features as plane, circle, cylinder and sphere. Meanwhile, complex features such as torus have been left to be dealt-with by the use of profile tolerance definition. This may be impractical when accuracy of the whole profile is desired. Hence, the true deviation model of torus is developed and then formulated as a minimax problem. Next, a relatively new and simple population based evolutionary approach, particle swarm optimization (PSO), is applied by imitating the social behavior of animals to find the minimum tolerance zone torusity. Simulated data with specified torusity zones are used to validate the deviation model. The torusity results are in close agreement with the actual torusity zones and also confirm the effectiveness of the proposed PSO when compared to those of the LSQ.

• Journal of the Korea Computer Graphics Society
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• v.23 no.3
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• pp.31-38
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• 2017

In this paper, we propose a system which estimates Manhattan coordinate systems for urban scene images using a convolutional neural network (CNN). Estimating the Manhattan coordinate system from an image under the Manhattan world assumption is the basis for solving computer graphics and vision problems such as image adjustment and 3D scene reconstruction. We construct a CNN that estimates Manhattan coordinate systems based on GoogLeNet [1]. To train the CNN, we collect about 155,000 images under the Manhattan world assumption by using the Google Street View APIs and calculate Manhattan coordinate systems using existing calibration methods to generate dataset. In contrast to PoseNet [2] that trains per-scene CNNs, our method learns from images under the Manhattan world assumption and thus estimates Manhattan coordinate systems for new images that have not been learned. Experimental results show that our method estimates Manhattan coordinate systems with the median error of $3.157^{\circ}$ for the Google Street View images of non-trained scenes, as test set. In addition, compared to an existing calibration method [3], the proposed method shows lower intermediate errors for the test set.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.spc4_2
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• pp.401-412
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• 2014

This study is purposed on showing an effective process for coordinate transformation of cadastral maps, based on the local datum, in the World Geodetic System (WGS) for Cadastral Reform Project (CRP). The process follows three steps: coordinate adjustment, datum transformation and distortion modeling. The first procedure is that point coordinates on local datum has to be adjusted by those GPS observed point-to-point distances, using trilateration. Secondly, the adjusted coordinates need to be transformed to WGS by applying the Affine model, while the verification of the methodology is implemented under numerical experiments. To conduct this procedure, 195 points in the same coordinate origins in Seoul and 61 points in several different origins in Incheon are used in the estimation. As a result, there are less than 2cm coordinate differences between transformed coordinates and measured ones at everywhere. Also, it is remarkable that the transformation does not depend on either of the particular common points or the sizes of computed region. Therefore, this suggested methodology is expected to easily provide identifications and corrections for points-deviations for improved quality of the cadastral map by distortion modeling through CRP.

• International Journal of Ocean System Engineering
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• v.2 no.4
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• pp.223-232
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• 2012

For the accurate estimation of the position and orientation of a UUV (unmanned underwater vehicle), a low-cost AHRS (attitude heading reference system) was developed using a low-cost IMU (inertial measurement unit) sensor which provides information on the 3D acceleration, 3D turning rate and 3D earth-magnetic field data in the object coordinate system. The main hardware system is composed of an IMU sensor (ADIS16405) and TMS320F28335, which is coded with an extended kalman filter algorithm with a 50-Hz sampling frequency. Through an experimental gimbal device, good estimation performance for the pitch, roll, and yaw angles of the developed AHRS was verified by comparing to those of a commercial AHRS called the MTi system. The experimental results are here presented and analyzed.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2182-2184
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• 2004

This paper presents a systematic and straightforward fault estimation approach for process fault detection. isolation and accommodation. The approach includes the design of a reduced-order observer and an algebraic-fault estimator. The observer is designed for an unknown input and fault-free system, which is obtained by coordinate transformations of original systems with unknown inputs and faults. The observer information is devoted to- the fault estimation for fault detection and isolation. The fault estimates can be used to form an additional control input to accommodate the fault. The suggested scheme is verified through simulation studies performed on the control of a vertical takeoff and landing (VTOL) aircraft in the vertical plane.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.344-351
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• 2017

In this paper, we propose the obstacle database management module for obstacle estimation and clustering. The proposed G-eye manager system can create customized walking route for blind people using the UI manager and verify the coordinates of the path. Especially, G-eye management system designed a regional information module. The regional information module can improve the loading speed of the obstacle data by classifying the local information by clustering the coordinates of the obstacle. In this paper, we evaluate the reliability of the walking route generated from the obstacle map. We obtain the coordinate value of the path avoiding the virtual obstacle from the proposed system and analyze the error rate of the path avoiding the obstacle according to the size of the obstacle. And we analyze the correlation between obstacle size and route by classifying virtual obstacles into sizes.

• Korean Journal of Computational Design and Engineering
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• v.14 no.1
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• pp.10-17
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• 2009

Surface detection and parameter estimation in point cloud is a relevant subject in CAD/CAM, reverse engineering, computer vision, coordinate metrology and digital factory. In this paper we present a software for a fully automatic surface detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting, which work interactively. Our newly developed algorithms for orthogonal distance fitting(ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. We demonstrate the performance of the software on a variety of point clouds generated by laser radar, computer tomography, and stripe-projection method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.12
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• pp.1002-1016
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• 1990

This paper described design and implementation of stereo vision system, and also, proposed method for displacement estimation of 3-D moving object using this system. The extraction of moving object is obtained by difference image algorithm. Geometrical position of 3-D moving object is calculated form the mapping of center area of two's 2-D object. 3-D coordinate position produced space depth, moving velociity, distance, moving track and proved displacement estimation of 3-D moving object.