• Journal of Korean Society for Geospatial Information Science
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• v.18 no.4
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• pp.101-107
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• 2010

The aim of this paper is to come up with a methodology of constructing an efficient model for multiple representations which can manage and reconcile real-time data about large-scale roads in Vector Domain. In other words, we suggested framework based on a bottom-up approach, which is allowed to integrate data from the network of the lowest level sequentially and perform automated matching in order to produce variable-scale map. Finally, we applied designed multi-LoD model to in-vehicle application.

• Journal of Digital Contents Society
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• v.11 no.4
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• pp.545-552
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• 2010

To a developing country, the advancement of economic status through informatization is an important element contributing to the local society, nation, and to the global world. This research paper is a study on application of Korea's local informatization successful model to Indonesia. Through activated interaction between enterprises and policy makers from Korea and Indonesia, the research paper seeks to create research based network and provide opportunities of information access and business matching to local informatized and e-business enterprises. In research adopted regions, Indonesia's pekalongan region where infra is settled to certain extent by city development project is selected. Therefore, investigating on pekalongan's current geographical, humanistic status quo, and infra, the paper suggest a road map to customized pekalongan's local informatization based on Korean local informatizing successful model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.8B
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• pp.1490-1499
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• 1999

This paper proposes a new handwritten numeral recognition method using numeral shape grasps and FSOM (Fuzzy Self-Organizing Map). The proposed algorithm is based on the idea that numeral input data with similar shapes are classified into the same class. Shapes of numeral data are created using lines of external-contact and the class of numeral data is determined by template matching of the shapes. Each class of numeral data has FSOM and feature extraction method, respectively. In this paper, we divide the numeral database into the 16 classes. The divided FSOM model allows not only an independent learning phase of SOM but also step-by-step learning. Experiments using Concordia University handwritten numeral database proved that the proposed algorithm is effective to improve recognition accuracy.

• Journal of Sensor Science and Technology
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• v.14 no.2
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• pp.116-124
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• 2005

In this paper range measurement systems are designed using an ultrasonic sensor and a camera. An ultrasonic sensor provides the range measurement to a target quickly and simply but its low resolution is a disadvantage. We tackle this problem by employing a camera. Instead using a stereoscopic sensor, which is widely used for 3D sensing but requires a computationally intensive stereo matching, the range is measured by focusing and structured lighting. In focusing a straightforward focusing measure named as MMDH(min-max difference in histogram) is proposed and compared with existing techniques. In the method of structure lighting, light stripes projected by a beam projector are used. Compared to those using a laser beam projector, the designed system can be constructed easily in a low-budget. The system equation is derived by analysing the sensor geometry. A sensing scenario using the systems designed is in two steps. First, when better accuracy is required, measurements by ultrasonic sensing and focusing of a camera are fused by MLE(maximum likelihood estimation). Second, when the target is in a range of particular interest, a range map of the target scene is obtained by using structured lighting technique. The systems designed showed measurement accuracy up to 0.3[mm] approximately in experiments.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.7
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• pp.107-116
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• 2014

This paper presents camera-based road surface marking detection methods suited to sensor fusion-based positioning system that consists of low-cost GPS (Global Positioning System), INS (Inertial Navigation System), EDM (Extended Digital Map), and vision system. The proposed vision system consists of two parts: lane marking detection and RSM (Road Surface Marking) detection. The lane marking detection provides ROIs (Region of Interest) that are highly likely to contain RSM. The RSM detection generates candidates in the regions and classifies their types. The proposed system focuses on detecting RSM without false detections and performing real time operation. In order to ensure real time operation, the gating varies for lane marking detection and changes detection methods according to the FSM (Finite State Machine) about the driving situation. Also, a single template matching is used to extract features for both lane marking detection and RSM detection, and it is efficiently implemented by horizontal integral image. Further, multiple step verification is performed to minimize false detections.

• The Journal of Korea Robotics Society
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• v.13 no.4
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• pp.230-236
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• 2018

3D depth perception has played an important role in robotics, and many sensory methods have also proposed for it. As a photodetector for 3D sensing, single photon avalanche diode (SPAD) is suggested due to sensitivity and accuracy. We have researched for applying a SPAD chip in our fusion system of time-of-fight (ToF) sensor and stereo camera. Our goal is to upsample of SPAD resolution using RGB stereo camera. Currently, we have 64 x 32 resolution SPAD ToF Sensor, even though there are higher resolution depth sensors such as Kinect V2 and Cube-Eye. This may be a weak point of our system, however we exploit this gap using a transition of idea. A convolution neural network (CNN) is designed to upsample our low resolution depth map using the data of the higher resolution depth as label data. Then, the upsampled depth data using CNN and stereo camera depth data are fused using semi-global matching (SGM) algorithm. We proposed simplified fusion method created for the embedded system.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.69.3-69.3
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• 2019

The CHIMPS2 survey is to extend the JCMT HARP $^{13}CO/C^{18}O$ J=3-2 Inner Milky-Way Plane Survey (CHIMPS) and the ${12}^CO$ J=3-2 survey (COHRS) into the inner Galactic Plane, the Central Molecular Zone (CMZ), and a section of the Outer Plane. When combined with the complementary $^{12}CO/^{13}CO/C^{18}O$ J=1-0 survey at the Nobeyama 45m (FUGIN) at matching 15" resolution and sensitivity, and other current CO surveys, the results will provide a complete set of transition data with which to calculate accurate column densities, gas temperatures and turbulent Mach numbers. These will be used to: analyze molecular cloud properties across a range of Galactic environments; map the star-formation efficiency (SFE) and dense-gas mass fraction (DGMF) in molecular gas as a function of position in the Galaxy and its relation to the nature of the turbulence within molecular clouds; determine Galactic structure as traced by molecular gas and star formation; constrain cloud-formation models; study the relationship of filaments to star formation; test current models of the gas kinematics and stability in the Galactic center region and the flow of gas from the disc. It will also provide an invaluable legacy data set for JCMT that will not be superseded for several decades. In this poster, we will present the current status of the CHIMPS2.

• 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.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.179-188
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• 2021

Construction monitoring is one of the key modules in smart construction. Unlike structured urban environment, construction site mapping is challenging due to the characteristics of an unstructured environment. For example, irregular feature points and matching prohibit creating a map for management. To tackle this issue, we propose a system for data acquisition in unstructured environment and a framework for Intensity and Ambient Enhanced Lidar Inertial Odometry via Smoothing and Mapping, IA-LIO-SAM, that achieves highly accurate robot trajectories and mapping. IA-LIO-SAM utilizes a factor graph same as Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping (LIO-SAM). Enhancing the existing LIO-SAM, IA-LIO-SAM leverages point's intensity and ambient value to remove unnecessary feature points. These additional values also perform as a new factor of the K-Nearest Neighbor algorithm (KNN), allowing accurate comparisons between stored points and scanned points. The performance was verified in three different environments and compared with LIO-SAM.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.149-158
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• 2024

This paper presents a robust autonomous navigation and reconnaissance system for tracked robots, designed to handle complex multi-floor indoor environments with stairs. We introduce a localization algorithm that adjusts scan matching parameters to robustly estimate positions and create maps in environments with scarce features, such as narrow rooms and staircases. Our system also features a path planning algorithm that calculates distance costs from surrounding obstacles, integrated with a specialized PID controller tuned to the robot's differential kinematics for collision-free navigation in confined spaces. The perception module leverages multi-image fusion and camera-LiDAR fusion to accurately detect and map the 3D positions of objects around the robot in real time. Through practical tests in real settings, we have verified that our system performs reliably. Based on this reliability, we expect that our research team's autonomous reconnaissance system will be practically utilized in actual disaster situations and environments that are difficult for humans to access, thereby making a significant contribution.