• Journal of Korea Multimedia Society
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• v.14 no.4
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• pp.517-528
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• 2011

In this paper, we propose an indoor navigation system that can do walk safely to the destination for visually impaired persons. The proposed system analyzes images taken with the camera finds the ID of the marker to identify the absolute position of the pedestrian. Using the distance and angle obtained from IMU(Inertial Measurement Unit) accelerometer sensor and a gyro sensor, the system decides the relative position of a pedestrian for the previous position to determine the next direction. At the same time, we simplify a complex spatial structure in front of user by means of ultrasonic sensors and determine an avoidance direction by estimating the patterns. Then, it uses a few IR(Infrared Rays) sensors to detect stair. Our system offers position of visually impaired persons incorporating multiple sensors and helps users to arrive to destination safely.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.119-126
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• 2020

The purpose of this study is to identify trends in the type and method of Inertial Measurement Unit (IMU) by investigating studies on the type and method of convergence study of the IMU by systematic review. The study was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. 23 studies that meet the selection criteria were selected from 630 studies identified by three databases. As a result of this study, showed that various research using IMU was being conducted around the world, and the type of IMU was strap, full body suit, belt, wrist watch, shoes and glove. Among them, the number of strap-type IMUs was the largest at 11. The IMU's strengths were simplicity, real-time data collection and ease of application, which were used as measurement methods such as task, walking, and range of joint. The result of this study is expected to be used as basic data for experts in the medical and rehabilitation fields that conduct IMU research.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.1
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• pp.67-75
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• 2010

This paper presents localization algorithm for mobile robot in outdoor environment. Outdoor environment includes the uncertainty on the ground. Magnetic sensor or IMU(Inertial Measurement Unit) has been used to estimate robot's heading angle. Two sensor is unavailable because mobile robot is electric car affected by magnetic field. Heading angle estimation algorithm for mobile robot is implemented using gyro sensor module consisting of 1-axis gyro sensors. Localization algorithm applied Extended Kalman filter that utilized GPS and encoder, gyro sensor module. Experiment results show that proposed localization algorithm improve considerably localization performance of mobile robots.

• Journal of Sensor Science and Technology
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• v.28 no.1
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• pp.30-35
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• 2019

Inertial measurement units (IMUs) are widely used for wearable motion-capturing systems in the fields of biomechanics and robotics. When the IMUs are combined with optical motion sensors (hereafter, OPTs) for their complementary capabilities, it is necessary to align the coordinate system orientations between the IMU and OPT. In this study, we compare the application of two coordinate transformation-based orientation alignment methods between two coordinate systems. The first method (M1) applies angular velocity coordinate transformation, while the other method (M2) applies gyroscopic angle coordinate transformation. In M1 and M2, the angular velocities and angles, respectively, are acquired during random movement for a least-square algorithm to determine the alignment matrix between the two coordinate systems. The performance of each method is evaluated under various conditions according to the type of motion during measurement, number of data points, amount of noise, and the alignment matrix. The results show that M1 is free from drift errors, while drift errors are present in most cases where M2 is applied. Thus, this study indicates that M1 has a far superior performance than M2 for the alignment of IMU and OPT coordinate systems for motion analysis.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.3
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• pp.273-284
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• 2020

The performance of Global Navigation Satellite System (GNSS) chipset and Inertial Measurement Unit (IMU) sensors embedded in smartphones for location-based services (LBS) is limited due to the economic reasons for their mass production. Therefore, it is necessary to efficiently process the output data of the smartphone's embedded sensors in order to derive the optimum navigation values and, as a previous step, output performance of smartphone embedded sensors needs to be verified. This paper analyzes the navigation performance of such devices by processing the raw measurements data output from smartphones. For this, up-to-dated versions of smartphones provided by Samsung (Galaxy s10e) and Xiaomi (Mi 8) are used in the test experiment to compare their performances and characteristics. The GNSS and IMU data are extracted and saved by using an open market application software (Geo++ RINEX Logger & Mobile MATLAB), and then analyzed in post-processing manner. For GNSS chipset, data is extracted from static environments and verified the position, Carrier-to-Noise (C/N₀), Radio Frequency Interference (RFI) performance. For IMU sensor, the validity of navigation and various location-based-services is predicted by extracting, storing and analyzing data in static and dynamic environments.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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• pp.141-148
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• 2003

This paper presents a rotating ann test for assessment of an underwater 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. 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 error model of the USBL acoustic navigation sensor and the scale effect and bias errors of the DVL, of which the state equation composed of the navigation states and sensor parameters is 25 in the order. 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. The rotating ann tests are conducted in the Ocean Engineering Basin of KRISO, KORDI to generate circular motion in laboratory, where the USBL system was absent in the basin. 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.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.525-528
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• 2003

In this paper, we present a novel idea to integrate low cost Inertial Measurement Unit(IMU) and Differential Global Positioning System (DGPS) for Telematics applications. As well known, low cost IMU produces large positioning and attitude errors in very short time due to the poor quality of inertial sensor assembly. To conquer the limitation, we present a bimodal approach for integrating IMU and DGPS, taking advantage of positioning and orientation data calculated from CCD images based on photogrammetry and stereo-vision techniques. The positioning and orientation data from the photogrammetric approach are fed back into the Kalman filter to reduce and compensate IMU errors and improve the performance. Experimental results are presented to show the robustness of the proposed method that can provide accurate position and attitude information for extended period for non-aided GPS information.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.884-887
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• 2023

Recently, research has been actively conducted to overcome the limitations of high-priced single sensors and reduce costs through the convergence of low-cost multi-variable sensors. This paper estimates state variables through asynchronous Kalman filters constructed using CVXPY and uses Cvxpylayers to compare and learn state variables estimated from CVXPY with true value data to estimate filter parameters of low-cost sensors fusion.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.68-77
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• 2021

In this paper, a GPS/MEMS IMU integrated navigation receiver module capable of operating in a high dynamic environment is designed and fabricated, and the results is confirmed. The designed module is composed of RF receiver unit, inertial measurement unit, signal processing unit, correlator, and navigation S/W. The RF receiver performs the functions of low noise amplification, frequency conversion, filtering, and automatic gain control. The inertial measurement unit collects measurement data from a MEMS class IMU applied with a 3-axis gyroscope, accelerometer, and geomagnetic sensor. In addition, it provides an interface to transmit to the navigation S/W. The signal processing unit and the correlator is implemented with FPGA logic to perform filtering and corrrelation value calculation. Navigation S/W is implemented using the internal CPU of the FPGA. The size of the manufactured module is 95.0×85.0×.12.5mm, the weight is 110g, and the navigation accuracy performance within the specification is confirmed in an environment of 1200m/s and acceleration of 10g.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.49-54
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• 2023

The Global Positioning System (GPS) was developed for military purposes and developed as it is today by opening civilian signals (GPS L1 frequency C/A signals). The current satellite orbits the earth about twice a day to measure the position, and receives more than 3 satellite signals (initially, 4 to calculate even the time error). The three-dimensional position of the ground receiver is determined using the data from the radio wave departure time to the radio wave Time of Arrival(TOA) of the received satellite signal through trilateration. In the case of navigation using GPS in recent years, a location error of 5 to 10 m usually occurs, and quite a lot of areas, such as apartments, indoors, tunnels, factory areas, and mountainous areas, exist as blind spots or neutralized areas outside the error range of GPS. Therefore, in order to acquire one's own location information in an area where GPS satellite signal reception is impossible, another method should be proposed. In this study, IMU(Inertial Measurement Unit) combined with an acceleration and gyro sensor and a geomagnetic sensor were used to design a system to enable location recognition even in terrain where GPS signal reception is impossible. A method to track the current position by calculating the instantaneous velocity value using a 9-DOF IMU and a geomagnetic sensor was studied, and its feasibility was verified through production and experimentation.