• Journal of Advanced Navigation Technology
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• v.14 no.2
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• pp.272-277
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• 2010

AR(Augmented Reality) needs medium between real and virtual, world, and recognition techniques are necessary to track an object continuously. Optical tracking using marker is mainly used, but it takes time and is inconvenient to attach marker onto the target objects. Therefore, many researchers try to develop markerless tracking techniques nowaday. In this paper, we extract features and 3D position from 3D objects and suggest realtime tracking based on these features and positions, which do not use just coplanar features and 2D position. We extract features using SURF, get rotation matrix and translation vector of 3D object using POSIT with these features and track the object in real time. If the extracted features are nor enough and it fail to track the object, then new features are extracted and re-matched to recover the tracking. Also, we get rotation in matrix and translation vector of 3D object using POSIT and track the object in real time.

• Journal of Ocean Engineering and Technology
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• v.31 no.2
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• pp.164-169
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• 2017

Studies were performed on an ARS using SDINS's RLG and the geomatic sensor. To develop the ARS, experiments were performed to determine the characteristics of the RLG and geomatic sensor. Based on the results, to reduce the angular position errors of the RLG, which accumulate from the angular velocity data, an algorithm was studied that uses the Extended Kalman filter (EKF) to compensate the RLG data and geomatic sensor data. To verify the performance of the developed algorithm for reducing the cumulative angular errors, experiments that included the developed EKF were performed. Through these, it was shown that a drastic reduction in the angular errors of the RLG were achieved.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.29 no.1
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• pp.377-382
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• 2005

This paper deals with productivity improvement of stevedoring system by pooling opertaions of transport vehicle at automated container terminal. Usually, in traditional container terminals, grouping operations of transport vehicle are applied for container crane because vehicle routing path is simple and vehicle assignment is easy. But this static assignment(SA) operation that arrsign vehicles to container crane ar apron reduces flexibility of vehicles. Therefore, This paper presented 4 dynamic assignment(DA) method to improve efficiency of vehicles. These 4 dynamic assignment method consider present situations of container crane such as sequence(Se), queue time(Qt), productivity(Pr), numeric of vehicle assignment(Nv), numeric of buffer(Nb) at vehicles assignment. At the results, dynamic assignment operation to consider Qt, Nv, Nb is most efficient and by next time, dynamic assignment operation to consider Se is superior more than static assignment operation. but, dynamic assignment operation to consider Pr or Qt of container crane only is inefficient than static assignment operation.

• Journal of Korean Society of Transportation
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• v.34 no.4
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• pp.341-353
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• 2016

Highway is mainly in charge of middle-long distance of vehicular travel. Trip length has shown a growing trend due to increased commute distances by the relocation of public agencies. For this reason, the proportion of driver-driven accidents, caused by their fatigue or sleepiness, are very high on highways. However, existing studies related to accident prediction have mainly considered external factors, such as road conditions, environmental factors and vehicle factors, without driving behavior. In this study, we suggested an accident index (FDR, Fatigued Driving Rate) based on traffic behavior using large-scale Car Navigation path data, and exlpored the relationship between FDR and traffic accidents. As a result, FDR and traffic accidents showed a high correlation. This confirmed the need for a paradigm shift (from facilities to travel behavior) in traffic accident prediction studies. FDR proposed in this study will be utilized in a variety of fields. For example, in providing information to prevent traffic accidents (sleepiness, reckless driving, etc) in advance, utilization of core technologies in highway safety diagnostics, selection of priority location of rest areas and shelter, and selection of attraction methods (rumble strips, grooving) for attention for fatigued sections.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.195-199
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• 2009

Augmented reality is a field of computer research which deals with the combination of real-world and computer-generated data, where computer graphics objects are blended into real footage in real time and it has tremendous potential in visualizing geospatial information. However, to utilize augmented reality in mobile system, many researches have undergone with GPS or marker based approaches. Localization and tracking of current position become more complex problem when it is used in indoor environments. Many proposed RF based tracking and localization. However, it does cause deployment problems of large sensors and readers. In this paper, we present a noble markerless AR approach for indoor navigation system only using a camera. We will apply this work to mobile seamless indoor/outdoor u-GIS system.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.11
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• pp.997-1003
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• 2012

GNSS and ARS are the most common sensors in low-end UAVs. However, these sensors are vulnerable to built-in errors and cannot measure the body heading independently. The GNSS/INS cannot fully compensate the IMU errors in initial alignment process and rectilinear flights. For an unmanned helicopter, a magnetometer can be more useful than any other sensors to obtain heading information. However, the electric motor which drives small helicopter UAV keeps the magnetometer from reading the pure magnetotelluric vector. This paper shows the effects of electric motor on the magnetometer readings, and presents a method to compensate the effects. The results are verified with flight test data. The simulation and experimental results in this paper proves that aiding GNSS/INS with magnetometer increases observability and improves accuracy.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.10
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• pp.52-60
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• 2010

It is generally important to get a precise position information for autonomous unmanned vehicle(AUV) to run safely. For getting the position of AUV, the GPS has been using to navigation in a vehicle. Though it is useful to finding a position, it is difficult to precisely control a trajectory of the AUV due to large measuring error which may reach over 10 meters. Therefore to apply AUV it needs to compensate for the error. This paper proposes a method to more precisely localize AUV using three low-cost differential global positioning systems (DGPS). The distance errors between each DGPS are minimized as using the least square method (LSM) and the Kalman filter to eliminate a Gaussian white noise. The selected DGPS is cheaper and easier to set up than the RTK-GPS. It is also more precise than the general GPS. The proposed method can compensate the relatively position error according to stationary and moving distance of the AUV. For evaluating the algorithm by simulation, the DGPS signal with the Gaussian white noise to any points is generated by the AR model and compared with the measurement signal. It is confirmed that the proposed method can effectively compensate the position error as comparing with the measurement signal. The compensated position signal can be used to localize and control the AUV in the road.