• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.135-143
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• 2009

Integrating the Global Positioning System (GPS) and Inertial Navigation System (INS) sensor technologies using the precise GPS Carrier phase measurements is a methodology that has been widely applied in those application fields requiring accurate and reliable positioning and attitude determination; ranging from 'kinematic geodesy', to mobile mapping and imaging, to precise navigation. However, such integrated system may not fulfil the demanding performance requirements when the baseline length between reference and mobil user GPS receiver is grater than a few tens of kilometers. This is because their positioning/attitude determination is still very dependent on the errors of the GPS observations, so-called "baseline dependent errors". This limitation can be remedied by the integration of GPS and INS sensors, using multiple reference stations. Hence, in order to derive the GPS distance dependent errors, this research proposes measurement processing algorithms for multiple reference stations, such as a reference station ambiguity resolution procedure using linear combination techniques, a error estimation based on Kalman filter and a error interpolation. In addition, all the algorithms are evaluated by processing real observations and results are summarized in this paper.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.3
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• pp.293-301
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• 2011

The ionospheric delay is the largest error source in GPS positioning after the SA effect has been turned off in May, 2000. In this study, we used 44 permanent GPS stations being operated by National Geographic Information Institute (NGII) to estimate Total Electron Content (TEC) based on pseudorange measurements phase-leveled by a linear combination with carrier phases. The Differential Code Bias (DCB) of GPS satellites and receivers was estimated and applied for an accurate estimation of the TEC. To validate our estimates of DCB, changes of TEC values after DCB application were investigated. As a result, the RMS error went down by about an order of magnitude; from 35~45 to 3~4 TECU. After the DCB correction, ionospheric TEC maps were produced at a spatial resolution of $1^{\circ}{\times}1^{\circ}$. To analyze the effect of the number of sites used for map generation on the accuracy of TEC values, we tried 10, 20, 30, and 44 stations and the RMS error was computed with the Global Ionosphere Map as the truth. While the RMS error was 5.3 TECU when 10 sites are used, the error reduced to 3.9 TECU for the case of 44 stations.