• Proceedings of the KSRS Conference
• /
• 2007.10a
• /
• pp.548-551
• /
• 2007

Among components of digital topographic maps used officially in Korea, only contours have 3D values except buildings and trees that are demanded in landscape planning. This study presented a series of processes for 3Dreconstructing landscape features such as terrain, buildings and standing trees using LiDAR (Light Detection And Ranging) data and aerial digital photo graphs. The 3D reconstructing processes contain 1) building terrain model, 2) delineating outline of landscape features, 3) extracting height values, and 4) shaping and coloring landscape features using aerial photograph and 3-D virtual data base. LiDAR data and aerial photograph was taken in November 2006 for $50km^{2}$ area in Sorak National Park located in eastern part of Korea. The average scanning density of LiDAR pulse was 1.32 points per square meter, and the aerial photograph with RGB bands has $0.35m{\times}0.35m$ spatial resolution. Using reconstructed 3D landscape features, visibility with the growing trees with time and at different viewpoints was analyzed. Visible area from viewpoint could be effectively estimated considering 3D information of landscape features. This process could be applied for landscape planning like building scale with the consideration of surrounding landscape features.

• ETRI Journal
• /
• v.43 no.4
• /
• pp.594-602
• /
• 2021

Recently, quantitative and repetitive inspections of the old urban area were conducted because many structures exceed their designed lifetime. The health of a building can be validated from the condition of the outer wall, while the slant angle of the wall widely serves as an indicator of urban regeneration projects. Mostly, the inspector directly measures the inclination of the wall or partially uses 3D point measurements using a static light detection and ranging (LiDAR). These approaches are costly, time-consuming, and only limited space can be measured. Therefore, we propose a mobile mapping system and automatic slant map generation algorithm, configured to capture urban environments online. Additionally, we use the LiDAR-inertial mapping algorithm to construct raw point clouds with gravity information. The proposed method extracts walls from raw point clouds and measures the slant angle of walls accurately. The generated slant angle map is evaluated in indoor and outdoor environments, and the accuracy is compared with real tiltmeter measurements.

• Journal of Urban Science
• /
• v.7 no.2
• /
• pp.53-60
• /
• 2018

Digital Twin is a technology that creates a photocopy of real-world objects on a computer and analyzes the past and present operational status by fusing the structure, context, and operation of various physical systems with property information, and predicts the future society's countermeasures. In particular, 3D rendering technology (UAS, LiDAR, GNSS, etc.) is a core technology in digital twin. so, the research and application are actively performed in the industry in recent years. However, UAS (Unmanned Aerial System) and LiDAR (Light Detection And Ranging) have to be solved by compensating blind spot which is not reconstructed according to the object shape. In addition, the terrestrial LiDAR can acquire the point cloud of the object more precisely and quickly at a short distance, but a blind spot is generated at the upper part of the object, thereby imposing restrictions on the forward digital twin modeling. The UAS is capable of modeling a specific range of objects with high accuracy by using high resolution images at low altitudes, and has the advantage of generating a high density point group based on SfM (Structure-from-Motion) image analysis technology. However, It is relatively far from the target LiDAR than the terrestrial LiDAR, and it takes time to analyze the image. In particular, it is necessary to reduce the accuracy of the side part and compensate the blind spot. By re-optimizing it after fusion with UAS and Terrestrial LiDAR, the residual error of each modeling method was compensated and the mutual correction result was obtained. The accuracy of fusion-based 3D model is less than 1cm and it is expected to be useful for digital twin construction.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.13 no.2
• /
• pp.54-63
• /
• 2010

The intensive rainfall over 455 mm occurred between on 9 to 14 July 2009 triggered debris flows around the mountain area in Jecheon County. We mapped the debris flow area and estimated the debris flow volume using a high resolution digital elevation model (DEM) generated respectively from terrestrial LiDAR (Light Detection And Ranging) and topographic maps. For the LiDAR measurement, the terrestrial laser scanning system RIEGL LMS-Z390i which is equipped with GPS system and high-resolution digital camera were used. After the clipping and filtering, the point data generated by LiDAR scanning were overlapped with digital map and produced DEM after debris flow. The comparison between digital map and LiDAR scanning result showed the erosion and deposition volumes of about $17,586m^3$ and $7,520m^3$, respectively. The LiDAR data allowed comprehensive investigation of the morphological features present along the sliding surface and in the deposit areas.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.2
• /
• pp.133-138
• /
• 2016

This paper describes the track initiation and target-tracking filter for ship tracking in a marine environment by using Light Detection And Ranging (LiDAR). LiDAR with three-dimensional scanning capability is more useful for target tracking in the short to medium range compared to RADAR. LiDAR has rotating multi-beams that return point clouds reflected from targets. Through preprocessing the cluster of the point cloud, the center point can be obtained from the cloud. Target tracking is carried out by using the center points of targets. The track of the target is initiated by investigating the normalized distance between the center points and connecting the points. The regular track obtained from the track initiation can be maintained by the target-tracking filter, which is commonly used in radar target tracking. The target-tracking filter is constructed to track a maneuvering target in a cluttered environment. The target-tracking algorithm including track initiation is experimentally evaluated in a sea-trial test with several boats.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.36 no.5
• /
• pp.355-369
• /
• 2018

LiDAR (Light Detection And Ranging) strip adjustment is process to improve geo-referencing of the ALS (Airborne Laser Scanner) strips that leads to seamless LiDAR data. Multiple strips are required to collect data over the large areas, thus the strips are overlapped in order to ensure data continuity. The LSA (LiDAR Strip Adjustment) consists of identifying corresponding features and minimizing discrepancies in the overlapping strips. The corresponding features are utilized as control features to estimate transformation parameters. This paper applied SURF (Speeded Up Robust Feature) to identify corresponding features. To improve determination of the corresponding feature, false matching points were removed by applying three schemes: (1) minimizing distance of the SURF feature vectors, (2) selecting reliable matching feature with high cross-correlation, and (3) reflecting geometric characteristics of the matching pattern. In the strip adjustment procedure, corresponding points having large residuals were removed iteratively that could achieve improvement of accuracy of the LSA eventually. Only a few iterations were required to reach reasonably high accuracy. The experiments with simulated and real data show that the proposed method is practical and effective to airborne LSA. At least 80 % accuracy improvement was achieved in terms of RMSE (Root Mean Square Error) after applying the proposed schemes.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2005.11a
• /
• pp.81-104
• /
• 2005

Accurate 3D models in urban areas are essential for a variety of applications, such as virtual visualization, CIS, and mobile communications. LiDAR(Light Detection and Ranging) is a relatively new technology for directly obtaining 3D points. Because Manual 3D data reconstruction from LiDAR data is very costly and time consuming, many researchs is focused on the automatic extraction of the useful data. In this paper, we classified ground and non-ground points data from LiDAR data by using filtering, and we reconstructed the DTM(Digital Terrain Model) using ground points data, buildings using nonground points data. After the reconstruction, we assessed the accuracy of the DTM and buildings. As a result of, DTM from LiDAR data were 0.16m and 0.59m in high raised apartments areas and low house areas respectively, and buildings were matched with the accuracy of a l/5,000 digital map.

• ETRI Journal
• /
• v.40 no.4
• /
• pp.522-530
• /
• 2018

In this study, we developed a robotic walker that actively controls its speed and direction of movement according to the user's gait intention. Sensor fusion between a low-cost light detection and ranging (LiDAR) sensor and inertia measurement units (IMUs) helps determine the user's gait intention. The LiDAR determines the walking direction by detecting both knees, and the IMUs attached on each foot obtain the angular rate of the gait. The user's gait intention is given as the directional angle and the speed of movement. The two motors in the robotic walker are controlled with these two variables, which represent the user's gait intention. The estimated direction angle is verified by comparison with a Kinect sensor that detects the centroid trajectory of both the user's feet. We validated the robotic walker with an experiment by controlling it using the estimated gait intention.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.11
• /
• pp.13-18
• /
• 2019

The DEM(Digital Elevation Model) is a three-dimensional spatial information that stores the height of the terrain as a numerical value. This means the elevation of the terrain not including the vegetation and the artifacts. The DEM is used in various fields, such as 3D visualization of the terrain, slope, and incense analysis, and calculation of the quantity of construction work. Recently, many studies related to the construction of 3D geospatial information have been conducted, but research related to DEM generation is insufficient. Therefore, in this study, a DEM was constructed using a MMS (Mobile Mapping System), UAV image, and UAV LiDAR (Light Detection And Ranging), and the accuracy evaluation of each result was performed. As a result, the accuracy of the DEM generated by MMS and UAV LiDAR was within ± 4.1cm, and the accuracy of the DEM using the UAV image was ± 8.5cm. The characteristics of MMS, UAV image, and UAV LiDAR are presented through a comparison of data processing and results. The DEM construction using MMS and UAV can be applied to various fields, such as an analysis and visualization of the terrain, collection of basic data for construction work, and service using spatial information. Moreover, the efficiency of the related work can be improved greatly.

• Spatial Information Research
• /
• v.16 no.3
• /
• pp.373-383
• /
• 2008

LiDAR, unlike satellite imagery and aerial photographs, which provides irregularly distributed three-dimensional coordinates of ground surface, enables three-dimensional modeling. In this study, urban area was classified based on 3D information collected by LiDAR. Morphological and spatial properties are determined by the ratio of ground and non-ground point that are estimated with the number of ground reflected point data of LiDAR raw data. With this information, the residential and forest area could be classified in terms of height and density of trees. The intensity of the signal is distinguished by a statistical method, Jenk's Natural Break. Vegetative area (high or low density) and non-vegetative area (high or low density) are classified with reflective ratio of ground surface.