• Journal of Institute of Control, Robotics and Systems
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• v.19 no.4
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• pp.372-378
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• 2013

In this paper, we research the localization problem of the crawler-type inspection robot for underwater structure which travels an outer wall of underwater structure. Since various factors of the underwater environment affect an encoder odometer, it is hard to localize robot itself using only on-board sensors. So in this research we used a depth sensor and an IMU to compensate odometer which has extreme error in the underwater environment through using Extended Kalman Filter(EKF) which is normally used in mobile robotics. To acquire valid measurements, we implemented precision sensor modeling after assuming specific situation that robot travels underwater structure. The depth sensor acquires a vertical position of robot and compensates one of the robot pose, and IMU is used to compensate a bearing. But horizontal position of robot can't be compensated by using only on-board sensors. So we proposed a localization algorithm which makes horizontal direction error bounded by using kinematics relationship. Also we implemented computer simulations and experiments in underwater environment to verify the algorithm performance.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.2
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• pp.117-124
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• 2020

In this paper, the symmetric position drift of the integration approach in pedestrian dead reckoning (PDR) system with dual foot-mounted IMU is analyzed. The PDR system that uses the inertial sensor attached to the shoe is called the IA-based PDR system. Since this system is designed based on the inertial navigation system (INS), it has the same characteristics as the error of the INS, then zero-velocity update (ZUPT) is used to correct this error. However, an error that cannot be compensated perfectly by ZUPT exists, and the trend of the position error is the symmetric direction along the side of the shoe(left, right foot) with the IMU attached. The symmetric position error along the side of the shoe gradually increases with walking. In this paper, we analyze the causes of symmetric position drift and show the results. It suggests the possibility of factors other than the error factors that are generally considered in the PDR system based on the integration approach.

• 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 for Aeronautical & Space Sciences
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• v.46 no.5
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• pp.385-392
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• 2018

This paper suggests a new method to estimate the roll and pitch damping moment coefficients of a flying disc through sensor data from the onboard IMU module. This method can be easily performed than wind tunnel or computational fluid dynamics methods because it estimates aerodynamic coefficients simply after accumulating the inertial data through several repeated flight experiments. Estimated coefficients are applied to a simulator which is based on the flight dynamics of a flying disc. Finally, the predicted flight trajectory is compared with the true position provided by GPS, which demonstrated the validity of the proposed estimation method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.233-235
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• 2017

Forklift tend to work in place rather than moving due to work environment conditions, so they express only the engine operation time without expressing the moving distance unlike a general car's instrument panel. Therefore, various consumables constituting the forklift have a replacement cycle according to the operation time of the engine. However, it is very difficult to judge the exact replacement cycle only by the engine operation time because the working environment differs for each forklift. In this paper, we propose a system that provides position information and moving distance information of forklift to driver using GPS and IMU sensor. By using this system, it is expected that the forklift status information, which is difficult to judge by the existing instrument panel, is provided as easier information, and economic benefits for forklift management and maintenance are expected.

• Journal of the Korean Institute of Gas
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• v.6 no.1 s.17
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• pp.52-58
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• 2002

The PIG(Pipeline Inspection Gauge) is inserted in the pipeline and stores data of pipeline. In order to investigate the status of pipeline, a lot of sensors such as caliper, pressure, IMU and odometer are used. In this paper, the navigation storage data system for PIG is developed. It has master/slave structure for a real time operation. The master system stores data, while the slave system acquire the data from sensors. The performance of the developed system is verified by pull rig test.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.493-495
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• 2015

PDR은 일반적으로 IMU센서로 부터의 가속도와 각속도를 측정하여 보행자의 위치를 추적하는 시스템이다. IMU센서로부터 측정된 가속도와 각속도 값은 센서를 기준으로 하기 때문에 보행자가 인지하는 고정 좌표계와는 차이가 있다. 이를 해결하기 위해 회전행렬을 사용하며 이후 계속해서 측정되는 각속도를 통해 회전행렬을 업데이트 한다. 업데이트된 회전행렬을 통해 좌표계를 환산하고 환산된 좌표계의 가속도 값으로부터 보행자는 고정좌표계 기준으로 위치 추적이 가능하다. 하지만 회전행렬을 업데이트 하는 과정에서 센서의 세 축이 이상적으로 수직이 아니라면 업데이트 과정에서 각속도의 오차가 누적되고 이는 좌표계를 환산에 영향을 끼쳐 위치 및 속도 추적 정확성을 낮춘다. 물리적인 Bias가 PDR 시스템에 누적오차를 발생시킨다. 이에 제안하는 센서 축 편향 보정 알고리즘은 IMU 센서의 물리적 축 오차를 보정해주어 더 정확한 위치 추적을 가능하게 한다. 또한 Matlab을 통해 데이터를 분석하고 알고리즘의 필요성을 보인다.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1632-1640
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• 2021

In order to improve target tracking performance of the conventional electro-optical tracking system (EOTS) in the high speed and high maneuvering vehicle, an EOTS navigation algorithm is proposed, in which an inertial measurement unit(IMU) is included and navigation results of the vehicle are used. The proposed algorithm integrates vehicle's navigation results and the IMU and the time-delay and the scale factor errors are augmented into the integrated Kalman filter. In order to evaluate the proposed navigation algorithm, a land vehicle navigation experiments were performed a navigation grade navigation system, TALIN4000 and a tactical grade IMU, LN-200 and a equipment for roll motion were loaded on the land vehicle. The performance evaluation results show that the proposed algorithm effecting works in high maneuvering environment and for the time-delay.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.227-229
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• 2016

Drone is demanding more than detaile hardware and software systems because Drone have characteristic flying. If Drone is a plane crash. they have risk of personal injury and property damage. now Drone control core system is preventing a plane crash and flight unsafe flying. The key is to help flying to convert for various external environment. This research prevent a plane crash that system planning to treat six-senser-figure doubly is IMU trouble or a trouble caused by a passing parameters and IMU install to help stability in flying.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.3
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• pp.149-155
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• 2022

This paper proposes a method for estimating the road surface condition and tilt angle using an inertial measurement unit (IMU) to improve the safety in the use of mobility aids for the elderly. The measurements of the accelerometers of the IMU usually include the accelerations caused by not only the gravitational force but also linear and rotational motions. Thus, the gravitational accelerations are first extracted using several physical constraints and then incorporated into the Kalman filter to estimate the tilt angle. In addition, because the magnitudes of the accelerations produced by the rotational motions (roll and pitch motions) vary with the road surface condition, a criterion based on such accelerations is presented to classify the condition of the road surface. The obtained road surface condition and tilt angle are finally combined to provide the safety information (e.g., safe, warning, and danger) for the user to improve the walking safety. Experiments were carried out and the results showed that the proposed method can provide the condition of the road surface, the tilt of the road surface, and the safety information correctly.