• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.135-138
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• 2020

This paper suggests a method of automatic clustering to search of relevant digital photos using geo-coded information. The provided scheme labels photo images with their corresponding global positioning system coordinates and date/time at the moment of capture, and the labels are used as clustering metadata of the images when they are in the use of retrieval. Experimental results show that geo-location information can improve the accuracy of image retrieval, and the information embedded within the images are effective and precise on the image clustering.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.15-26
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• 2022

3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.11-21
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• 2020

This paper deals with the impact of a GNSS (Global Navigation Satellite System) antenna radome on the PCV (Phase Center Variations) and the estimated kinematic coordinates. For the Trimble and Leica antennas, specially set up CORS (Continuously Operation Reference Stations) in Korea, the PCC (Phase Center Corrections) were calculated and compared for NONE, SCIS, SCIT, and TZGD radome from the PCV model published by the IGS (International GNSS Services). The results revealed that the PCC differences compared to the NONE were limited to about 1mm in the horizontal component while those of the vertical direction ranged from a few millimeters to a maximum of 7mm. Among the radomes of which PCV were compared, the SCIT had the most significant influence on the vertical component, and its GPS (Global Positioning System) L2 and L2 PCC (Phase Center Corrections) had opposite direction. As a result of comparing the kinematic coordinates estimated by the baseline processing of 7 CORSs with an application of the PCV models of the various radomes, the SCIS which was actually installed at CORS in Korea showed 3.4mm bias, the most substantial impact on the ellipsoidal height estimation whereas the SCIT model resulted in relatively small biases.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.6
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• pp.307-315
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• 2000

For the autonomy of a mobile robot it is first needed to know its position and orientation. Various methods of estimating the position of a robot have been developed. However, it is still difficult to localize the robot without any initial position or orientation. In this paper we present the method how to make the colored map and how to calculate the position and direction of a robot using the angle data of an omnidirectional image. The wall of the map is rendered with the corresponding color images and the color histograms of images and the coordinates of feature points are stored in the map. Then a mobile robot gets the color omnidirectional image at arbitrary position and orientation, segments it and recognizes objects by multiple color indexing. Using the information of recognized objects robot can have enough feature points and localize itself.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.6
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• pp.571-578
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• 2012

In the paper, we proposed a ring type structured light image based embedded ranging sensor for a mobile robot. Since the proposed ranging sensor obtains omnidirectional object distance, it is useful for autonomous navigation of a mobile robot. By matching the local omnidirectional distance map with a given global object map, it is possible to get position and heading angle of mobile robot in the global coordinates. Experiments for matching and navigation were carried out to verify the performance of the proposed ranging sensor.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.2
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• pp.132-138
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• 2009

In this paper, control of an omni-directional mobile robot is presented. Relying on encoder measurements to define the azimuth angle yields the dead-reckoned situation which the robot fails in localization. The azimuth angle error due to dead-reckoning is compensated and corrected by the magnetic compass sensor. Noise from the magnetic compass sensor has been filtered out. Kinematics and dynamics of the omni-directional mobile robot are derived based on the global coordinates and used for simulation studies. Experimental studies are also conducted to show the correction by the magnetic compass sensor.

• Journal of Astronomy and Space Sciences
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• v.35 no.4
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• pp.287-293
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• 2018

This paper presents a satellite relative navigation strategy for formation flying, which chooses an appropriate navigation algorithm according to the operating environment. Not only global positioning system (GPS) measurements, but laser measurements can also be utilized to determine the relative positions of satellites. Laser data is used solely or together with GPS measurements. Numerical simulations were conducted to compare the relative navigation algorithm using only laser data and laser data combined with GPS data. If an accurate direction of laser pointing is estimated, the relative position of satellites can be determined using only laser measurements. If not, the combined algorithm has better performance, and is irrelevant to the precision of the relative angle data between two satellites in spherical coordinates. Within 10 km relative distance between satellites, relative navigation using double difference GPS data makes more precise relative position estimation results. If the simulation results are applied to the relative navigation strategy, the proper algorithm can be chosen, and the relative position of satellites can be estimated precisely in changing mission environments.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.229-230
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• 2022

본 논문에서는 CNN Object Detection과 더불어 3D 카메라 기반 디지털 좌표 인식 기술을 제안한다. 이 기술은 3D Depth Camera인 Intel 사의 Realsense D455를 이용해 대상을 감지하고 분류하며 대상의 위치를 파악한다. 또한 이 기술은 기존의 Depth Camera 내장 거리와는 다르게 좌표를 인식하여 좌표간의 거리까지 계산이 가능하다. 또한 Tensorflow SSD 구조와의 메모리 공유를 통해 시스템의 자원 낭비를 줄이며, 속도를 높이는 멀티쓰레드를 탑재했다. 본 기술을 통해 좌표간의 거리를 계산함으로써 스포츠, 심리, 놀이, 산업 등 다양한 환경에서 활용할 수 있다.

• Journal of the Korean earth science society
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• v.39 no.1
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• pp.46-52
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• 2018

This paper is to study the development of three dimensional Digital Surface Model (DSM) using photogrammetry technique based on self-developed Drone (Unmanned Aerial Vehicle (UAV)). To develop DSM, we selected a study area in Jeju island and took 24 pictures from the drone. The three dimensional coordinates of the photos were made by Differential Global Positioning System (DGPS) surveying with 10 ground control points (GCP). From the calculated three dimensional coordinates, we produced orthographic image and DSM. The accuracy of DSM was calculated using three GCPs. The average accuracy of X and Y was from 8.8 to 14.7 cm, and the accuracy of Z was 0.8 to 12.4 cm. The accuracy was less than the reference accuracy of 1/1,000 digital map provided by National Geographic Information Institute (NGII). From the results, we found that the self-developed drone and the photogrammetry technique are a useful tool to make DSM and digital map of Jeju.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.2
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• pp.15-24
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• 2016

GPS(Global Positioning System) location tracking is a method for taking the precise coordinates after the coordinates are obtained by a GPS receiver, and displaying them on the map. In this paper with WAVE(Wireless Access for Vehicular Environment) simulation, we show that various services such as vehicle tracking service, real-time road conditions service and logistics can go tracking service, control and operation services according to the vehicle position and the traveling direction by using the GPS position data. A vehicle tracking system using GPS is automatically able to manage multiple RCP when exchanging data between RMA and the RCP, and it provides rapid requests and responses. To verify that multiple sessions between RMA and RM, as well as multiple sessions between RMA and RCP are able to be implemented, we take RMA as a RCP application on an OBU, until the RMA is receiving data response from corresponding RM. As a result of this experiment, we show that the response speeds of single session between RMA and RM using 1, 2, 3, and 4 kbyte unit data are similar, 62.32ms, 62.65ms, 63.02ms, and 63.48ms, respectively. Likewise, those of 128 muliple sessions using 1, 2, 3, and 4 kbyte unit data are not much more time difference, 298.08ms, 302.21ms, 322.85ms, and 329.62ms, respectively.