• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.93-96
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• 2007

LIDAR is a relatively new technological tool that can be used to accurately georeference terrain features, and also is becoming an important 3D mapping tool in GIS. In this study it is described the capabilities of terrestrial LIDAR that was used to build a 3D terrain model of extremely steep rock face, along with the useful data and examples of contributions terrestrial lidar has made to outcrop studies. For this, High-resolution terrestrial lidar acquisition, processing, interpretation are discussed and applied to mapping of geological surfaces in three dimensions. We expected that lidar is a tool with which we can improve our current field methods and quantify the observations geologists make.

• Journal of Korea Multimedia Society
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• v.24 no.11
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• pp.1518-1525
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• 2021

Due to the growth of VR industry and rise of digital twin industry, the importance of implementing 3D data same as real space is increasing. However, the fact that it requires expertise personnel and huge amount of time is a problem. In this paper, we propose a system that generates point cloud data with same shape and color as a real space, just by scanning the space. The proposed system integrates 3D geometric information from lidar and color information from stereo camera into one point cloud. Since the number of 3D points generated by lidar is not enough to express a real space with good quality, some of the pixels of 2D image generated by camera are mapped to the correct 3D coordinate to increase the number of points. Additionally, to minimize the capacity, overlapping points are filtered out so that only one point exists in the same 3D coordinates. Finally, 6DoF pose information generated from lidar point cloud is replaced with the one generated from camera image to position the points to a more accurate place. Experimental results show that the proposed system easily and quickly generates point clouds very similar to the scanned space.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.550-555
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• 2003

최근에 Lidar 데이터를 이용한 3차원 위치 정보와 지표면 속성 정보를 취득하는 연구가 많이 진행되고 있다. 높은 위치 정확도, 3차원 데이터 동시 취득, 기존 측정 방식에 비하여 점 데이터 취득의 자동화, 데이터 정확도의 안정성 등으로 인하여 복잡한 지형 및 인공구조물이 존재하는 지역에서 Lidar 데이터의 응용 사례가 많이 나타나고 있으며, 특히 건물 모델링에서 반자동 방식의 디지털 사진측량에 비하여 자동 모델링의 가능성을 보여주고 있다. 일반적으로 Lidar 데이터의 점밀도는 1점/㎡이내이며, 촬영된 스트립을 중복시켜 점밀도를 높이기도 한다. 건물은 크기와 형태가 다양하기 때문에 모델링에 필요한 점밀도를 제시하기는 어렵지만 5점 내외에서 모델링이 가능하다고 알려져 있으며 건물이외에 다른 지형지물에 대한 모델링 연구는 거의 이루어지지 않고 있다. 따라서 본 논문에서는 Lidar 데이터의 점밀도에 따라 지물의 모델링 가능성을 평가하고 효율적인 데이터 취득 방안을 제시하고자 한다.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.773-775
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• 2003

From the view point of data fusion, we integrate LIDAR data and digital aerial images to perform 3D building modeling in this study. The proposed scheme comprises two major parts: (1) building block extraction and (2) building model reconstruction. In the first step, height differences are analyzed to detect the above ground areas. Color analysis is then performed for the exclusion of tree areas. Potential building blocks are selected first followed by the refinement of building areas. In the second step, through edge detection and extracting the height information from LIDAR data, accurate 3D edges in object space is calculated. The accurate 3D edges are combined with the already developed SMS method for building modeling. LIDAR data acquired by Leica ALS 40 in Hsin-Chu Science-based Industrial Park of north Taiwan will be used in the test.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.1
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• pp.43-54
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• 2012

This paper presents a new localization technique of an UGV(Unmanned Ground Vehicle) by matching ortho-edge images generated from a DSM (Digital Surface Map) which represents the 3D geometric information of an outdoor navigation environment and 3D range data which is obtained from a LIDAR (Light Detection and Ranging) sensor mounted at the UGV. Recent UGV localization techniques mostly try to combine positioning sensors such as GPS (Global Positioning System), IMU (Inertial Measurement Unit), and LIDAR. Especially, ICP (Iterative Closest Point)-based geometric registration techniques have been developed for UGV localization. However, the ICP-based geometric registration techniques are subject to fail to register 3D range data between LIDAR and DSM because the sensing directions of the two data are too different. In this paper, we introduce and match ortho-edge images between two different sensor data, 3D LIDAR and DSM, for the localization of the UGV. Details of new techniques to generating and matching ortho-edge images between LIDAR and DSM are presented which are followed by experimental results from four different navigation paths. The performance of the proposed technique is compared to a conventional ICP-based technique.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.235-241
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• 2006

The demand of 3D terrain mapping techniques is increasing in many application fields such as CNS(Car Navigation System), web service system, DMB(Digital Multimedia Broadcasting) systems and etc To construct a 3D terrain map, it is a pre-requite step that register data collected from different surveying sources. This Paper Present the methodology to register airborne photo, LIDAR data, and digital map, which are major data sources to create a 3D terrain mao. For this purpose, we developed the generally applicable algorithm that uses linear features to register airborne photos and digital maps to LIDAR data. The algorithm explicitly formulates step-by-step methodologies to establish observation equations for transformation. The results clearly demonstrate the proposed algorithm is appropriate to register these data sources.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.10-11
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• 2015

최근 자율 주행 분야의 연구에서 LIDAR를 활용한 분류 기법들이 연구되고 있다. 그러나 2D 영상 처리와 비교하여, 대량의 3D 포인트를 사용하는 분류 알고리즘의 성능을 평가하기 위한 지상 검증자료를 쉽게 획득하기 어렵다. 본 연구는 LIDAR를 가상 공간에서 시뮬레이션 함으로써 감지한 물체의 정보를 기록함으로써 3D 포인트 클라우드를 사용하는 다양한 분류 알고리즘의 검증을 위한 지상검증자료를 생성하는 기법을 설명한다. 본 기법은 실제 LIDAR를 사용하는 것보다 적은 비용으로 다양한 환경에서의 분류 알고리즘 성능 검증을 가능하게 한다.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.637-641
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• 2007

Airborne LIDAR (Light Detection and Ranging) technology has reached a degree of the required accuracy in mapping professions, and advanced LIDAR systems are becoming increasingly common in the various fields of application. LiDAR data constitute an excellent source of information for reconstructing the Earth's surface due to capability of rapid and dense 3D spatial data acquisition with high accuracy. However, organizing the LIDAR data and extracting information from the data are difficult tasks because LIDAR data are composed of randomly distributed point clouds and do not provide sufficient semantic information. The main reason for this difficulty in processing LIDAR data is that the data provide only irregularly spaced point coordinates without topological and relational information among the points. This study introduces an efficient and robust method for automatic extraction of building footprints using airborne LIDAR data. The proposed method separates ground and non-ground data based on the histogram analysis and then rearranges the building boundary points using convex hull algorithm to extract building footprints. The method was implemented to LIDAR data of the heavily built-up area. Experimental results showed the feasibility and efficiency of the proposed method for automatic producing building layers of the large scale digital maps and 3D building reconstruction.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.923-929
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• 2008

Due to the increasing need for 3D spatial data, modeling of topography and artificial structures plays an important role in three-dimensional Urban Analysis. This study suggests a methodology for solving the problem of calculation for the extraction of building boundary, minimizing the user's intervention, and automatically extracting building boundary, using the LIDAR data. The methodology suggested in this study is characterized by combining the merits of the point-based process and the image-based process. The procedures for extracting building boundary are three steps: 1) LIDAR point data are interpolated to extract approximately building region. 2) LIDAR point data are triangulated in each individual building area. 3) Extracted boundary of each building is then simplified in consideration of its area, minimum length of building.The performance of the developed methodology is evaluated using real LIDAR data. Through the experiment, the extracted building boundaries are compared with digital map.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.3
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• pp.261-272
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• 2005

3D models in urban areas are essential for a variety of applications, such as virtual visualization, GIS, and mobile communications. LIDAR (Light Detection and Ranging) is a relatively new technology for obtaining Digital Terrain Models (DTM) of the earth's surface since manual 3D data reconstruction is very costly and time consuming. In this paper an approach to extract ground and non-ground points data from LIDAR data by using filtering is presented and the accuracy for generating DTM from ground points data is evaluated. Numerous filter algorithms have been developed to date. To determine the performance of filtering, we selected three filters which are based on the concepts for height difference, slope, and morphology, and also were applied two different data acquired from high raised apartments areas and low house areas. From the results it has been found that the accuracy for generating DTM from LIDAR data are 0.16 m and 0.59 m in high raised apartments areas and low house areas respectively. We expect that LIDAR data is used to generate the accurate DTM in urban areas.