• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.99-110
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• 2023

In this paper, we propose a system termed ForestLi that can accurately estimate the diameter at breast height (DBH) and tree height using LiDAR point cloud data. The ForestLi system processes LiDAR point cloud data through the following steps: downsampling, outlier removal, ground segmentation, ground height normalization, stem extraction, individual tree segmentation, and DBH and tree height measurement. A commercial system, such as LiDAR360, for processing LiDAR point cloud data requires the user to directly correct errors in lower vegetation and individual tree segmentation. In contrast, the ForestLi system can automatically remove LiDAR point cloud data that correspond to lower vegetation in order to improve the accuracy of estimating DBH and tree height. This enables the ForestLi system to reduce the total processing time as well as enhance the accuracy of accuracy of measuring DBH and tree height compared to the LiDAR360 system. We performed an empirical study to confirm that the ForestLi system outperforms the LiDAR360 system in terms of the total processing time and accuracy of measuring DBH and tree height.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.3
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• pp.397-406
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• 2022

Recently, research and development to revitalize smart construction are being actively carried out. Accordingly, 3D mapping technology that digitizes construction site is drawing attention. To create a 3D digital map for construction site a point cloud generation method based on LiDAR(Light detection and ranging) using MMS(Mobile mapping system) is mainly used. The purpose of this study is to analyze the accuracy of MMS LiDAR-based point cloud data. As a result, accuracy of MMS point cloud data was analyzed as dx = 0.048m, dy = 0.018m, dz = 0.045m on average. In future studies, accuracy comparison of point cloud data produced via UAV(Unmanned aerial vegicle) photogrammetry and MMS LiDAR should be studied.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_3
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• pp.1233-1241
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• 2023

This paper investigates the efficient application of drone LiDAR technology for acquiring precise point cloud data in construction and civil engineering. A structured workflow encompassing data acquisition, processing, and accuracy verification is introduced. Practical testing on a construction site affirms that drone LiDAR surveying yields accurate and reliable data across various applications. With a focus on accuracy and verification, the results contribute to the progression of surveying methodologies in construction and civil engineering. The findings provide valuable insights into the dynamic technological landscape of these fields, establishing a foundation for more effective and precise surveying techniques. This study underscores the transformative potential of drone LiDAR technology in shaping the future of construction and civil engineering survey practices.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_3
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• pp.1325-1332
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• 2023

Recently, much research is being conducted based on point cloud data for the growth of innovations such as construction automation in the transportation field and virtual national space. This data is often measured through remote control in terrain that is difficult for humans to access using devices such as UAVs and UGVs. Drones, one of the UAVs, are mainly used to acquire point cloud data, but photogrammetry using a vision camera, which takes a lot of time to create a point cloud map, is difficult to apply in construction sites where the terrain changes periodically and surveying is difficult. In this paper, we developed a point cloud mapping system by adopting non-repetitive scanning LiDAR and attempted to confirm improvements through field application. For accuracy analysis, a point cloud map was created through a 2 minute 40 second flight and about 30 seconds of software post-processing on a terrain measuring 144.5 × 138.8 m. As a result of comparing the actual measured distance for structures with an average of 4 m, an average error of 4.3 cm was recorded, confirming that the performance was within the error range applicable to the field.

• 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.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.387-388
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• 2010

There is no generalized and systematic method yet to data pre-processing for point cloud data classification even if there have been lots of previous studies such as local maxima filter, morphology filter, slope based filter and so on. Main focus of this study is to present classification method for bare ground information from LiDAR data for the mountainous area.

• 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.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.463-467
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• 2006

Several segmentation algorithms have been proposed for DTM generation or building modeling from airborne LiDAR data. Three components are important for accurate segmentation: (i) the adjacent relationship of n-nearest points or mesh, etc. (ii) the effective decision parameters of height, slope, curvature, and plane condition, (iii) grouping methods. In this paper, we created the topology of point cloud data using the contour tree and implemented the region-growing Terrain and non-terrain points were classified correctly in the segmented data, which can be used also for feature classification.

• 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 the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.593-605
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• 2012

LiDAR systems provide dense and accurate topographic information. A pre-requisite to achieving the potential accuracy of LiDAR is having a proper system calibration, which aims at estimating all the systematic errors in the system measurements and the mounting parameters relating the different components. This paper presents a rigorous and two approximate methods for LiDAR system calibration. The rigorous approach makes use of the LiDAR equation and the system raw measurements. The approximate approaches utilize simplified LiDAR equations using some assumptions, which allow for less strict requirements regarding the raw measurements. The first presented approximate method, denoted as quasi-rigorous, assumes that we are dealing with a vertical platform (i.e., small pitch and roll angles). This method requires time-tagged point cloud and trajectory position data. The second approximate method, denoted as simplified, assumes that we are dealing with parallel strips, vertical platform, and minor terrain elevation variations compared to the flying height above ground. Such method can be performed using the LiDAR point cloud only. Experimental results using a real dataset, whose characteristics deviate to some extent from the utilized assumptions in the approximate methods, are presented to provide a comparative analysis of the outcome from the introduced methods.