• Spatial Information Research
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• v.11 no.3
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• pp.291-299
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• 2003

The Mobile Mapping System collects geographic information through mounted sensors such as a pair of CCD camera, CPS, IMU(Inertial Measurement Unit) and Odometer at regular distance or time interval. The advantage of such system is to easy identification of positions and geographic informations of mobile objects in real time. Among many wireless communication ways for real-time positions and geographic information data from the mobile mapping system to the user such as PDA, wireless modem, cellophane, and web, the web is considered to be more stabile, effect and economic than any other methods. In this paper, a study on the web-based real-time mobile mapping platform to identify the user position is presented using the real-time NovAtel BDS.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.2
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• pp.109-117
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• 2018

The experimental results are presented of an autonomous flight algorithm of a foldable quadcopter with airdrop launching functions. A foldable wing structure enabled the quadcopter to be inserted into a rocket container with limited space. The foldable quadcopter was then separated from the rocket in the air. The flight pattern was tracked using a global positioning system (GPS) with various sensors, including an inertial measurement unit (IMU) module until a designated target position was reached. Extensive field tests were conducted through an international rocket competition, ARLISS 2017, which was held in Black Rock Desert, Nevada, USA. The flight trajectory record of the experiments is stored in electrically erasable programmable read-only memory (EEPROM) embedded in the main control unit. The flight record confirmed that the quadcopter successfully separated from the rocket, executed flight toward the target for a certain length of time, and stably landed on the ground.

• Journal of Ocean Engineering and Technology
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• v.17 no.4
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• pp.73-80
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• 2003

This paper presents considerations on the results of the rotating arm test, which was carried out for assessment of an hybrid navigation system for a semi-autonomous underwater vehicle. The navigation system consists of an inertial measurement unit(IMU), an ultra-short baseline(USBL) acoustic navigation sensor and a doppler velocity log(DVL) accompanying a magnetic compass. A navigational systemmodel is derived to include the scale effect and bias errors of the DVL, of which the state equation composed of the navigation states and sensor parameters are 25 in the order. The extended Kalman filter was used to propagate the error covariance, The rotating arm tests were carried out in the Ocean Engineering Basin of KRISO, to generate circular motion. The hybrid underwater navigation system shows good tracking performance against the circular planar motion. Additionally this paper checked the effects of the sampling ratio of the navigation system and the possibility of the dead reckoning with the DVL and the magnetic compass to estimate the position of the vehicle.

• Journal of Ocean Engineering and Technology
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• v.17 no.6
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• pp.83-90
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• 2003

This paper presents an underwater hybrid navigation system for a semi-autonomous underwater vehicle (SAUV). The navigation system consists of an inertial measurement unit (IMU), and a Doppler velocity log (DVL), accompanied by a magnetic compass. The errors of inertial measurement units increase with time, due to the bias errors of gyros and accelerometers. A navigational system model is derived, to include the scale effect and bias errors of the DVL, of which the state equation composed of the navigation states and sensor parameters is 20. The conventional extended Kalman filter was used to propagate the error covariance, update the measurement errors, and correct the state equation when the measurements are available. Simulation was performed with the 6-d.o,f equations of motion of SAUV, using a lawn-mowing survey mode. The hybrid underwater navigation system shows good tracking performance, by updating the error covariance and correcting the system's states with the measurement errors from a DVL, a magnetic compass, and a depth sensor. The error of the estimated position still slowly drifts in the horizontal plane, about 3.5m for 500 seconds, which could be eliminated with the help of additional USBL information.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.503-505
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• 1986

주어진 제어대상 모델에 대하여 제어기를 구성하여 실제로 적용하는 경우 모델의 불일치, 모델링에서 고려하지 않은 외란(disturbance), 측정잡음등에 의하여 성능이 설계시와 달라진다. 실제적용에서도 성능을 계속 유지하기 위하여 제어기는 안정성, 계수변화(parameter variation)에 대한 강인성(robustness), 외란상쇄(disturbance rejection) 및 측정잡음에 둔감함등의 특성을 가져야 한다. 귀환(feedback)을 사용하여 제어기를 구성하는 경우 위의 모든 조건을 만족 시킬 수 없으므로 제어목적에 따라 적당한 조건을 선정하여 중요한 특성을 주로 갖게 한다. 본 논문에서는 쌍동선(small waterplane area twin hull ship-SWATHS)에 대하여 PID, LQ, LQG 제어기를 구성하여 안정성, 계수 변화에 대한 강인성, 외란 상쇄 및 측정잡음의 영향을 비교하였다. 쌍동선의 경우 다른 단동선(mono hull ship)에 비하여 접수면(waterplane)이 적으므로 무게증변을 흡수할 수 있는 복원력이 약하여 적은 외력에도 상하동요(heave)와 종동요(pitch)가 심하게 일어난다. 이러한 동요를 줄이는 것이 쌍동선의 제어목적이다. 본 연구에서는 먼저 선형화된 수직축 운동방정식을 이용하여 선체운동의 모델을 구했으며 중첩의 원리(super-position theorem)에 의하여 주파수 응답의 합으로 파도입력을 모델링 하였으며 제어를 위하여 필요한 측정치는 IMU(Inertial Measurement Unit)에서 제공된다고 가정하였다. 쌍동선의 동요의 원인은 파도, 바람, 조류 등이 있으나 파도에 의한 영향이 가장 크므로 본 논문에서는 파도에 의한 영향만을 고려하였다. 파도는 쌍동선에 외란으로 작용하며 측정할 수 없는 양이므로 PID, LQ 제어에서는 제어모델에 포함되지 않지만 LQG 제어에서는 제어모델에 포함된다. LQG 제어의 경우 제어모델에 파도를 백색잡음으로 가정하고 제어기를 구성한 것 (LQG1)과 2차의 쉐이핑필터(shaping filter)를 사용하여 구성한 것(LQG2)으로 나누었다.

• Journal of Ocean Engineering and Technology
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• v.24 no.3
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• pp.46-51
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• 2010

This paper describes the implementation of a precise underwater navigation solution using a multi-sensor fusion technique based on USBL, DVL, and IMU measurements. To implement this precise underwater navigation solution, three strategies are chosen. The first involves heading alignment angle identification to enhance the performance of a standalone dead-reckoning algorithm. In the second, the absolute position is found quickly to prevent the accumulation of integration error. The third one is the introduction of an effective outlier rejection algorithm. The performance of the developed algorithm was verified with experimental data acquired by the deep-sea ROV, Hemire, in the East-sea during a survey of a methane gas seepage area at a 1,500 m depth.

• Journal of the Korea Institute of Military Science and Technology
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• v.2 no.2
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• pp.186-196
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• 1999

The CPS provides data with long-term stability independent of passed time and the INS provides high-rate data with short-term stability. By integrating these complementary systems, a highly accurate navigation system can be achieved. In this paper, a loosely-coupled GPS/INS integration system is designed. It is a simple structure and is easy to implement and preserves independent navigation capability of GPS and INS. The integration system consists of a NCU, an IMU, a GPS receiver, and a monitoring system. The navigation algorithm in the NCU is designed under the multi-tasking environment based on a real-time kernel system and the monitoring system is designed using the Visual C++. The integrated Kalman filter is designed as a feedback formed 15-state filter, in which the states are position errors, velocity errors, attitude errors and sensor bias errors. The van test result shows that the integrated system provides more accurate navigation solution then the inertial or the GPS-alone navigation system.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.169-174
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• 2002

In this paper, we suggest a new spatial information system called video GIS where video is used for spatial data construction and is integrated with map. We develop a prototype system of video GIS and apply it to roadside facility management. The main functions supported by the suggested system are data collection, coordinate calculation and conversion, data construction, analysis, searching, and browsing. The stereo images and corresponding position data are collected by a vehicle named 4S-Van that has GPS, IMU, and cameras. The 3-D coordinates of the objects in the images, such as road sign, signal lamp, and building, can be calculated and constructed from the collected data. The spatial objects are displayed on both image and map, and can be searched and browsed, which enables visual and realistic browsing and management of spatial objects. Compared to conventional field survey used in roadside facility management, the method enables faster, easier, and more efficient construction of spatial data. The suggested video GIS can be applied not only to roadside facility management but also to many similar projects of central or local governments that are related to GIS.

• 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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.397-402
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• 2019

Recently, with the emergence of autonomous vehicles and the increasing interest in safety, a variety of research has been being actively conducted to precisely estimate the position of a vehicle by fusing sensors. Previously, researches were conducted to determine the location of moving objects using GNSS (Global Navigation Satellite Systems) and/or IMU (Inertial Measurement Unit). However, precise positioning of a moving vehicle has lately been performed by fusing data obtained from various sensors, such as LiDAR (Light Detection and Ranging), on-board vehicle sensors, and cameras. This study is designed to enhance kinematic vehicle positioning performance by using feature-based recognition. Therefore, an analysis of the required precision of the observations obtained from the images has carried out in this study. Velocity and attitude observations, which are assumed to be obtained from images, were generated by simulation. Various magnitudes of errors were added to the generated velocities and attitudes. By applying these observations to the positioning algorithm, the effects of the additional velocity and attitude information on positioning accuracy in GNSS signal blockages were analyzed based on Kalman filter. The results have shown that yaw information with a precision smaller than 0.5 degrees should be used to improve existing positioning algorithms by more than 10%.