• 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.39 no.5
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• pp.289-295
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• 2021

Recently, owing to the development of autonomous vehicles, research on precisely determining the position of a moving object has been actively conducted. Previous research mainly used the fusion of GNSS/IMU (Global Positioning System / Inertial Navigation System) and sensors attached to the vehicle through a Kalman filter. However, in recent years, new technologies have been used to determine the location of a moving object owing to the improvement in computing power and the advent of deep learning. Various techniques using RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory), and NARX (Nonlinear Auto-Regressive eXogenous model) exist for such learning-based positioning methods. The purpose of this study is to compare the precision of existing filter-based sensor fusion technology and the NARX-based method in case of GNSS signal blockages using simulation data. When the filter-based sensor integration technology was used, an average horizontal position error of 112.8 m occurred during 60 seconds of GNSS signal outages. The same experiment was performed 100 times using the NARX. Among them, an improvement in precision was confirmed in approximately 20% of the experimental results. The horizontal position accuracy was 22.65 m, which was confirmed to be better than that of the filter-based fusion technique.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.9
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• pp.799-807
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• 2017

In this paper, a new method is proposed for estimating pedestrians' walking velocity based on lower-limb kinematics and wearable inertial measurement unit (IMU) sensors. While the soles and ground are not in contact during the walking cycle, the walking velocity can be estimated by integrating the acceleration output of the inertial sensor mounted on the pelvis. To minimize the effects of acceleration measurement errors caused by the tilt of the pelvis while walking, the estimated walking velocity based on lower-limb kinematics is imposed as the initial value in the acceleration signal integration process of the pelvis inertial sensor. In the experiment involving outdoor walking for six minutes, sensor drift due to error accumulation was not observed, and the RMS error in the walking velocity estimation was less than 0.08 m/s.

• Spatial Information Research
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• v.19 no.4
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• pp.21-31
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• 2011

As large scaled natural or man-made disasters being increased, the demand for rapid responses for such emergent situations also has been ever-increasing. These responses need to acquire spatial information of each individual site rapidly for more effective management of the situations. Therefore, we are developing a close-range real-time aerial monitoring system based on a low altitude unmanned helicopter. This system can acquire airborne sensory data in real-time and generate rapidly geospatial information. The system consists of two main parts: aerial and ground parts. The aerial part includes an aerial platform equipped with multi-sensor(cameras, a laser scanner, a GPS receiver, an IMU) and sensor supporting modules. The ground part includes a ground vehicle, a receiving system to receive sensory data in real-time and a processing system to generate the geospatial information rapidly. Development and testing of the individual modules and subsystems have been almost completed. Integration of the modules and subsystems is now in progress. In this paper, we w ill introduce our system, explain intermediate results, and discuss expected outcome.

• Korean Journal of Remote Sensing
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• v.30 no.6
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• pp.797-807
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• 2014

The Sea Surface Temperature (SST) is one of the most important oceanic environmental factors in determining the change of marine environments and ecological activities. Satellite thermal infrared images can be effective for understanding the global trend of sea surface temperature due to large scale. However, their low spatial resolution caused some limitations in some areas where complicated and refined coastal shapes due to many islands are present as in the Korean Peninsula. The coastal ocean is also very important because human activities interact with the environmental change of coastal area and most aqua farming is distributed in the coastal ocean. Thus, low-cost airborne thermal infrared remote sensing with high resolution capability is considered for verifying its possibility to extract SST and to monitor the changes of coastal environment. In this study, an airborne thermal infrared system was implemented using a low-cost and ground-based thermal infrared camera (FLIR), and more than 8 airborne acquisitions were carried out in the western coast of the Korean Peninsula during the periods between May 23, 2012 and December 7, 2013. The acquired thermal infrared images were radiometrically calibrated using an atmospheric radiative transfer model with a support from a temperature-humidity sensor, and geometrically calibrated using GPS and IMU sensors. In particular, the airborne sea surface temperature acquired in June 25, 2013 was compared and verified with satellite SST as well as ship-borne thermal infrared and in-situ SST data. As a result, the airborne thermal infrared sensor extracted SST with an accuracy of $1^{\circ}C$.

• 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 the Korean Society for Precision Engineering
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• v.31 no.1
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• pp.43-50
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• 2014

This paper introduces a robot platform which can do weeding while traveling between rice seedlings stably against irregular land surface of a paddy field. Also, an autonomous navigation technique that can track on stable state without any damage of the seedlings in the working area is proposed. Detection of the rice seedlings and avoidance knocking down by the robot platform is achieved by the sensor fusion of a laser range finder (LRF) and an inertial measurement unit (IMU). These sensors are also used to control navigating direction of the robot to keep going along the column of rice seedling consistently. Deviation of the robot direction from the rice column that is sensed by the LRF is fed back to a proportional and derivative controller to obtain stable adjustment of navigating direction and get proper returning speed of the robot to the rice column.

• Journal of Advanced Navigation Technology
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• v.16 no.5
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• pp.738-743
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• 2012

Kalman filter is generally used in INS/GPS integrated navigation filter. However, the INS with low performance inertia sensor can not find accurate azimuth in initial alignment stage because sensor noise level is too large compare to Earth rotation rate, therefore the performance and stability of Kalman filter can not be guaranteed. In this paper, the extended Kalman filter and particle filter combined filter structure which can be overcome large initial azimuth error is proposed.

• Journal of Sensor Science and Technology
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• v.25 no.5
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• pp.365-370
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• 2016

The study about VR (Virtual Reality) has been done from the 1960s, but technical limits and high cost made VR hard to commercialize. However, in recent, high resolution display, computing power and 3D sensing have developed and hardware has become affordable. Therefore, normal users can get high quality of immersion and interaction. However, HMD devices which offer VR environment have high latency, so it disrupts the VR environment. People are usually sensitive to relative latency over 20ms. In this paper, as adding the Electromyogram (EMG) sensors to typical IMU sensor only system, the latency reduction method is proposed. By changing software and hardware components, some cases the latency was reduced significantly. Hence, this study covers the possibility and the experimental verification about EMG sensors for reducing the latency.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.11
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• pp.967-976
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• 2016

This paper presents a 3D carrier-smoothed GNSS/DR (Global Navigation Satellite System/Dead Reckoning) integrated system for precise ground-vehicle trajectory estimation. For precise DR navigation on sloping roads, the AHRS (Attitude Heading Reference System) methodology is employed. By combining the integrated carrier phase of GNSS and DR sensor measurements, a vehicle trajectory with an accuracy of less than 20cm is obtained even when cycle slip or change of visibility occur. In order to supplement the weak GNSS environment with DR successfully, the DR sensor is precisely compensated for using GNSS Doppler measurements when GNSS visibility is good. By integrating a multi-GNSS receiver with low-cost IMU, a precise 3D navigation system for land vehicles is proposed in this paper. For real-time implementation, a decoupled Kalman filter is employed in the integrated system. Through field experiments, the performance of the proposed system is verified in various road environments, including sloping roads, good-visibility areas, high multi-path areas, and under-ground parking areas.