• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.4_1
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• pp.319-326
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• 2014

The accurate geo-referencing processes that apply ground control points is prerequisite for effective end use of HRSI (High-resolution satellite imagery). Since the conventional control point acquisition by human operator takes long time, demands for the automated matching to existing reference data has been increasing its popularity. Among many options of reference data, the airborne LiDAR (Light Detection And Ranging) data shows high potential due to its high spatial resolution and vertical accuracy. Additionally, it is in the form of 3-dimensional point cloud free from the relief displacement. Recently, a new matching method between LiDAR data and HRSI was proposed that is based on the image projection of whole LiDAR data into HRSI domain, however, importing and processing the large amount of LiDAR data considered as time-consuming. Therefore, we wmotivated to ere propose a local LiDAR chip generation for the HRSI geo-referencing. In the procedure, a LiDAR point cloud was rasterized into an ortho image with the digital elevation model. After then, we selected local areas, which of containing meaningful amount of edge information to create LiDAR chips of small data size. We tested the LiDAR chips for fully-automated geo-referencing with Kompsat-2 and Kompsat-3 data. Finally, the experimental results showed one-pixel level of mean accuracy.

• Journal of KIBIM
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• v.14 no.2
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• pp.1-12
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• 2024

The paper proposes a methodology that combines the Random Sample Consensus (RANSAC) algorithm and the Point Cloud Encoder-Decoder Network (PCEDNet) algorithm to automatically extract the length of infrastructure elements from point cloud data acquired through 3D LiDAR scans of retaining walls. This methodology is expected to significantly improve time and cost efficiency compared to traditional manual measurement techniques, which are crucial for the data-driven analysis required in the precision-demanding construction sector. Additionally, the extracted positional and dimensional data can contribute to enhanced accuracy and reliability in Scan-to-BIM processes. The results of this study are anticipated to provide important insights that could accelerate the digital transformation of the construction industry. This paper provides empirical data on how the integration of digital technologies can enhance efficiency and accuracy in the construction industry, and offers directions for future research and application.

• Journal of the Korea Institute of Building Construction
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• v.23 no.3
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• pp.273-284
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• 2023

This study is centered on the combined use of LiDAR(Light Detection and Ranging) and AR(Augmented Reality) technologies during vertical and horizontal frame measurements in construction projects. The intention is to enhance the quality control procedure, elevate accuracy, and curtail manual labor along with time expenditure. Present methods for accuracy inspection in frame construction often grapple with reliability concerns due to subjective interpretation and the scope for human error. This research recommends the application of LiDAR and AR technologies to counter these issues and augment the efficiency of the inspection process, along with facilitating the dissemination of results. The suggested technique involves the collection of 3D point cloud data of the frame utilizing LiDAR and leveraging this data for checks on construction accuracy. Furthermore, the inspection outcomes are fed into a BIM (Building Information Modeling) model, and the results are visualized via AR. Upon juxtaposing this methodology with the current approach, it is evident that it offers benefits in terms of objective inspection, speed, precise result sharing, and potential enhancements to the overall quality and productivity of construction projects.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.2
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• pp.91-102
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• 2017

Backpack-mounted mapping system is firstly introduced for flexible movement in indoor spaces where satellite-based localization is not available. With the achieved advances in miniaturization and weight reduction, use of LiDAR (Light Detection and Ranging) sensors in mobile platforms has been increasing, and indeed, they have provided high-precision information on indoor environments and their surroundings. Previous research on the development of backpack-mounted mapping systems, has concentrated mostly on the improvement of data processing methods or algorithms, whereas practical system components have been determined empirically. Thus, in the present study, a simulator for a LiDAR sensor (Velodyne VLP-16), was developed for comparison of the effects of diverse conditions on the backpack system and its operation. The simulated data was analyzed by visual inspection and comparison of the data sets' statistics, which differed according to the LiDAR arrangement and moving speed. Also, the data was used as input to a point-cloud registration algorithm, ICP (Iterative Closest Point), to validate its applicability as pre-analysis data. In fact, the results indicated centimeter-level accuracy, thus demonstrating the potentials of simulation data to be utilized as a tool for performance comparison of pointdata processing methods.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.99-106
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• 2019

Technology that cannot be excluded from 4th industry is self-driving sector. The self-driving sector can be seen as a key set of technologies in the fourth industry, especially in the DB sector is getting more and more popular as a business. The DB, which was previously produced and managed in two dimensions, is now evolving into three dimensions. Among the data obtained by Mobile Mapping System () to produce the HD MAP necessary for self-driving, Point Cloud, which is LiDAR data, is used as a DB because it contains accurate location information. However, at present, it is not widely used as a base data for 3D modeling in addition to HD MAP production. In this study, MMS Point Cloud was used to extract facilities around the road and to overlay the location to expand the usability of Point Cloud. Building utility poles and communication poles DB from Point Cloud and comparing road name address base and location, it is believed that the accuracy of the location of the facility DB extracted from Point Cloud is also higher than the basic road name address of the road, It is necessary to study the expansion of the facility field sufficiently.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.8
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• pp.5695-5706
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• 2015

Recently adoption of BIM technology for building renovation and remodeling has been increased in construction industry. However most buildings have trouble in 2D drawing-based BIM modeling, because 2D drawings have not been updated real situations continually. Applying reverse engineering, this study analysed the point cloud data structure and the process for guideline of reverse engineering of architectural MEP, and deducted the relating considerations. To active usage of 3D scanning technique in domestic, the objective of this study is to analyze the point cloud data processing from real site with terrestrial LiDAR and the process from data gathering to data acquisition.

• 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 Broadcast Engineering
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• v.28 no.3
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• pp.329-332
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• 2023

3D Object Detection using deep neural network has been developed a lot for obstacle detection in autonomous vehicles because it can recognize not only the class of target object but also the distance from the object. But in the case of 3D Object Detection models, the detection performance for distant objects is lower than that for nearby objects, which is a critical issue for autonomous vehicles. In this paper, we introduce a technique that increases the performance of 3D object detection models, particularly in recognizing distant objects, by generating virtual 3D vehicle data and adding it to the dataset used for model training. We used a spherical point tracing method that leverages the characteristics of 3D LiDAR sensor data to create virtual vehicles that closely resemble real ones, and we demonstrated the validity of the virtual data by using it to improve recognition performance for objects at all distances in model training.

• The Journal of Engineering Geology
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• v.29 no.3
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• pp.251-264
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• 2019

The displacement monitoring of unstable block at the rock slope located in the Cheonbuldong valley of Seoraksan National Park was carried out using Terrestrial LiDAR. The rock slopes around Guimyeonam and Oryeon waterfall where rockfall has occurred or is expected to occur are selected as the monitoring section. The displacement monitoring of unstable block at the rock slope in the selected area was performed 5 times for about 7 months using Terrestrial LiDAR. As a result of analyzing the displacement based on the Terrestrial LiDAR scanning, the error of displacement was highly influenced by the interpolation of the obstruction section and the difference of plants growth. To minimize the external influences causing the error, the displacement of unstable block should be detected at the real scanning point. As the result of analyzing the displacement of unstable rock at the rock slope using the Terrestrial LiDAR data, the amount of displacement was very small. Because the amount of displacement was less than the range of error, it was difficult to judge the actual displacement occurred. Meanwhile, it is important to select a section without vegetation to monitor the precise displacement of unstable rock at the rock slope using Terrestrial LiDAR. Also, the PointCloud removal and the mesh model analysis in a vegetation section were the most important work to secure reliability of data.

• Explosives and Blasting
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• v.40 no.4
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• pp.15-26
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• 2022

With the recent increase in old and dangerous buildings, the demand for technology in the field of structure demolition is rapidly increasing. In particular, in the case of structures with severe deformation of damage, there is a risk of deterioration in stability and disaster due to changes in the load distribution characteristics in the structure, so rapid structure demolition technology that can be efficiently dismantled in a short period of time is drawing attention. However, structural deformation such as unauthorized extension or illegal remodeling occurs frequently in many old structures, which is not reflected in structural information such as building drawings, and acts as an obstacle in the demolition design process. In this study, as an effective way to overcome the discrepancy between the structural information of old structures and the actual structure, access to actual structures through 3D modeling was considered. 3D point cloud data inside and outside the building were obtained through LiDAR and drone photography for buildings scheduled to be blasting demolition, and precision matching between the two spatial data groups was performed using an open-source based spatial information construction system. The 3D structure model was completed by importing point cloud data matched with 3D modeling software to create structural drawings for each layer and forming each member along the structure slab, pillar, beam, and ceiling boundary. In addition, the modeling technique proposed in this study was verified by comparing it with the actual measurement value for selected structure member.