• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.3
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• pp.235-242
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• 2002

This study is focused on the analysis of road extracting accuracy from the high resolution IKONOS satellite image data. A geometric correction of the image is performed using the RFM and interpretation with the screen digitizing is also performed for extracting the roads information. For the evaluation of road extracting accuracy, the road locations and the road widths are compared with the national digital map. The comparison results shows that the road boundary and the size of road width are able to extract with the geometric accuracy of $\pm$3.4m and $\pm$1.1m.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.1
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• pp.15-16
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• 1997

공작기계의 기본은 POSITION ACCURACY이다. XYZ 3축 직선운동 복합으로 거의 모든 형태를 표현할 수 있다. 3축 각각의 직선운동만 정확하면 정밀한 절삭가공이 된다는 뜻이다. 직선운동의 정확도를 기하기 위하여 볼스크류 를 쓰고 기아도 그라인딩을 한다. POSITION ACCURACY는 NC-CONTROLLER를 사용하면서 극대로 증가한다. 이것이 공작기계의 원리이다. 항공기의 조립은 공작기계가 갖고 있는 X Y Z 3축의 복합만으로는 STROKE가 짧아 표현할 수 없는 상당히 긴 STROKE이면서 공간의 위치가 요구된다. 이를 찾는 방법은 지극히 정밀한 LENS를 동원한 OPTICAL TOOL을 쓰는 것이 가장 용이하다. 항곡부품의 위치도 OPTICAL TOOL을 사용하는 것이 가장 용이하다. 물론 건축용 OPTICAL TOOL보다 항공용은 몇배 더 정밀하다.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.2 s.16
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• pp.47-60
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• 2000

On this study, Among the classification methods of land cover using satellite imagery, we compared the classification accuracy of Neural Network Classifier and that of Maximum Likelihood Classifier which has the characteristics of parametric and non-parametric classification method. In the assessment of classification accuracy, we analyzed the classification accuracy about testing area as well as training area that many analysts use generally when assess the classification accuracy. As a result, Neural Network Classifier is superior to Maximum Likelihood Classifier as much as 3% in the classification of training data. When ground reference data is used, we could get poor result from both of classification methods, but we could reach conclusion that the classification result of Neural Network Classifier is superior to the classification result of Maximum Likelihood Classifier as much as 10%.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.11
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• pp.1031-1038
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• 2016

In the UAVs, the position error of the inertial navigation system is constantly increased when global positioning system goes wrong due to interference. It makes impossible to ensure mission and flight safety. If the data-link system provide the position of the UAV for inertial navigation system periodically, then the UAV may operate normally under malfunction of the global positioning system. In this paper, we introduce an algorithm for estimating the position of the UAV using the monopulse tracking and distance measurement of the line-of-sight data-link system. Also, we propose a method to improve the performance of position estimation. And we assured ourselves that this method can be applied in the UAVs.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.1
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• pp.65-72
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• 2010

In this study, the performances of a medium grade IMU which is aimed for Mobile Mapping System and a low grade IMU for pedestrian navigation are analyzed through simulations under GPS signal blockage. In addition, an analysis on the accuracy improvement of barometer, electronic compass, or multi-sensor(combination of barometer and electronic compass) to correct medium grade or low grade IMU errors in the situation of GPS signal blockage is performed. With the medium grade IMU, the three dimensional positioning error from INS exceeds the demanded accuracy of 5m when the block time is over 30 seconds. When we correct IMU with barometer, compass, or multi-sensor, however, the demanded accuracy is maintained up to 60 seconds. In addition, barometer is more effective than the electronic compass when they are combined. In case of low grade IMU like MEMS IMU, the three dimensional positioning error from INS exceeds the demanded accuracy of 20m when the block time is over 15 seconds. When we correct INS with barometer, compass, or multi-sensor, however, the demanded accuracy is maintained up to 15 seconds in simulation results. On the contrary to medium grade IMU, electronic compass is more effective than the barometer in case of low velocity such as pedestrian navigation. It is expected that the analysis suggested a method to decrease position or attitude error using aided sensor integration when MMS or pedestrian navigation is operated under 1he environment of GPS signal blockage.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.126-129
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• 2018

기존 자율주행자동차의 영상 센서 시스템이 표지판에 약간의 부착물만 붙어도 인식이 되지 않는 점을 고려해 Semi-Passive RFID(Radio-Frequency Identification)기술을 이용한다. 각 표지판마다 소스코드를 설정한 후 RFID 태그를 부착하고 자동차의 룸미러 뒤쪽 중앙에 RFID 리더기를 부착해 원거리에서 태그와 리더기의 작용을 통해 영상 센서 시스템의 취약점을 보완해 오류를 줄인다. 태그의 건전지를 대체하여 M24LR16E칩을 적용한다. 이 칩은 기존에 낭비되는 전파와 폐열, 움직임에 따라 발생하는 동작 에너지의 미세한 에너지를 모아 메모리칩을 구동한다. 또한 GPS를 이용한 위치정보 획득 및 지리적 변화의 낮은 정확도를 보완해 도시 인프라에 부착된 RFID를 제안하여 이를 이용한 위치정보 획득과 처리방안의 문제점도 해결한다.

• Journal of KIISE:Databases
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• v.33 no.3
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• pp.261-270
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• 2006

Positional data of moving objects can be regularly sampled in order to minimize the cost of data collection in LBS. Since position data which are regularly sampled cannot include the changes of position occurred between sampling periods, sampled position data differ from the data predicted by a time parameterized linear function. Uncertain position data caused by these differences make the accuracy of the range queries for present positions diminish in the TPR tree. In this paper, we propose the uncertainty region to handle the range queries for uncertain position data. The uncertainty region is defined by the position data predicted by the time parameterized linear function and the estimated uncertainty error. We also present the weighted recent uncertainty error policy and the kalman filter policy to estimate the uncertainty error. For performance test, the query processor based by the uncertainty region is implemented in the TPR tree. The experiments show that the Proposed query processing methods are more accurate than the existing method by 15%.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.3
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• pp.263-273
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• 2008

The precision of sensors' position is particularly important in the application of road extraction or digital map generation. In general, the various ranging solution systems such as GPS, Total Station, and Laser Ranger have been employed for the position of the sensor. Basically, the ranging solution system has problems that the signal may be blocked or degraded by various environmental circumstances and has low temporal resolution. To overcome those limitations a IMU/range integrated system could be introduced. In this paper, after pointing out the limitation of extended Kalman filter which has been used for workhorse in navigation and geodetic community, the two sampling based nonlinear filters which are sigma point Kalman filter using nonlinear transformation and carefully chosen sigma points and particle filter using the non-gaussian assumption are implemented and compared with extended Kalman filter in a simulation test. For the ranging solution system, the GPS and Total station was selected and the three levels of IMUs(IMU400C, HG1700, LN100) are chosen for the simulation. For all ranging solution system and IMUs the sampling based nonlinear filter yield improved position result and it is more noticeable that the superiority of nonlinear filter in low temporal resolution such as 5 sec. Therefore, it is recommended to apply non-linear filter to determine the sensor's position with low degree position sensors.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.705-714
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• 2019

Precision positioning is necessary for various use of high-resolution UAV images. Basically, GCP is used for this purpose, but in case of emergency situations or difficulty in selecting GCPs, the data shall be obtained without GCPs. This study proposed a method of improving positional accuracy for x, y coordinate of UAV based 3 dimensional point cloud data generated without GCPs. Road vector file by the public data (Open Data Portal) was used as reference data for improving location accuracy. The geometric correction of the 2 dimensional ortho-mosaic image was first performed and the transform matrix produced in this process was adopted to apply to the 3 dimensional point cloud data. The straight distance difference of 34.54 m before the correction was reduced to 1.21 m after the correction. By confirming that it is possible to improve the location accuracy of UAV images acquired without GCPs, it is expected to expand the scope of use of 3 dimensional spatial objects generated from point cloud by enabling connection and compatibility with other spatial information data.

• 한국지형공간정보학회:학술대회논문집
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• 1997.12a
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• pp.17-25
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• 1997

스트립 방식으로 촬영된 위성영상의 기하학적 모델링시에는 일반적으로 단위모델별로 위성영상에서 식별이 용이한 지점을 지상기준점으로 선정하여 영상에서의 좌표를 해석도화기나 컴퓨터 화면상에서 획득하고, 그에 대응하는 지상좌표는 현지측량이나 기본지도로부터 얻는다. 현지측량방법의 경우에는 정확하고 신뢰성있는 좌표를 제공해 줄 수 있는 반면에 현지로의 접근이 용이해야 하고 비용과 시간이 많이 소요되며, 소축척의 기본지도상에서 직접 독취하는 경우에는 지도자체에 내재된 오차의 정도가 불명확하고 지상기준점으로 선정한 지점이 명확히 표기되어 있지 않은 경우가 있으므로 영상좌표 측정시에 오차의 유발 가능성이 크다. 특히 영상 해상도 $5{\sim}10m$의 연속된 화상을 3차원 기하학적 모델링을 수행하기 위하여 현지측량에 의해 지상기준점을 획득하는 방법은 경제성 및 효율성 측면에서 불리하다. 따라서 본 연구에서는 삼각점 조서를 이용하여 연속촬영된 SPOT 위성영상을 스트립 단위로 모델링하여 영상해상도 수준의 위치정확도를 얻을 수 있는 연속화상 모델링기법을 제시하였다.