• 한국공간정보시스템학회:학술대회논문집
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• 2004.12a
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• pp.119-124
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• 2004

LiDAR 자료를 이용한 지형 및 공간 정보 자료를 구축하기 위해서는 LiDAR 자료의 오차 보정, 건물영역 및 지면 분리, 건물 및 지형의 재구성 등의 과정이 필요하다. 그 중에서 건물영역 분리 과정은 막대한 양의 LiDAR 자료에 대한 직, 간접적인 처리를 필요로 하며 결과물의 품질에도 큰 영향을 미친다. 본 연구에서는 LiDAR 자료로부터 건물 영역을 분리해 내기 위하여 LiDAR 원 자료를 그대로 활용하는 방식을 제안하였다. 항공레이저측량은 스캔라인을 따라 취득되는 포인트정보를 순서대로 저장하여 제공하므로 LiDAR 자료로부터 연속적으로 입력되는 포인트들은 서로 인접할 가능성이 높다. 이와 같은 특성을 이용하여 유사한 고도 값을 갖는 인접 포인트들로 클래스를 형성하고 새로운 포인트가 속할 클래스를 검색하여 편입시킴으로써 건물영역을 분리해 낸다. 아울러 각 건물 클래스에 대한 레이블링도 자동적으로 수행하며 새로운 포인트가 편입될 클래스를 검색하는 방법에 있어서도, 클래스의 검색 순서와 클래스의 자료 구조를 효율적으로 운용함으로써 성능 향상을 도모하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.236-242
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• 2019

Recently, scanning projects have been carried out in various construction and construction fields for maintenance purposes. The point cloud generated by the scan results is composed of a number of points representing the object to be scanned. The process of extracting the necessary information, including dimensions, from such scan data is called paradox. The reverse engineering process of modeling a point cloud as BIM involves considerable manual work. Owing to the time-consuming reverse engineering nature of the work, the costs increase exponentially when rework requests are made, such as design changes. Reverse engineering automation technology can help improve these problems. On the other hand, the reverse design product is variable depending on the use, and the kind and detail level of the product may be different. This paper proposes the G2BM (Geometry-to-BIM mapping) rule definition method that automatically maps a BIM object from a primitive geometry to a BIM object. G2BM proposes a process definition and a customization method for reverse engineering BIM objects that consider the use case variability.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.1-7
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• 2019

When using conventional 3D image scanning methods, it is common for image scanning to be done manually, which is labor-intensive. Scanning a space made up of complicated equipment or scanning a narrow space that is difficult for the user to enter, is problematic, resulting in quality degradation due to the presence of shadow areas. This paper proposes a method to scan an image using a rover equipped with a scanner in areas where it is difficult for a person to enter. To control the scan path precisely, the 3D image remote scan automation method based on the rover move rule definition is described. Through the study, the user can automate the 3D scan plan in a desired manner by defining the rover scan path as the rule base.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.829-830
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• 2010

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.345-352
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• 2022

Recent LiDAR(Light Detection And Ranging) sensor is used for scanning object around in real-time. This sensor can detect movement of the object and how it has changed. As the production cost of the sensors has been decreased, LiDAR begins to be used for various industries such as facility guard, smart city and self-driving car. However, LiDAR has a large input data size due to its real-time scanning process. So another way for processing a large amount of data are needed in LiDAR system because it can cause a bottleneck. This paper proposes edge computing to compress massive point cloud for processing quickly. Since laser's reflection range of LiDAR sensor is limited, multiple LiDAR should be used to scan a large area. In this reason multiple LiDAR sensor's data should be processed at once to detect or recognize object in real-time. Edge computer compress point cloud efficiently to accelerate data processing and decompress every data in the main cloud in real-time. In this way user can control LiDAR sensor in the main system without any bottleneck. The system we suggest solves the bottleneck which was problem on the cloud based method by applying edge computing service.

• 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.

• Journal of Korea Spatial Information System Society
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• v.10 no.4
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• pp.21-26
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• 2008

A method for processing huge airborne laser scanned data using parallel computing and virtual grid is proposed and the method is tested by generating raster DSM(Digital Surface Model) with IDW(Inverse Distance Weighting). Parallelism is involved for fast interpolation of huge point data and virtual grid is adopted for enhancing searching efficiency of irregularly distributed point data. Processing time was checked for the method using cluster constituted of one master node and six slave nodes, resulting in efficiency near to 1 and load scalability property. Also large data which cannot be processed with a sole system was processed with cluster system.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.3
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• pp.245-259
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• 2023

The purpose of this paper is to examine the most recent tunnel scanning systems to obtain insights for the development of non-contact mobile inspection system. Tunnel scanning systems are mostly being developed by adapting two main technologies, namely laser scanning and image scanning systems. Laser scanning system has the advantage of accurately recreating the geometric characteristics of tunnel linings from point cloud. On the other hand, image scanning system employs computer vision to effortlessly identify damage, such as fine cracks and leaks on the tunnel lining surface. The analysis suggests that image scanning system is more suitable for detecting damage on tunnel linings. A camera-based tunnel scanning system under development should include components such as lighting, data storage, power supply, and image-capturing controller synchronized with vehicle speed.

• Tunnel and Underground Space
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• v.19 no.1
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• pp.1-10
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• 2009

Site characterization for structural stability in rock masses mainly involves the collection of joint property data, and in the current practice, much of this data is collected by hand directly at exposed slopes and outcrops. There are many issues with the collection of this data in the field, including issues of safety, slope access, field time, lack of data quantity, reusability of data and human bias. It is shown that information on joint orientation, spacing and roughness in rock masses, can be automatically extracted from LIDAR (light detection and ranging) point floods using the currently available Split-FX point cloud processing software, thereby reducing processing time, safety and human bias issues.

• Journal of KIBIM
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• v.10 no.1
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• pp.1-8
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• 2020

In managing building facilities, spatial information is the basic data for decision making. However, the method of acquiring spatial information is not easy. In many cases, the site and drawings are often different due to changes in facilities and time after construction. In this case, the site data should be scanned to obtain spatial information. The scan data actually contains spatial information, which is a great help in making space related decisions. However, to obtain scan data, an expensive LiDAR (Light Detection and Ranging) device must be purchased, and special software for processing data obtained from the device must be available.Recently, SLAM (Simultaneous localization and mapping), an advanced map generation technology, has been spreading in the field of robotics. Using SLAM, 3D spatial information can be obtained quickly in real time without a separate matching process. This study develops and tests whether SLAM technology can be used to obtain spatial information for facility management. This draws considerations for developing a SLAM device for real-time remote scanning for facility management. However, this study focuses on the system development method that acquires spatial information necessary for facility management through SLAM technology. To this end, we develop a prototype, analyze the pros and cons, and then suggest considerations for developing a SLAM system.