• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1545-1550
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• 2022

In this research, a method for minimizing the replacement time of AMR (Autonomous Mobile Robot), which is used in various industrial groups such as logistics and manufacturing, was studied in the event of a LiDAR failure. In this regard, a general LiDAR exchange process was defined and a new exchange process based on the newly designed jig, which is mounted on the AMR, for the quick change of LiDAR was proposed. The experiment is conducted using commercialized AMR which was developed for application in the factory of an automobile manufacturing company. It was confirmed that LiDAR can be replaced and aligned within 24 minutes when the new exchange process is employed, which is about 76% or more shorter than the general LiDAR exchange process. As a result, we can minimize AMR downtime and overall process delays by applying the proposed process.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.119-124
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• 2006

The world which based on knowledge and information is changing significantly. In the various knowledge and information, the importance of GSIS has increased for efficient application and management of country. The Geomatics has made a change rapidly, observation methods have improved too. The existing acquisition of Geoinformation depend on aerial photogtaphs, but new technology Jike application of high resolution satellite images. SAR and LiDAR, is the fastest. especially, LiDAR surveying is most advanced active observation technology and Geoinfomtation is acquired by reflection of its laser pulse. In this study. the position accuracy of extracted building from LiDAR was evaluated by GPS surveying, then each data was made comparison between LiDAR's and GPS's data. After processing. the result of this study will be suggested basic data about application.

• Journal of Sensor Science and Technology
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• v.32 no.5
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• pp.267-274
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• 2023

LiDAR, one of the most important sensing methods used in mobile robots and cars with assistive/autonomous driving functions, is used to locate surrounding obstacles or to build maps. For real-time path generation, the detection of potholes or puddles on the driving surface is crucial. To achieve this, we used the coordinates of the reflection points provided by LiDAR as well as the intensity information to classify water areas, which was achieved by applying a linear regression method to the intensity distribution. The rationale for using the LiDAR index as an input variable for linear regression is presented, and we demonstrated that it is not affected by errors in the distance measurement value. Because of LiDAR vertical scanning, if the reflective surface is not uniform, it is divided into different groups according to the intensity distribution, and a mathematical basis for this is presented. Through experiments in an outdoor driving area, we could distinguish between flat ground, potholes, and puddles, and kinematic analysis was performed to calculate the maximum width that could be crossed for a given vehicle body size and wheel radius.

• 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 Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.3
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• pp.313-322
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• 2005

Rapid advances in technology and changes in human and cultural activities bring about changes to the earth surface in terms of spatial extension as well as time frame of the changes. Such advances introduce shorter updating frequency of maps and geospatial database. To satisfy these requirements, recent research efforts in the geoinformatics field have been focused on the automation and speeding up of the mapping processes which resulted in products such as the digital photogrammetric workstation, GPSIINS, applications of satellite imagery, automatic feature extraction and the LiDAR system. The possibility of automatically extracting buildings and generating contours from airborne LiDAR data has received much attention because LiDAR data produce promising results. However, compared with the manually derived building footprints using traditional photogrammetric process, more investigation and analysis need to be carried out in terms of accuracy and efficiency. On the other hand, generation of the contours with LiDAR data is more efficient and economical in terms of the quality and accuracy. In this study, the effects of various conditions of the pre-processing phase and the subsequent building extraction and contour generation phases for digital mapping have on the accuracy were investigated.

• 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.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.157-163
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• 2019

The LiDAR sensor, which is widely regarded as one of the most important sensors, has recently undergone active commercialization owing to the significant growth in the production of ADAS and autonomous vehicle components. The LiDAR sensor technology involves radiating a laser beam at a particular angle and acquiring a three-dimensional image by measuring the lapsed time of the laser beam that has returned after being reflected. The LiDAR sensor has been incorporated and utilized in various devices such as drones and robots. This study focuses on object detection and recognition by employing sensor fusion. Object detection and recognition can be executed as a single function by incorporating sensors capable of recognition, such as image sensors, optical sensors, and propagation sensors. However, a single sensor has limitations with respect to object detection and recognition, and such limitations can be overcome by employing multiple sensors. In this paper, the performance of an eight-channel scanning LiDAR was evaluated and an object detection algorithm based on it was implemented. Furthermore, object detection characteristics during daytime and nighttime in a real road environment were verified. Obtained experimental results corroborate that an excellent detection performance of 92.87% can be achieved.

• Journal of Cadastre & Land InformatiX
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• v.49 no.2
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• pp.57-66
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• 2019

Recently, as a part of the production of spatial information by smart cities, three-dimensional reproduction of structures for reverse engineering has been attracting attention. In particular, terrestrial LiDAR is mainly used for 3D reproduction of structures, and 3D reproduction research by UAS has been actively conducted. However, both technologies produce blind spots due to the shooting angle. This study deals with vertical structures. 3D model implemented through SfM-based image analysis technology using UAS and reproducibility and effectiveness of 3D models by terrestrial LiDAR-based laser scanning are examined. In addition, two 3D models are merged and reviewed to complement the blind spot. For this purpose, UAS based image is acquired for artificial rock wall, VCP and check point are set through GNSS equipment and total station, and 3D model of structure is reproduced by using SfM based image analysis technology. In addition, Through 3D LiDAR scanning, the 3D point cloud of the structure was acquired, and the accuracy of reproduction and completeness of the 3D model based on the checkpoint were compared and reviewed with the UAS-based image analysis results. In particular, accuracy and realistic reproducibility were verified through a combination of point cloud constructed from UAS and terrestrial LiDAR. The results show that UAS - based image analysis is superior in accuracy and 3D model completeness and It is confirmed that accuracy improves with the combination of two methods. As a result of this study, it is expected that UAS and terrestrial LiDAR laser scanning combination can complement and reproduce precise three-dimensional model of vertical structure, so it can be effectively used for spatial information construction, safety diagnosis and maintenance management.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.137-147
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• 2023

LiDAR is used in autonomous driving and various industrial fields to measure the size and distance of an object. In addition, the sensor also provides intensity images based on the amount of reflected light. This has a positive effect on sensor data processing by providing information on the shape of the object. LiDAR guarantees higher performance as the resolution increases but at an increased cost. These conditions also apply to LiDAR intensity images. Expensive equipment is essential to acquire high-resolution LiDAR intensity images. This study developed artificial intelligence to improve low-resolution LiDAR intensity images into high-resolution ones. Therefore, this study performed parameter analysis for the optimal super-resolution neural network model. The super-resolution algorithm was trained and verified using 2,500 LiDAR intensity images. As a result, the resolution of the intensity images were improved. These results can be applied to the autonomous driving field and help improve driving environment recognition and obstacle detection performance

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.1
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• pp.1-11
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• 2018

Mapping accurate spatial information is important for constructing three-dimensional (3D) spatial models and managing artificial facilities, and, especially, mapping road centerlines is necessary for constructing accurate road maps. This research developed a semi-automatic methodology for mapping road centerlines using the MMS(Mobile Mapping System) LiDAR(Light Detection And Ranging) point cloud as follows. First, the intensity image was generated from the given MMS LiDAR data through the interpolation method. Next, the line segments were extracted from the intensity image through the edge detection technique. Finally, the road centerline segments were manually selected among the extracted line segments. The statistical results showed that the generated road centerlines had 0.065 m overall accuracy but had some errors in the areas near road signs.