• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.5
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• pp.462-471
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• 2008

Flight recoder is used to analyze the accident factors and prevent the accident. In the analysis of the flight recorder, the most important factor is how to estimate the precise location of the flight. Traditional aviation navigation is based on stable sensors such as DME and VOR. In order to enhance the precision of the location estimation, the integrated navigation algorithm is designed to incorporate DME, Air data sensors and INS(Inertial Navigation System). The results demonstrate that the proposed algorithm can achieve better accuracy, comparing with the traditional navigation schemes, in flight location estimation.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.7
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• pp.562-569
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• 2016

This paper proposes a navigation system with a robust localization method for an underwater unmanned vehicle. For robust localization with IMU (Inertial Measurement Unit), a DVL (Doppler Velocity Log), and depth sensors, the EKF (Extended Kalman Filter) has been utilized to fuse multiple nonlinear data. Note that the GPS (Global Positioning System), which can obtain the absolute coordinates of the vehicle, cannot be used in the water. Additionally, the DVL has been used for measuring the relative velocity of the underwater vehicle. The DVL sensor measures the velocity of an object by using Doppler effects, which cause sound frequency changes from the relative velocity between a sound source and an observer. When the vehicle is moving, the motion trajectory to a target position can be recorded by the sensors attached to the vehicle. The performance of the proposed navigation system has been verified through real experiments in which an underwater unmanned vehicle reached a target position by using an IMU as a primary sensor and a DVL as the secondary sensor.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.507-513
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• 2021

GPR (Ground Penetrating RADAR) is a sensor that inspects the pavement state of roads, sinkholes, and underground pipes. It is widely used in road management. MMS (Mobile Mapping System) creates a detailed and accurate road map of the road surface and its surroundings. If both types of data are built in the same area, it is efficient to construct both ground and underground spatial information at the same time. In addition, since it is possible to grasp the road and important facilities around the road, the location of underground pipelines, etc. without special technology, an intuitive understanding of the site is also possible, which is a useful tool in managing the road or facilities. However, overseas equipment to which this latest technology is applied is expensive and does not fit the domestic situation. LiDAR (Light Detection And Raging) and GNSS/INS (Global Navigation Satellite System / Inertial Navigation System) were synchronized in order to replace overseas developed equipment and to secure original technology to develop domestic equipment in the future, and GPR data was also synchronized to the same GNSS/INS. We developed software that performs georeferencing using the location and attitude information from GNSS/INS at the time of acquiring synchronized GPR data. The experiments were conducted on the road site by dividing the open sky and the non-open sky. The road and surrounding facilities on the ground could be easily checked through the 3D point cloud data acquired through LiDAR. Georeferenced GPR data could also be viewed with a 3D viewer along with point cloud data, and the location of underground facilities could be easily and quickly confirmed through GPR data.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.1
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• pp.46-55
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• 2006

The purpose of this study is a development of an automatic mosaicing for applying to large number of airborne multispectral images, which reduces manual operation by human. 2436 airborne multispectral images were acquired from DuncanTech MS4100 camera with three bands; green, red and near infrared. LIDAR(LIght Detection And Ranging) data and GPS/INS(global positioning system/inertial navigation system) data were collected with the multispectral images. First, the multispectral images were converted to image patterns by unsupervised classification. Their patterns were compared with those of adjacent images to derive relative spatial position between images. Relative spatial positions were derived for 80% of the whole images. Second, it accomplished an automatic mosaicing using GPS/INS data and unsupervised classification. Since the time of GPS/INS data did not synchronized the time of readout images, synchronized GPS/INS data with the time of readout image were selected in consecutive data by comparing unsupervised classified images. This method realized mosaicing automatically for 96% images and RMSE (root mean square error) for the spatial precision of mosaiced images was only 1.44 m by validation with LIDAR data.

• International Journal of Highway Engineering
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• v.13 no.2
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• pp.175-185
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• 2011

Geometric information is a key element for evaluating traffic safety and road maintenance. This study developed an algorithm to identify horizontal alignment using global positioning system(GPS) and inertial navigation system(INS) data. Roll and heading information extracted from GPS/INS were utilized to classify horizontal alignment into tangent, circular curve, and transition curve. The proposed algorithm consists of two components including smoothing for eliminating outlier and a heuristic classification algorithm. A genetic algorithm(GA) was adopted to calibrate parameters associated with the algorithm. Both freeway and rural highway data were used to evaluate the performance of the proposed algorithm. Promising results, which 90.48% and 88.24% of classification accuracy were obtainable for freeway and rural highway respectively, demonstrated the technical feasibility of the algorithm for the implementation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.4
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• pp.693-699
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• 2006

Digital airborne image must be precisely orthorectified to become geographical information. For orthorectification of airborne images, GPS/INS (Global Positioning System/Inertial Navigation System) and LIDAR (LIght Detection And Ranging) elevation data were employed. In this study, 635 frame airborne images were produced and LIDAR data were converted to raster image for applying to image orthorectification. To derive images with constant brightness, flat field correction was applied to images. The airborne images were geometrically corrected by calculating internal orientation and external orientation using GPS/INS data and then orthorectified using LIDAR digital elevation model image. The precision of orthorectified images was validated by collecting 50 ground control points from arbitrary five images and LIDAR intensity image. As validation result, RMSE (Root Mean Square Error) was 0.387 as almost same as only two times of pixel spatial resolution. It is possible that this automatic orthorectification method of airborne image with higher precision is applied to airborne image industry.

• Special Issue of the Society of Naval Architects of Korea
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• 2008.09a
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• pp.68-75
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• 2008

This paper describes the design and implementation of a low cost inertial navigation system(INS) using an inertial measurement unit(IMU), a digital compass, GPS, and an embedded system. The system has been developed for a transporter that load and unload ship blocks in a shipbuilding yard. When the transporter would move from place to place, they would periodically pass under obstructions that would obscure the GPS signal. This increases the error when estimating the position. Thus the INS has been used to improve position accuracy. INS is also capable of providing continuous estimates of the transporter's position and orientation. Even though IMU is typically very expensive, this INS is made of "low cost" components and the indirect Kalman filtering algorithm.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.3
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• pp.107-111
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• 2021

A 3D point cloud map is an essential elements in various fields, including precise autonomous navigation system. However, generating a 3D point cloud map using a single sensor has limitations due to the price of expensive sensor. In order to solve this problem, we propose a precise 3D mapping system using low-cost sensor fusion. Generating a point cloud map requires the process of estimating the current position and attitude, and describing the surrounding environment. In this paper, we utilized a commercial visual-inertial odometry sensor to estimate the current position and attitude states. Based on the state value, the 2D LiDAR measurement values describe the surrounding environment to create a point cloud map. To analyze the performance of the proposed algorithm, we compared the performance of the proposed algorithm and the 3D LiDAR-based SLAM (simultaneous localization and mapping) algorithm. As a result, it was confirmed that a precise 3D point cloud map can be generated with the low-cost sensor fusion system proposed in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.64.1-64
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• 2001

Errors in inertial navigation system(INS) can be divided into two major groups which are system related errors and modeling errors due to approximation and linearization. Measurement noise, calibration, and alignment errors make up the first group, whereas the uncertainties in the gravity vector fall in the second category and are important error source for high quality INS, especially during high altitude and and/or long time missions, when the gravity errors tent to build up. The quality of a medium to high accuracy INS depends on the knowledge of the local gravity field. In this paper, the feasibility of improving airborns INS by use of more accurate gravity model is studied. To make consistent comparisons, WGS-84 parameters are used and ...

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.439-439
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• 2000

In this paper, the relationship among the vertical acceleration, measuring points and walking patterns is analyzed. To measure acceleration, the acceleration measurement unit and communication board is constructed. It uses MEMS accelerometer ADXL-202 that detects 2-axis acceleration simultaneously. It is shown by the experiment test that the walking pattern is recognized and walking step is detected at easy when acceleration measurement unit is mounted on leg.. This results can be directly utilized in designing the personal navigation system with low-cost inertial sensor.