• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.1036-1043
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• 2022

This paper discusses about the development of a visual sensor that can be installed in an automatic mooring device to detect the berthing condition of a vessel. Despite controlling the ship's speed and confirming its location to prevent accidents while berthing a vessel, ship collision occurs at the pier every year, causing great economic and environmental damage. Therefore, it is important to develop a visual system that can quickly obtain the information on the speed and location of the vessel to ensure safety of the berthing vessel. In this study, a visual sensor was developed to observe a ship through an image while berthing, and to properly check the ship's status according to the surrounding environment. To obtain the adequacy of the visual sensor to be developed, the sensor characteristics were analyzed in terms of information provided from the existing sensors, that is, detection range, real-timeness, accuracy, and precision. Based on these analysis data, we developed a 3D visual module that can acquire information on objects in real time by conducting conceptual designs of LiDAR (Light Detection And Ranging) type 3D visual system, driving mechanism, and position and force controller for motion tilting system. Finally, performance evaluation of the control system and scan speed test were executed, and the effectiveness of the developed system was confirmed through experiments.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.3
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• pp.104-115
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• 2014

A levee is defined as an man-made structure protecting the areas from temporary flooding. This paper suggests a methodology for establishing the levee GIS database using the airborne topographic LiDAR(Light Detection and Ranging) data taken in the Nakdong river basins and the WAMIS(WAter Management Information System) information. First, the National Levee Database(NLD) established by the USACE(United States Army Corps Engineers) and the levee information tables established by the WAMIS are compared and analyzed. For extracting the levee information from the LiDAR data, the DSM(Digital Surface Model) is generated from the LiDAR point clouds by using the interpolation method. Then, the slope map is generated by calculating the maximum rates of elevation difference between each pixel of the DSM and its neighboring pixels. The slope classification method is employed to extract the levee component polygons such as the levee crown polygons and the levee slope polygons from the slope map. Then, the levee information database is established by integrating the attributes extracted from the identified levee crown and slope polygons with the information provided by the WAMIS. Finally, this paper discusses the advantages and limitations of the levee GIS database established by only using the LiDAR data and suggests a future work for improving the quality of the database.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.395-401
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• 2018

Recently, most of mobile devices are equipped with GNSS (Global Navigation Satellite System). When the GNSS signal is available, it is easy to obtain position information. However, GNSS is not suitable solution for indoor localization, since the signals are normally not reachable inside buildings. A wide varieties of technology have been developed as a solution for indoor localization such as Wi-Fi, beacons, and inertial sensor. With the increased sensor combinations in mobile devices, mobile devices also became feasible to provide a solution, which based on PDR (Pedestrian Dead Reckoning) method. In this study, we utilized the combination of three sensors equipped in mobile devices including accelerometer, digital compass, and gyroscope and applied three representative PDR methods. The proposed methods are done in three stages; step detection, step length estimation, and heading determination and the final indoor localization result was evaluated with terrestrial LiDAR (Light Detection And Ranging) data obtained in the same test site. By using terrestrial LiDAR data as reference ground truth for PDR in two differently designed experiments, the inaccuracy of PDR methods that could not be found by existing evaluation method could be revealed. The firstexperiment included extreme direction change and combined with similar pace size. Second experiment included smooth direction change and irregular step length. In using existing evaluation method which only checks traveled distance, The results of two experiments showed the mean percentage error of traveled distance estimation resulted from three different algorithms ranging from 0.028 % to 2.825% in the first experiment and 0.035% to 2.282% in second experiment, which makes it to be seen accurately estimated. However, by using the evaluation method utilizing terrestrial LiDAR data, the performance of PDR methods emerged to be inaccurate. In the firstexperiment, the RMSEs (Root Mean Square Errors) of x direction and y direction were 0.48 m and 0.41 m with combination of the best available algorithm. However, the RMSEs of x direction and y direction were 1.29 m and 3.13 m in the second experiment. The new evaluation result reveals that the PDR methods were not effective enough to find out exact pedestrian position information opposed to the result from existing evaluation method.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1307-1312
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• 2023

In this paper, we propose a hybrid camera system that combines cameras with different focal lengths and LiDAR (Light Detection and Ranging) sensors to address the core components of autonomous driving perception technology, which include object recognition and distance measurement. We extract objects within the scene and generate precise location and distance information for these objects using the proposed hybrid camera system. Initially, we employ the YOLO7 algorithm, widely utilized in the field of autonomous driving due to its advantages of fast computation, high accuracy, and real-time processing, for object recognition within the scene. Subsequently, we use multi-focal cameras to create depth maps to generate object positions and distance information. To enhance distance accuracy, we integrate the 3D distance information obtained from LiDAR sensors with the generated depth maps. In this paper, we introduce not only an autonomous vehicle platform capable of more accurately perceiving its surroundings during operation based on the proposed hybrid camera system, but also provide precise 3D spatial location and distance information. We anticipate that this will improve the safety and efficiency of autonomous vehicles.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.6
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• pp.625-635
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• 2014

Multiple strips are required for large area mapping using ALS (Airborne Laser Scanner) system. LiDAR (Light Detection And Ranging) data collected from the ALS system has discrepancies between strips due to systematic errors of on-board laser scanner and GPS/INS, inaccurate processing of the system calibration as well as boresight misalignments. Such discrepancies deteriorate the overall geometric quality of the end products such as DEM (Digital Elevation Model), building models, and digital maps. Therefore, strip adjustment for minimizing discrepancies between overlapping strips is one of the most essential tasks to create seamless point cloud data. This study implemented area-based matching (ABM) to determine conjugate features for computing 3D transformation parameters. ABM is a well-known method and easily implemented for this purpose. It is obvious that the exact same LiDAR points do not exist in the overlapping strips. Therefore, the term "conjugate point" means that the location of occurring maximum similarity within the overlapping strips. Coordinates of the conjugate locations were determined with sub-pixel accuracy. The major drawbacks of the ABM are sensitive to scale change and rotation. However, there is almost no scale change and the rotation angles are quite small between adjacent strips to apply AMB. Experimental results from this study using both simulated and real datasets demonstrate validity of the proposed scheme.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1135-1144
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• 2023

Many agricultural reservoirs in South Korea, constructed before 1970, have become aging facilities. The majority of small-scale reservoirs lack measurement systems to ascertain basic specifications and water levels, classifying them as unmeasured reservoirs. Furthermore, continuous sedimentation within the reservoirs and industrial development-induced water quality deterioration lead to reduced water supply capacity and changes in reservoir morphology. This study utilized Light Detection And Ranging (LiDAR) sensors, which provide elevation information and allow for the characterization of surface features, to construct high-resolution Digital Surface Model (DSM) and Digital Elevation Model (DEM) data of reservoir facilities. Additionally, bathymetric measurements based on multibeam echosounders were conducted to propose an updated approach for determining reservoir capacity. Drone-based LiDAR was employed to generate DSM and DEM data with a spatial resolution of 50 cm, enabling the display of elevations of hydraulic structures, such as embankments, spillways, and intake channels. Furthermore, using drone-based hyperspectral imagery, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were calculated to detect water bodies and verify differences from existing reservoir boundaries. The constructed high-resolution DEM data were integrated with bathymetric measurements to create underwater contour maps, which were used to generate a Triangulated Irregular Network (TIN). The TIN was utilized to calculate the inundation area and volume of the reservoir, yielding results highly consistent with basic specifications. Considering areas that were not surveyed due to underwater vegetation, it is anticipated that this data will be valuable for future updates of reservoir capacity information.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1095-1109
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• 2021

The technology used to recognize the location and surroundings of autonomous vehicles is called SLAM. SLAM standsfor Simultaneously Localization and Mapping and hasrecently been actively utilized in research on autonomous vehicles,starting with robotic research. Expensive GPS, INS, LiDAR, RADAR, and Wheel Odometry allow precise magnetic positioning and mapping in centimeters. However, if it can secure similar accuracy as using cheaper Cameras and GPS data, it will contribute to advancing the era of autonomous driving. In this paper, we present a method for converging monocular camera with RTK-enabled GPS data to perform RMSE 33.7 cm localization and mapping on the urban road.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.677-684
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• 2021

This paper presents a time-to-digital converter for measuring absolute time differences. The time-to-digital converter was designed and fabricated in 0.18-um CMOS technology and it can be applied to Light Detection and Ranging system which requires long time-cover range and 50ps time resolution. Since designed time-to-digital converter adopted the reference clock of 625MHz generated by phase locked loop, it could have absolute time resolution of 50ps after automatic calibration and its cover range was over than 800ns. The time-to-digital converter adopted a counter and chain delay lines for time measurement. The counter is used for coarse time measurement and chain delay lines are used for fine time measurement. From many times experiments, fabricated time-to-digital converter has 50 ps time resolution with maximum INL of 0.8 LSB and its power consumption is about 70 mW.

• Journal of Korean Society of Forest Science
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• v.100 no.3
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• pp.315-326
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• 2011

In recent years, domestic and international interests focus on climate change, and importance of forest as carbon sink have been also increased. Particularly REDD+ mechanism expanded from REDD (Reduced Emissions from Deforestation and Degradation) is expected to perform a new mechanism for reducing greenhouse gas in post 2012. To conduct this mechanism, countries which try to get a carbon credit have to certify effectiveness of their activities by MRV (Measuring, Reporting and Verification) system. This study analyzed the approaches for detecting land cover change and estimating carbon stock by remote sensing technology which is considered as the effective method to develop MRV system. The most appropriate remote sensing for detection of land cover change is optical medium resolution sensors and satellite SAR (Synthetic Aperture Radar) according to cost efficiency and uncertainty assessment. In case of estimating carbon stock, integration of low uncertainty techniques, airborne LiDAR (Light Detection and Ranging), SAR, and cost efficient techniques, optical medium resolution sensors and satellite SAR, could be more appropriate. However, due to absence of certificate authority, guideline, and standard of uncertainty, we should pay continuously our attention on international information flow and establish appropriate methods. Moreover, to apply monitoring system to developing countries, close collaboration and monitoring method reflected characteristics of each countries should be considered.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1527-1539
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• 2022

As coastal erosion of the east coast is accelerating, the need for scientific and quantitative coastal erosion monitoring technology for a wide area increases. The traditional method for observing changes in the coast was precision monitoring based on field surveys, but it can only be applied to a small area. The airborne bathymetric Light Detection And Ranging (LiDAR) system is a technology that enables economical surveying of coastal and seabed topography in a wide area. In particular, it has the advantage of constructing topographical data for the intertidal zone, which is a major area of interest for coastal erosion monitoring. In this study, time series analysis of coastal seabed topography acquired in Aug, 2021 and Mar. 2022 on the littoral cell GW36 in Gangwon was performed using the Seahawk Airborne Bathymetric LiDAR (ABL) system. We quantitatively monitored the topographical changes by measuring the baseline length, shoreline and Digital Terrain Model (DTM) changes. Through this, the effectiveness of the ABL surveying technique was confirmed in coastal erosion monitoring.