• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.2
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• pp.127-136
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• 2002

DEM(Digital Elevation Model) is used widely in image processing, water resources, construction, GIS, landscape architecture, telecommunication, military operations and other related areas. And it is used especially in producing ortho-photo based on specific DEM and developing 3D GIS database vividly. As LiDAR(Light and Detection And Ranging) system emerged recently, DEM could be developed in urban area more efficiently and more economically, compared to the conventional DEM Production. Traditional method using check points for elevation has tome limitations in structure's height accuracy by LiDAR, because it uses only terrain height. Accordingly after the downtown of Chungju city was selected as a test field in this paper and DEM and digital ortho images was produced by way of LiDar survey, the accuracy was evaluated through analytical plotting map. The result shows that in case of buildings in LiDAR DEM, the accuracy is 0.30 m in X, 0.62 m in Y and RMS is 1.17 m. The difference distribution between DEM and plotting map in range of $\pm$10 cm was 36.2% and $\pm$10 cm $\pm$20 cm was 43.53%. The accuracy of LiDAR in this study meets 1/5,000 which is the regulation for map of NGI(National Geography Institute) and LiDAR can be possibly used in many other applied area.

• ETRI Journal
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• v.43 no.4
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• pp.603-616
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• 2021

Over the last few years, autonomous vehicles have progressed very rapidly. The odometry technique that estimates displacement from consecutive sensor inputs is an essential technique for autonomous driving. In this article, we propose a fast, robust, and accurate odometry technique. The proposed technique is light detection and ranging (LiDAR)-based direct odometry, which uses a spherical range image (SRI) that projects a three-dimensional point cloud onto a two-dimensional spherical image plane. Direct odometry is developed in a vision-based method, and a fast execution speed can be expected. However, applying LiDAR data is difficult because of the sparsity. To solve this problem, we propose an SRI generation method and mathematical analysis, two key point sampling methods using SRI to increase precision and robustness, and a fast optimization method. The proposed technique was tested with the KITTI dataset and real environments. Evaluation results yielded a translation error of 0.69%, a rotation error of 0.0031°/m in the KITTI training dataset, and an execution time of 17 ms. The results demonstrated high precision comparable with state-of-the-art and remarkably higher speed than conventional techniques.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.611-622
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• 2020

As the advancement of technologies on indoor positioning systems and measuring devices such as LiDAR (Light Detection And Ranging) and cameras, the demands on analyzing and searching indoor spaces and visualization services via virtual and augmented reality have rapidly increasing. To this end, it is necessary to model 3D objects from measured data from real-world structures. In addition, it is important to store these structured data in standardized formats to improve the applicability and interoperability. In this paper, we propose a method to construct IndoorGML data, which is an international standard for indoor modeling, from point cloud data acquired from LiDAR sensors. After examining considerations that should be addressed in IndoorGML data, we present a construction method, which consists of free space extraction and connectivity detection processes. With experimental results, we demonstrate that the proposed method can effectively reconstruct the 3D model from point cloud.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.149-152
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• 2006

LiDAR(Light Detection and Ranging)는 레이저 광선을 주사하여 목표물에 도달하는 시간측정을 바탕으로 x, y, z 좌표의 고밀도의 정밀한 점 자료를 획득하며, 도시 지역의 형상 추출, 수치고도모델 제작 및 산림 지역에서 수고 측정 등의 생태학적인 분야에 이르기까지 그 활용분야가 점차로 증대되고 있다. 이 연구에서는 LiDAR 시스템이 목표물에서 반사되어 들어오는 신호(return)를 여러 번에 걸쳐 나누어 기록하는 자료를 이용하여 수목의 수관층 및 하층 식생 등으로 복잡한 구조를 보이는 산림지역에서 LiDAR 신호가 투과되는 특성을 이용한 지수를 계산하였다. 수관점유율과 관련성을 보이는 지수는 향후 엽면적지수(LAI)와의 객관적인 관계를 규명하게 될 것이다.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2009.04a
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• pp.244-245
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• 2009

일반적으로 LiDAR(Light Detection And Ranging)의 자료로부터 3차원 위치정보와 속성 정보를 취득하여 활용 하는 연구가 많이 진행되고 있다. 본 연구에서는 Grid($100m{\times}100m$) 기반인 2차원적 Grid Point를 통해 Sensor Field를 정하고 LiDAR의 3차원적 좌표정보를 이용하여 최적 센서 위치를 선정하고 중간에 장애물(Obstacle)이 존재하는 경우 또한 알고리즘을 통해 최적위치인 Grid point를 선정하였다. 알고리즘은 3가지 측면을 고려하여 분류하였다. 첫째 장애물이 없는(Non Obstacle) 2차원적인 경우, 둘째 장애물이 존재(Obstacle)하는 2차원적인 경우, 셋째 장애물이 존재(Obstacle)하며 3차원적인 알고리즘을 고려하였다. 향후 연구에서는 LiDAR를 직접 적용하여 최적 선정 지역을 도출하여 알고리즘을 적용할 것이다.

• Journal of Advanced Navigation Technology
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• v.24 no.3
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• pp.192-197
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• 2020

Recently, the technologies related to autonomous drive has studying the goal for safe operation and prevent accidents of vehicles. There is radar and camera technologies has used to detect obstacles in these autonomous vehicle research. Now a day, the method for using LiDAR sensor has considering to detect nearby objects and accurately measure the separation distance in the autonomous navigation. It is calculates the distance by recognizing the time differences between the reflected beams and it allows precise distance measurements. But it also has the disadvantage that the recognition rate of object in the atmospheric environment can be reduced. In this paper, point cloud data by triangular functions and Line Regression model are used to implement measurement algorithm, that has improved detecting objects in real time and reduce the error of measuring separation distances based on improved reliability of raw data from LiDAR sensor. It has verified that the range of object detection errors can be improved by using the Python imaging library.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.2
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• pp.209-215
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• 2006

Shoreline changes its shapes and attribution dynamically by natural, unnatural acts and is the most information for country. These shorelines can apply to framework data of MGIS (Marine Geographic Information System), and they are getting important to implement a phase of monitoring around coastal areas. This study proposed an algorithm automatically extracting shorelines to use a new developed LiDAR (Light Detection And Ranging) data which is applying in ocean and coastal areas. Then, in result, it was compared to shorelines which is derived from ground survey. In result, it shows stable shorelines in various coast areas such as nature, artificial coast. Additionally, and a possibility of shoreline extraction through LiDAR data.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.656-657
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• 2008

Light Detection and Ranging (LiDAR) data has been used to detect the objects of earth surface from huge point clouds gotten from the laser scanning system equipped on airplane. According to the precision of 3~5 points per square meter, objects like buildings, cars and roads can be easily described and constructed. Many various areas, such as hydrological modeling and urban planning adopt this kind of significant data. Researchers have been engaging in finding accurate road networks from LiDAR data recent years. In this paper, A novel algorithm with regard to extracting road points from LiDAR data has been developed based on the continuity and structural characteristics of road networks.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.5
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• pp.215-225
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• 2020

Light detection and ranging (LiDAR) sensors detect the distance of the surrounding environment and objects. Conventional LiDAR sensors require a certain amount of a power because they detect objects by transmitting lasers at a regular interval depending on a constant resolution. The constant power consumption from operating multiple LiDAR sensors is detrimental to autonomous and electric vehicles using battery power. In this paper, we propose two algorithms that improve the inefficient power consumption during the constant operation of LiDAR sensors. LiDAR sensors with algorithms efficiently reduce the power consumption in two ways: (a) controlling the resolution to vary the laser transmission period (T_P) of a laser diode (LD) depending on the vehicle's speed and (b) reducing the static power consumption using a sleep mode depending on the surrounding environment. A proposed LiDAR sensor with a resolution control algorithm reduces the power consumption of the LD by 6.92% to 32.43% depending on the vehicle's speed, compared to the maximum number of laser transmissions (N_x·max). The sleep mode with a surrounding environment-sensing algorithm reduces the power consumption by 61.09%. The proposed LiDAR sensor has a risk factor for 4-cycles that does not detect objects in the sleep mode, but we consider it to be negligible because it immediately switches to an active mode when a change in surrounding conditions occurs. The proposed LiDAR sensor was tested on a commercial processor chip with the algorithm controlling the resolution according to the vehicle's speed and the surrounding environment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.453-464
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• 2019

Recently, as the demand for 3D geospatial information increases, the importance of rapid and accurate data construction has increased. Although many studies have been conducted to register UAV (Unmanned Aerial Vehicle) imagery based on LiDAR (Light Detection and Ranging) data, which is capable of precise 3D data construction, studies using LiDAR data embedded in MMS (Mobile Mapping System) are insufficient. Therefore, this study compared and analyzed 9 matching algorithms based on feature points for registering reflectance image converted from LiDAR point cloud intensity data acquired from MMS with image data from UAV. Our results indicated that when the SIFT (Scale Invariant Feature Transform) algorithm was applied, it was able to stable secure a high matching accuracy, and it was confirmed that sufficient conjugate points were extracted even in various road environments. For the registration accuracy analysis, the SIFT algorithm was able to secure the accuracy at about 10 pixels except the case when the overlapping area is low and the same pattern is repeated. This is a reasonable result considering that the distortion of the UAV altitude is included at the time of UAV image capturing. Therefore, the results of this study are expected to be used as a basic research for 3D registration of LiDAR point cloud intensity data and UAV imagery.