• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.727-742
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• 2020

Point cloud-based 3D maps can obtain many kinds of information for maintenance work on forest road networks. This study was conducted to compare the importance of each factor to select the factors required for the mapping of 3D forest road maps. This can be used as basic data for attribute information required to maintain forest road networks. The results of this study found that out of a total of 30 indexes extracted for mapping 3D forest roads, a total of 21 indexes related to stakeholder groups were significantly different. The importance of the index required by the civil service group was significantly higher than that of the other groups overall. In the case of the academic group, the index importance for cut slope, fill slope, and drainage facility was significantly higher. On the other hand, the index importance for the forestry cooperative and forest professional engineer group was mostly distributed between the civil servants' group and the academic group. In particular, the type of drainage system showed the highest value among the detailed indexes. Overall, drainage related factors in this survey had high coefficient values. The impact of water on forest roads was the most important part in road maintenance. In addition, the soil texture had a high value in relation to slope stability. This is thought to be because the texture of the soil affects the stability of the slope.

• Journal of KIBIM
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• v.10 no.1
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• pp.1-8
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• 2020

In managing building facilities, spatial information is the basic data for decision making. However, the method of acquiring spatial information is not easy. In many cases, the site and drawings are often different due to changes in facilities and time after construction. In this case, the site data should be scanned to obtain spatial information. The scan data actually contains spatial information, which is a great help in making space related decisions. However, to obtain scan data, an expensive LiDAR (Light Detection and Ranging) device must be purchased, and special software for processing data obtained from the device must be available.Recently, SLAM (Simultaneous localization and mapping), an advanced map generation technology, has been spreading in the field of robotics. Using SLAM, 3D spatial information can be obtained quickly in real time without a separate matching process. This study develops and tests whether SLAM technology can be used to obtain spatial information for facility management. This draws considerations for developing a SLAM device for real-time remote scanning for facility management. However, this study focuses on the system development method that acquires spatial information necessary for facility management through SLAM technology. To this end, we develop a prototype, analyze the pros and cons, and then suggest considerations for developing a SLAM system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.2
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• pp.103-111
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• 2021

MMS (Mobile Mapping System) is being used for HD (High Definition) map construction because it enables fast and accurate data construction, and it is receiving a lot of attention. However, research on the use of MMS in the construction field is insufficient. In this study, road surveying and inspection of construction structures were performed using MMS. Through data acquisition and processing using MMS, point cloud data for the study site was created, and the accuracy was evaluated by comparing with traditional surveying methods. The accuracy analysis results showed a maximum of 0.096m, 0.091m, and 0.093m in the X, Y, and H directions, respectively. Each RMSE was 0.012m, 0.015m, and 0.006m. These result satisfy the accuracy of topographic surveying in the general survey work regulation, indicating that construction surveying using MMS is possible. In addition, a 3D model was created using the design data for the underpass road, and the inspection was performed by comparing it with the MMS data. Through inspection results, deviations in construction can be visually confirmed for the entire underground roadway. The traditional method takes 6 hours for the 4.5km section of the target area, but MMS can significantly shorten the data acquisition time to 0.5 hours. Accurate 3D data is essential data as basic data for future smart construction. With MMS, you can increase the efficiency of construction sites with fast data collection and accuracy.

• Journal of Drive and Control
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• v.19 no.4
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• pp.62-69
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• 2022

Construction machinery is exposed to accidents such as collisions, narrowness, and overturns during operation. In particular, mobile crane is operated only with the driver's vision and limited information of the assistant worker. Thus, there is a high risk of an accident. Recently, some collision avoidance device using sensors such as cameras and LiDAR have been applied. However, they are still insufficient to prevent collisions in the omnidirectional 3D space. In this study, a rotating LiDAR device was developed and applied to a 250-ton crane to obtain a full-space point cloud. An algorithm that could provide distance information and safety status to the driver was developed. Also, deep-learning segmentation algorithm was used to classify human-worker. The developed device could recognize obstacles within 100m of a 360-degree range. In the experiment, a safety distance was calculated with an error of 10.3cm at 30m to give the operator an accurate distance and collision alarm.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.6
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• pp.471-476
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• 2022

In this paper, we propose an object detection using a 4D imaging radar, which developed to solve the problems of weak cameras and LiDAR in bad weather. When data are measured and collected through a 4D imaging radar, the density of point cloud data is low compared to LiDAR data. A technique for clustering objects and extracting the features of objects through voxels in the cluster is proposed using the characteristics of wide distances between objects due to low density. Furthermore, we propose an object detection using the extracted features.

• SUVANNABHUMI
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• v.13 no.2
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• pp.167-186
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• 2021

The Grand Stupa is mentioned in historical text as 'Rajakudakan', which means a royal building with a multitiered superstructure. This Grand Stupa is the principal construction of Wat Chedi Luang, and marks the center of the Chiang Mai City Plan. This study argues that the Grand Stupa was built in 1391 during Phaya Saen Mueang Ma's reign, possibly inspired by the construction of Ku Phaya in Bagan. Thereafter, in 1545, the Grand Stupa's superstructure collapsed after the great earthquake, resulted in the irreparable damage since then. Therefore, a survey using a 3D laser scanner is conducted to collect the most precise data on the current condition of the Grand Stupa, yielding an assumption of its reconstruction. Other simultaneous stupas or those that show a close architectural relationship (e.g. stupas in Wat Chiang Man and Wat Lok Moli and the stupa of King Tilokaraj in Wat Chet Yot in Chiang Mai) are also employed as research frameworks for the reconstruction. As a result, the architectural research on the Grands Stupa, compared with simultaneous stupas, yields a fruitful argument that the pre-collapse superstructure form of the Grand Stupa marks the most architectural similarity to the stupa of Wat Chiang Man.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.25-31
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• 2022

Moving among multiple floors is one of the most challenging tasks for indoor autonomous robots. Most of the previous researches for indoor mapping and localization have focused on singular floor environment. In this paper, we present an algorithm that creates a multi-floor map using 3D point cloud. We implement localization within the multi-floor map using a LiDAR and an IMU. Our algorithm builds a multi-floor map by constructing a single-floor map using a LOAM-based algorithm, and stacking them through global registration that aligns the common sections in the map of each floor. The localization in the multi-floor map was performed by adding the height information to the NDT (Normal Distribution Transform)-based registration method. The mean error of the multi-floor map showed 0.29 m and 0.43 m errors in the x, and y-axis, respectively. In addition, the mean error of yaw was 1.00°, and the error rate of height was 0.063. The real-world test for localization was performed on the third floor. It showed the mean square error of 0.116 m, and the average differential time of 0.01 sec. This study will be able to help indoor autonomous robots to operate on multiple floors.

• Journal of KIBIM
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• v.12 no.3
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• pp.39-51
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• 2022

3D scan technology can utilize real spatial information as it is in virtual space, so it can be usefully used in various fields such as reverse engineering of buildings and process management. Recently, with the development of ICT technology, more precise scan data can be obtained, and scan processing time has also been greatly reduced. In addition, the combination of software and scanning equipment used in 3D scanning technology is very diverse, and results are very different depending on which technology is used. Accordingly, there is a problem that it is difficult for a user who has no experience in 3D scanning technology to determine which technology and equipment should be used to obtain good results. In this study, 3D scan technologies mainly used at home and abroad are investigated, classified, and tested at actual construction sites to suggest considerations and suitable 3D scan methods when using 3D scans in construction sites. The test results were analyzed to evaluate the time it takes to scan, the final quality, and the user's convenience according to each technology method.

• The Journal of Korea Robotics Society
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• v.16 no.4
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• pp.299-305
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• 2021

In this study, the task of robotic tidy-up is to clean the current environment up exactly like a target image. To perform a tidy-up task using a robot, it is necessary to estimate the pose of various objects and to classify the objects. Pose estimation requires the CAD model of an object, but these models of most objects in daily life are not available. Therefore, this study proposes an algorithm that uses point cloud and PCA to estimate the pose of objects without the help of CAD models in cluttered environments. In addition, objects are usually detected using a deep learning-based object detection. However, this method has a limitation in that only the learned objects can be recognized, and it may take a long time to learn. This study proposes an image matching based on few-shot learning and Siamese network. It was shown from experiments that the proposed method can be effectively applied to the robotic tidy-up system, which showed a success rate of 85% in the tidy-up task.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.20-25
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• 2022

This paper represents an adaptive Region of Interest (ROI) decision for real-time performance in an autonomous driving perception module. Since the whole automated driving system consists of numerous modules and subdivisions of module occur, it is necessary to consider the characteristics, complexity, and limitations of each module. Furthermore, Light Detection And Ranging (Lidar) sensors require a considerable amount of time. In view of these limitations, division of submodule is inevitable to represent high real-time performance for stable system. This paper proposes ROI to reduce the number of data respect to computation time. ROI is set by a road's design speed and the corresponding ROI is applied differently to each vehicle considering its speed. The simulation model is constructed by ROS, and overall data analysis is conducted by Matlab. The algorithm is validated using real-time driving data in urban environment, and the result shows that ROI provides low computational costs.