• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.2
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• pp.111-122
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• 2015

This study purposed on introducing a new automated solution for detecting railway tracks and reconstructing track models from the mobile laser scanning data. The proposed solution completes following procedures; the study initiated with detecting a potential railway region, called Region Of Interest (ROI), and approximating the orientation of railway track trajectory with the raw data. At next, the knowledge-based detection of railway tracks was performed for localizing track candidates in the first strip. In here, a strip -referring the local track search region- is generated in the orthogonal direction to the orientation of track trajectory. Lastly, an initial track model generated over the candidate points, which were detected by GMM-EM (Gaussian Mixture Model-Expectation & Maximization) -based clustering strip- wisely grows to capture all track points of interest and thus converted into geometric track model in the tracking by detection framework. Therefore, the proposed railway track tracking process includes following key features; it is able to reduce the complexity in detecting track points by using a hypothetical track model. Also, it enhances the efficiency of track modeling process by simultaneously capturing track points and modeling tracks that resulted in the minimization of data processing time and cost. The proposed method was developed using the C++ program language and was evaluated by the LiDAR data, which was acquired from MMS over an urban railway track area with a complex railway scene as well.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.577-580
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• 2005

Surface quality of a micro-processed sample with laser has been investigated by using of scanning confocal microscope(SCM) and atomic force microscope(AFM). Samples are bump electrodes and ITO glass of LCD module used in a mobile phone and a wafer surface scribed by UV laser. A image of $140\times120{\mu}m^2$ is obtained within 1 second by SCM because scan speed of a x-axis and y-axis are 1kHz and 1Hz, respectively. AFM is able to measure correctly hight and width of ITO and scribing depth and width of a wafer with a resolution less than 300 . However, the scan speed is slow and it is difficult to distinguish a surface composed of different nm kinds of materials. Results show that SCM is preferable to obtain a image of a sample composed of different kinds of material than AFM because the intensity of a reflected light from surface is different from each material.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.4 s.181
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• pp.52-59
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• 2006

Surface qualities of a micro-processed sample with a pulse laser have been investigated by making use of scanning confocal microscope(SCM) and atomic force microscope(AFM). Samples are bump electrodes and ITO glass of LCD module used in a mobile phone and a wafer surface scribed by UV laser. A image of $140{\times}120{\mu}m^2$ is obtained within 1 second by SCM because scan speed of a x-axis and y-axis are 1kHz and 1Hz, respectively. AFM is able to correctly measure the hight and width of ITO, and scribing depth and width of a wafer with a resolution less than 300nm. However, the scan speed is slow and it is difficult to distinguish a surface composed of different kinds of materials. Results show that SCM is preferable to obtain a image of a sample composed of different kinds of material than AFM because the intensity of a reflected light from the surface is different for each material.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.2
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• pp.85-91
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• 2014

The objective of the research is to automatically recognize railway objects from MLS data in which 9 key objects including terrain, track, bed, vegetation, platform, barrier, posts, attachments, powerlines are targeted. The proposed method can be divided into two main sub-steps. First, multi-scale contextual features are extracted to take the advantage of characterizing objects of interest from different geometric levels such as point, line, volumetric and vertical profile. Second, by considering contextual interactions amongst object labels, a contextual classifier is utilized to make a prediction with local coherence. In here, the Conditional Random Field (CRF) is used to incorporate the object context. By maximizing the object label agreement in the local neighborhood, CRF model could compensate the local inconsistency prediction resulting from other local classifiers. The performance of proposed method was evaluated based on the analysis of commission and omission error and shows promising results for the practical use.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.6
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• pp.497-502
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• 2015

One of the goals in the field of mobile robotics is the development of personal service robots for the elderly which behave in populated environments. In this paper, we describe a security robot system and ongoing research results that minimize the risk of the elderly and the infirm to access an area to enter restricted areas with high potential for falls, such as stairs, steps, and wet floors. The proposed robot system surveys a potential falling area with an equipped laser scanner sensor. When it detects walking in elderly or infirm patients who in restricted areas, the robot calculates the velocity vector, plans its own path to forestall the patient in order to prevent them from heading to the restricted area and starts to move along the estimated trajectory. The walking human is assumed to be a point-object and projected onto a scanning plane to form a geometrical constraint equation that provides position data of the human based on the kinematics of the mobile robot. While moving, the robot continues these processes in order to adapt to the changing situation. After arriving at an opposite position to the human's walking direction, the robot advises them to change course. The simulation and experimental results of estimating and tracking of the human in the wrong direction with the mobile robot are presented.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.995-1005
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• 2011

An efficient outdoor local localization method is suggested using multi-sensor fusion with MU-EKF (Multi-Update Extended Kalman Filter) for car-like mobile robots. In outdoor environments, where mobile robots are used for explorations or military services, accurate localization with multiple sensors is indispensable. In this paper, multi-sensor fusion outdoor local localization algorithm is proposed, which fuses sensor data from LRF (Laser Range Finder), Encoder, and GPS. First, encoder data is used for the prediction stage of MU-EKF. Then the LRF data obtained by scanning the environment is used to extract objects, and estimates the robot position and orientation by mapping with map objects, as the first update stage of MU-EKF. This estimation is finally fused with GPS as the second update stage of MU-EKF. This MU-EKF algorithm can also fuse more than three sensor data efficiently even with different sensor data sampling periods, and ensures high accuracy in localization. The validity of the proposed algorithm is revealed via experiments.

• Korean Journal of Construction Engineering and Management
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• v.17 no.3
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• pp.32-42
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• 2016

In Korea, the absence of BIM use in existing civil structures and buildings is driving a demand for as-built BIM. As-built BIMs are often created using laser scanners that provide dense 3D point cloud data. Conventional static laser scanning approaches often suffer from limitations in their operability due to the difficulties in moving the equipment, the selection of scanning location, and the requirement of placing targets or extracting tie points for registration of each scanned point cloud. This paper aims at reducing the manual effort using a kinematic 3D laser scanning system based on graph-based simultaneous localization and mapping (SLAM) for continuous indoor mapping. The robotic platform carries three 2D laser scanners: the front scanner is mounted horizontally to compute the robot's trajectory and to build the SLAM graph; the other two scanners are mounted vertically to scan the profiles of surrounding environments. To reduce the accumulated error in the trajectory of the platform through loop closures, the graph-based SLAM system incorporates AdaBoost loop closure approach, which is particularly suitable for the developed multi-scanner system providing more features than the single-scanner system for training. We implemented the proposed method and evaluated it in two indoor test sites. Our experimental results show that the false positive rate was reduced by 13.6% and 7.9% for the two dataset. Finally, the 2D and 3D mapping results of the two test sites confirmed the effectiveness of the proposed graph-based SLAM.

• Transactions of Materials Processing
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• v.20 no.2
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• pp.104-109
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• 2011

The metal front case of a mobile phone is manufactured by press forming and welding of thin metal sheets. Twisting of the frame after the forming process is one of main obstacle for the assembly with reinforcement by welding. This study introduces a method preventing twisting of the metal front case frame in press forming. The spring-back after forming produces twisting of the frame, which leads to a low structural stiffness. To reduce twisting, connectors are required to reinforce the structural stiffness of the frame. In this study, the twisting profile is evaluated using a finite element(FE) analysis for various connector shapes. The actual connector shape is determined by minimization of the frame twisting within the tolerance of the FE-analysis. To verify the validity of the proposed blank shape, a forming experiment is performed and the twisting profile is measured using a 3D laser scanning method. The dimensional accuracy is found to be within the tolerance and in good agreement with the FE-analysis.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.05b
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• pp.62-65
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• 2003

When a mobile robot moves around autonomously without man-made corrupted bye landmarks, it is essential to recognize the placement of surrounding objects especially for self localization, obstacle avoidance, and target classification and localization. To recognize the environment we use many Kinds of sensors, such as ultrasonic sensors, laser range finder, CCD camera, and so on. Among the sensors, ultra sonic sensors(sonar)are unexpensive and easy to use. In this paper, we analyze the sonar data and propose a method to recognize features of indoor environment. It is supposed that the environments are consisted of features of planes, edges, and corners, For the analysis, sonar data of plane, edge, and corner are accumulated for several given ranges. The data are filtered to eliminate some noise using the Kalman filter algorithm. Then, the data for each feature are compared each other to extract the character is ties of each feature. We demonstrate the applicability of the proposed method using the sonar data obtained form a sonar transducer rotating and scanning the range information around a indoor environment.

• Proceedings of the KIEE Conference
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• 2002.06a
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• pp.114-119
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• 2002

본 논문에서는 자율 이동 로봇의 장애물에 대한 실시간 충돌 회피 동작의 문제에 대하여, 로봇이 장애물과 충돌하지 않는 안정적인 회피 동작과 유연한 궤적 생성, 그리고 효과적인 최적의 조향 동작을 계획할 수 있게 하기 위하여 레이저 스케닝 센서를 이용한 지역 장애물 회피 방법을 제안한다. 레이저 센서를 이용한 로봇의 안전한 방향 탐색은 자율 이동 로봇이 검출할 수 있는 최대의 검출 영역에서부터 로봇의 중심점을 향해 순차적으로 임계치를 줄여가는 동안 나타나는 무충돌 안전 구간과 충돌 구간을 정의함으로서 구성된다. 제안한 안전 방향 구간 탐색에 의한 로봇의 장애물 회피 동작의 성능 실험은 최적 방향의 탐색 성능을 평가하며, 실제의 이동 로봇을 이용하여 실험한 결과에 대하여 고찰하고 결론을 내린다.