• Journal of Institute of Control, Robotics and Systems
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• v.17 no.2
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• pp.183-189
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• 2011

An alignment algorithm for strapdown inertial navigation systems is proposed, in which the psi angle error model is utilized. The proposed alignment algorithm is derived from the Psi angle error model which has been widely used in real-time navigation systems. The equation for expecting steady state alignment error is also derived. The proposed algorithm was verified through real-time experiments. Experimental results show that the proposed algorithm can be used in the inertial navigation system and GNSS/INS integrated navigation system to get an initial attitude of the vehicle.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2005.10a
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• pp.47-52
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• 2005

This paper describes how a relatively compensate the error of position by using low cost Inertial Measurement Unit (IMU) has been evaluated and compared with the well established method based on a Kalman Filter(KF). The compensation algorithm by using IMU have been applied to the problem of integrating information from an Inertial Navigation System (INS). The KF is to estimate and compensate the errors of an INS by using the integrated INS velocity and position. We verify the proposed algorithm by simulation results.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.613-620
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• 2013

A new remote control algorithm for a mobile robot is proposed, where a remote controller consists of a camera and inertial sensors. Initially the relative position and orientation of a robot is estimated by capturing four circle landmarks on the plate of the robot. When the remote controller moves to point to the destination, the camera pointing trajectory is estimated using an inertial navigation algorithm. The destination is transmitted wirelessly to the robot and then the robot is controlled to move to the destination. A quick movement of the remote controller is possible since the destination is estimated using inertial sensors. Also unlike the vision only control, the robot can be out of camera's range of view.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.1
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• pp.101-108
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• 1998

This paper concerns the navigation algorithm of motion reference unit (MRU) for autonomous underwater vehicle (AUV) We apply the strapdown navigation system using middle level inertial sensors. But, because the MRU consists of inertial sensors, the values of AUV motion calculated by navigation computer are increased by drift property of inertial sensors. Therefore, we propose the attitude algorithm using switching method according to the motion of AUV From this algorithm, the drift terms are eliminated effectively for roll and pitch. But, another device is required for yaw angle.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.7
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• pp.627-632
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• 2007

Dual quaternion is efficient methodology to express rotation and translation of the vehicle's movements in the unified frame work. Recently, a strapdown inertial navigation algorithm based on dual quaternion was introduced. By comparing and analyzing the classical and dual-quaternion algorithms, this paper proposes a new strapdown inertial navigation algorithm that maintains the accuracy benefit of the dual-quaternion algorithm with considerable computational reduction. Simulation results show the efficiency of the proposed hybrid strapdown navigation algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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• pp.189-198
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• 2019

In recent years, visual-inertial odometry(VIO) algorithms have been extensively studied for the indoor/urban environments because it is more robust to dynamic scenes and environment changes. In this paper, we propose loosely coupled(LC) VIO algorithm that utilizes the velocity vectors from both visual odometry(VO) and inertial measurement unit(IMU) as a filter measurement of Extended Kalman filter. Our approach improves the estimation performance of a filter without adding extra sensors while maintaining simple integration framework, which treats VO as a black box. For the VO algorithm, we employed a fundamental part of the ORB-SLAM, which uses ORB features. We performed an outdoor experiment using an RGB-D camera to evaluate the accuracy of the presented algorithm. Also, we evaluated our algorithm with the public dataset to compare with other visual navigation systems.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.5
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• pp.1010-1018
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• 2009

An inertial navigation system for pedestrian position tracking is proposed, where the position is computed using inertial sensors mounted on shoes. Inertial navigation system(INS) errors increase with time due to inertial sensor errors, and therefore it needs to reset errors frequently. During normal walking, there is an almost periodic zero velocity instance when a foot touches the floor. Using this fact, estimation errors are reduced and this method is called the zero velocity updating algorithm. When implementing this zero velocity updating algorithm, it is important to know when is the zero velocity interval. The gait states are modeled as a Markov process and each state is estimated using the hidden Markov model smoother. With this gait estimation, the zero or nearly zero velocity interval is more accurately estimated, which helps to reduce the position estimation error.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.672-675
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• 1996

This paper presents algorithm including Kalman filter for transfer alignment of velocity and quaternion matching method, when master inertial navigation system is a gimbled type and slave inertial navigation system is a strapdown type on a cruising ship which is naturally in motion of horizontal axis attitude. And relative attitudes are considered on a measurement equation for quaternion matching between master INS and slave INS.

• Journal of Advanced Navigation Technology
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• v.23 no.3
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• pp.245-250
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• 2019

Inertial navigation system has navigation errors because of the error of inertial measurement unit (IMU) and misalignment over time. In order to solve this problem, aided navigation system is performed using global navigation satellite system (GNSS), speedometer, etc. The inertial navigation system equipped with underwater vehicle mainly uses speedometer and performed aided navigation because satellite signals do not pass through underwater. There are DVL, EM-Log, and RPM in the speedometer, and the sensors are applied according to the system environment. This paper describes velocity aided navigation using RPM of inertial navigation system operating in high speed and deep water environment. In addition, we proposes an algorithm to compensate the limit of RPM with straight direction and the current velocity error. There are results of monte-calo simulation to prove performance of the proposed algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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• pp.291-297
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• 2000

This paper presents an attitude determination algorithm for the local level strapdown inertial navigation where the body the earth and the transport rate can be calculated separately using a DCM computation scheme, Also presented is a velocity determination algorithm taking into account the attitude variation caused by he change of the navigation frame. The proposed algorithm are implemented using three samples of the velocity and the angle within the integration interval.