• Journal of Digital Contents Society
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• v.18 no.5
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• pp.989-994
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• 2017

In this paper, we propose a pothole detection system using 2D LiDAR and a pothole detection algorithm. Conventional pothole detection methods can be divided into vibration-based method, 3D reconstruction method, and vision-based method. Proposed pothole detection system uses two inexpensive 2D LiDARs and improves pothole detection performance. Pothole detection algorithm is divided into preprocessing for noise reduction, clustering and line extraction for visualization, and gradient function for pothole decision. By using gradient of distance data function, we check the existence of a pothole and measure the depth and width of the pothole. The pothole detection system is developed using two LiDARs, and the 3D pothole detection performance is shown by detecting a pothole with moving LiDAR system.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.152-161
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• 2017

Making precise 3D maps using Mobile Mapping System (MMS) sensors are essential for the development of self-driving cars. This paper conducts research on the extraction of 3D objects around the roads using the point cloud acquired by the MMS Light Detection and Ranging (LiDAR) sensor through the following steps. First, the digital surface model (DSM) is generated using MMS LiDAR data, and then the slope map is generated from the DSM. Next, the 3D objects around the roads are identified using the slope information. Finally, 97% of the 3D objects around the roads are extracted using the morphological filtering technique. This research contributes a plan for the application of automated driving technology by extracting the 3D objects around the roads using spatial information data acquired by the MMS sensor.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.1
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• pp.47-56
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• 2010

The use of airborne LiDAR data obtained by airborne laser scanners has increased in the field of spatial information such as building modeling. LiDAR data consist of irregularly distributed 3D coordinates and lack visual and semantic information. Therefore, LiDAR data processing is complicate. This study suggested a method of LiDAR data segmentation using roof surface patches from aerial images. Each segmented patch was modeled by analyzing geometric characteristics of the LiDAR data. The optimal functions could be determined with segmented data that fits various shapes of the roof surfaces as flat and slanted planes, dome and arch types. However, satisfiable segmentation results were not obtained occasionally due to shadow and tonal variation on the images. Therefore, methods to remove unnecessary edges result in incorrect segmentation are required.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.141-152
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• 2008

The 3D building modeling is one of crucial components in constructing 3D geospatial information. The existing methods for 3D building modeling depend mainly on manual photogrammetric processes, which indeed take great amount of time and efforts. In recent years, many researches on 3D building modeling using aerial LiDAR data have been actively performed to aim at overcoming the limitations of existing 3D building modeling methods. Either techniques with interpolated grid data or data fusion with digital map and images have been investigated in most of existing researches on 3D building modeling with aerial LiDAR data. The paper proposed a method of 3D building modeling with LiDAR data only. Firstly, octree-based segmentation is applied recursively to LiDAR data classified as buildings in 3D space until there are no more LiDAR points to be segmented. Once octree-based segmentation is completed, each segmented patch is thereafter merged together based on its geometric spatial characteristics. Secondly, building model components are created with merged patches. Finally, a 3D building model is generated and composed with building model components. The experimental results with real LiDAR data showed that the proposed method was capable of modeling various types of 3D buildings.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.4
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• pp.253-265
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• 2016

We propose an obstacle classification method using multi-decision factors and decision sections based on Single 2D LiDAR. The existing obstacle classification method based on single 2D LiDAR has two specific advantages: accuracy and decreased calculation time. However, it was difficult to classify obstacle type, and therefore accurate path planning was not possible. To overcome this problem, a method of classifying obstacle type based on width data was proposed. However, width data was not sufficient to enable accurate obstacle classification. The proposed algorithm of this paper involves the comparison between decision factor and decision section to classify obstacle type. Decision factor and decision section was determined using width, standard deviation of distance, average normalized intensity, and standard deviation of normalized intensity data. Experiments using a real autonomous vehicle in a real environment showed that calculation time decreased in comparison with 2D LiDAR-based method, thus demonstrating the possibility of obstacle type classification using single 2D LiDAR.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.931-936
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• 2008

Surveying by using terrestrial LiDAR(Light Detection And Ranging) is more rapid than by using total station which enables tunnel section profile surveying to be done in suitable time and minimize centerline error, occurrence of overcut and undercut. Therefore, utilization of terrestrial LiDAR has increased more and more in section profile survey and measurement field Moreover, studies of terrestrial LiDAR for accurate and efficient utilization is now ongoing vigorously. Average end area formula, which was generally used to calculate overcut and undercut, was compared with existing methods such as total station survey and photogrammetry. However, there are no criteria of spacing distance for calculating overcut and undercut through terrestrial LiDAR surveying which can acquire 3D information of whole tunnel. This research performed reverse engineering to decide optimal spacing distance when surveying tunnel section profile by comparing whole tunnel volume and tunnel volume in difference spacing distance. This result was utilized to produce CAD drawing for the test tunnel site where there is no design drawings. In addition to this, efficiency of LiDAR and accuracy of CAD drawing was compared with targetless total station surveying of tunnel section profile. Finally, error analysis of target coordinate's accuracy and incidence angle was done in order to verify the accuracy of terrestrial LiDAR technology.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.4
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• pp.429-438
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• 2011

LiDAR technology is a combination of laser ranging, satellite positioning technology and digital image technology for study and determination with high accuracy of the true earth surface features in 3 D. Laser scanning data is typically a points cloud on the ground, including coordinates, altitude and intensity of laser from the object on the ground to the sensor (Wehr & Lohr, 1999). Data from laser scanning can produce products such as digital elevation model (DEM), digital surface model (DSM) and the intensity data. In Vietnam, the LiDAR technology has been applied since 2005. However, the application of LiDAR in Vietnam is mostly for topological mapping and DEM establishment using point cloud 3D coordinate. In this study, another application of LiDAR data are present. The study use the intensity image combine with some other data sets (elevation data, Panchromatic image, RGB image) in Bacgiang City to perform land cover classification using neural network method. The results show that it is possible to obtain land cover classes from LiDAR data. However, the highest accurate classification can be obtained using LiDAR data with other data set and the neural network classification is more appropriate approach to conventional method such as maximum likelyhood classification.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.1 s.39
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• pp.67-73
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• 2007

Recently, three-dimensional (3D) GI (Geospatial Information) using LiDAR system has been used various fields such as the production of digital map, the modeling of 3D building and urban area, and analysis of communication network and environmental effect. In this study, the production cost of digital map by aerial LiDAR surveying were compared with the cost by aerial photograph surveying for analysis of economical efficiency. It is expected that the results of this study will be used base data for production, update, revision of digital map and curtail effect of national budget.

• Journal of the Korean Society of Industry Convergence
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• v.20 no.2
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• pp.125-132
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• 2017

The slope of the road in the forest area has a characteristic of steep slope, so natural disasters such as slope collapse occur. The slope displacement observation technique according to landslide is being studied as a method to observe a wide area and a method to observe a small area. This is a study on high-precision digital map generation using ground LiDAR. It is possible to create a high - precision digital map by minimizing the US side using the 3D LiDAR in the steep slope area where the GPS and Total Station measurement are difficult in the maintenance of the danger slope area. It is difficult to objectively evaluate whether the contour lines generated by LiDAR are correct and it is considered necessary to construct a test bed for this purpose. Based on this study, if terrain changes such as landslides occur in the future, it will be useful for measuring slope displacement.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.55-63
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• 2021

The roughness of the road is an important factor directly connected to the ride comfort, and is an evaluation item for functional evaluation and pavement quality management of the road. In this study, data on the road surface were acquired using the latest 3D geospatial information construction technology of ground LiDAR, drone photogrammetry, and drone LiDAR, and the accuracy and roughness of each method were analyzed. As a result of the accuracy evaluation, the average accuracy of terrestrial LiDAR were 0.039m, 0.042m, 0.039m RMSE in X, Y, Z direction, and drone photogrammetry and drone LiDAR represent 0.072~0.076m, 0.060~0.068m RMSE, respectively. In addition, for the roughness analysis, the longitudinal and lateral slopes of the target section were extracted from the 3D geospatial information constructed by each method, and the design values were compared. As a result of roughness analysis, the ground LiDAR showed the same slope as the design value, and the drone photogrammetry and drone LiDAR showed a slight difference from the design value. Research is needed to improve the accuracy of drone photogrammetry and drone LiDAR in measurement fields such as road roughness analysis. If the usability through improved accuracy can be presented in the future, the time required for acquisition can be greatly reduced by utilizing drone photogrammetry and drone LiDAR, so it will be possible to improve related work efficiency.