• ETRI Journal
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• v.27 no.4
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• pp.418-426
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• 2005

A calibration technique for a redundant inertial measurement unit (IMU) containing low-grade inertial sensors is proposed. In order to calibrate a redundant IMU that can detect and isolate faulty sensors, the fundamental coordinate frames in the IMU are defined and the IMU error is modeled based on the frames. Equations to estimate the error coefficients of the redundant IMU are formulated, and a test sequence using a 2-axis turntable is also presented. Finally, a redundant IMU with cone configuration is implemented using low-grade inertial sensors, and the performance of the proposed technique is verified experimentally.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.10
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• pp.53-59
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• 2004

A calibration technique for a redundant IMU with low-grade inertial sensors is proposed. In order to calibrate the redundant IMU that can detect and isolate a faulty sensor, the fundamental coordinate frames in the IMU are defined and the IMU error is modeled based on the frames. Equations to estimate the error coefficients of the redundant IMU are formulated, and a test sequence using the 2-axis rate table is also presented. Finally, a redundant IMU with cone configuration is implemented using the low-grade inertial sensors and the performance of the proposed technique is verified by some experiments.

• Journal of Drive and Control
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• v.13 no.1
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• pp.49-53
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• 2016

The accurate calculation of bucket tip position has a large influence on showing the motion of an excavator on the display device of the excavator and controlling the excavator automatically. It is generally known that Inertial Measurement Unit (IMU) sensors are more accurate than accelerometer-based sensors while the boom, arm or bucket moves because additional forces beyond gravity add additional acceleration to the sensors. To prove the accuracy difference between the two types of sensors, a position recognition system using an accelerometer-based sensor and an IMU sensor is implemented on the excavator. The experimental results show that the system using the IMU sensor significantly reduces the position recognition error while bucket moves and additional force beyond gravity exists.

• Journal of Ocean Engineering and Technology
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• v.11 no.1
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• pp.95-105
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• 1997

This paper presents a Inertial Measuring Unit(IMU) for motion measurement of an AUV. The IMU is composed of three parts: inertial sensors with three servo accelerometers and three rate gyros, an analog/digital interface board, and a signal processing board with TMS320C31 DSP processor. The IMU is a class of strap-down inwetial navigation system does not applicable directly to the navigation system in consequence of the AUV and integrated sensors for an integrated navigation system of the AUV. Fast calculstion of direction cosine matrix for the coordinate transformation body to reference is obtained through the DSP processor. A switching algotrithm is used to lessen the low frequency drift effect of the gyros in the vertical plane with use of low pass filtering of the signal of the accelerometers.

• Journal of Sensor Science and Technology
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• v.27 no.2
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• pp.86-92
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• 2018

In recent times, motion capture technology using inertial measurement unit (IMU) sensors has been actively used in sports. In this study, we developed a canoe paddle, installed with an IMU and a water level sensor, as a system tool for training and calibration purposes in water sports. The hardware was fabricated to control an attitude heading reference system (AHRS) module, a water level sensor, a communication module, and a wireless charging circuit. We also developed an application program for the mobile device that processes paddling motion data from the paddling operation and also visualizes it. An AHRS module with acceleration, gyro, and geomagnetic sensors each having three axes, and a resistive water level sensor that senses the immersion depth in the water of the paddle represented the paddle motion. The motion data transmitted from the paddle device is internally decoded and classified by the application program in the mobile device to perform visualization and to operate functions of the mobile training/correction system. To conclude, we tried to provide mobile knowledge service through paddle sport data using this technique. The developed system works reasonably well to be used as a basic training and posture correction tool for paddle sports; the transmission delay time of the sensor system is measured within 90 ms, and it shows that there is no complication in its practical usage.

• 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 Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.18-20
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• 2021

In order to prevent collision accidents of ships, which has been increasing recently, research on artificial intelligence-based autonomously operated ships (Maritime Autonomous Surface Ship, MASS) is underway. However, most of the studies related to autonomous ships mainly target medium-to-large ships due to the size and cost of the autonomous navigation system, and the sensors used here have a problem in that it is difficult to mount them on small ships. Therefore, this paper provides a path tracking system equipped with GPS and IMU sensors for autonomous operation of small ships. GPS and IMU sensors are utilized to determine the exact position of the vessel, which allows the proposed system to manually control the small vessel model to create a path and then when the small vessel travels the same path. Use the Pure Pursuit algorithm to follow the path. As a result, In this research, it is expected that a lightweight and low-cost sensor can be used to develop an autonomous operation system for small ships at low cost.

• Journal of Korea Spatial Information System Society
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• v.6 no.1 s.11
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• pp.51-58
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• 2004

Mobile Mapping System is an effective wav to obtain position and image using vehicle equipped with GPS(Global Positioning System), IMU(Inertial Measurement Unit), and CCD camera. It have been used various fields of load facility management, map upgrade and etc. It is difficult to upgrade Mobile Mopping System which is developed from abroad and add other sensors because we don't know the way to integrate and synchronize multi-sensors. In this paper, we present the effective way of the integration and synchronization method for multi sensors we designed and manufactured Synchronization equipment by considering sensors of laser, odometer and etc.

• Journal of Ocean Engineering and Technology
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• v.11 no.1
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• pp.96-96
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• 1997

This paper presents a Inertial Measuring Unit(IMU) for motion measurement of an AUV. The IMU is composed of three parts: inertial sensors with three servo accelerometers and three rate gyros, an analog/digital interface board, and a signal processing board with TMS320C31 DSP processor. The IMU is a class of strap-down inwetial navigation system does not applicable directly to the navigation system in consequence of the AUV and integrated sensors for an integrated navigation system of the AUV. Fast calculstion of direction cosine matrix for the coordinate transformation body to reference is obtained through the DSP processor. A switching algotrithm is used to lessen the low frequency drift effect of the gyros in the vertical plane with use of low pass filtering of the signal of the accelerometers.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.167-181
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• 2024

Utilization of a driving simulator in the development of autonomous driving technology allows us to perform various tests effectively in criticial environments, thereby reducing the development cost and efforts. However, there exists a serious drawback that the driving simulator has a big difference from the real environment, so a problem occurs when the autonomous driving algorithm developed using the driving simulator is applied directly to the real vehicle system. This is defined as so-called Sim2Real problem and can be classified into scenarios, sensor modeling, and vehicle dynamics. This Paper presensts on a method to solve the Sim2Real problem in autonomous driving simulator focusing on IMU sensor. In order to reduce the difference between emulated virtual IMU sensor real IMU sensor, IMU sensor emulation techniques through precision error modeling of IMU sensor are introduced. The error model of IMU sensors takes into account bias, scale factor, misalignmnet, and random walk by IMU sensor grades.