• Journal of Advanced Navigation Technology
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• v.23 no.3
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• pp.228-236
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• 2019

Recently, multi-sensor integration techniques have been actively studied to obtain reliable and accurate navigation solution in GPS (Global Positioning System)-denied harsh environments such as urban canyons, tunnels, and underground roads. In this paper, we propose a low-cost ultrasonic-speedometer utilizing the characteristics of the ultrasonic propagation. An efficient integrated INS (inertial navigation system)/ultrasonic-speedometer navigation system is also proposed to improve the accuracy of positioning in GPS-denied environments. To evaluate the proposed system, car experiments with field-collected measurements were performed. By the experiment results, it was confirmed that the proposed INS/ultrasonic-speedometer system bounds the positioning error growth effectively even though GPS signal is blocked more than 10 seconds and a low-cost MEMS IMU (micro electro mechanical systems inertial measurement unit) is utilized.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.609-617
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• 2016

In general, Personal Navigation Systems (PNSs) can be defined systems to acquire pedestrian positional information. GPS is an example of PNS. However, GPS can only be used where the GPS signal can be received. Pedestrian Dead Reckoning (PDR) can estimate the positional information of pedestrians using Inertial Measurement Unit (IMU). Therefore, PDR can be used for GPS-disabled areas. This paper proposes a PDR scheme considering various movement types over GPS-disabled areas as combat environments. We propose a movement distance estimation scheme and movement direction estimation scheme as pedestrian's various movement types such as walking, running and crawling using IMU. Also, we propose a fusion algorithm between GPS and PDR to mitigate the lack of accuracy of positional information at the entrance to the building. The proposed algorithm has been tested in a real test bed. In the experimental results, the proposed algorithms exhibited an average position error distance of 5.64m and position error rate in goal point of 3.41% as a pedestrian traveled 0.6km.

• 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.

• The Journal of Korea Robotics Society
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• v.7 no.4
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• pp.276-283
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• 2012

Due to the low localization accuracy and the requirement of special infrastructure, current LBS(Localization Based Service) is limited to show P.O.I.(Point of Interest) nearby. Improvement of IMU(Inertial Measurement Unit) based deadreckoning is presented in this paper. Additional sensors such as the magnetic compass and magnetic flux sensors are used as well as the accelerometer and the gyro for getting more information of movement. Based on the pedestrian movement, appropriate sensor information is selected and the complementary filter is used in order to enhance the accuracy of the localization.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.11
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• pp.1011-1017
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• 2012

Augmented Reality is being widely used not only for Smartphone users but also in industries such as maintenance, construction area. With smartphone, due to the low localization accuracy and the requirement of special infrastructure, current LBS (Localization Based Service) is limited to show P.O.I. (Point of Interest) nearby. Improvement of IMU (Inertial Measurement Unit) based deadreckoning is presented in this paper. Additional sensors such as the magnetic compass and magnetic flux sensors are used as well as the accelerometer and the gyro for getting more movement information. Based on the pedestrian movement, appropriate sensor information is selected and the complementary filter is used in order to enhance the accuracy of the localization. Additional sensors are used to measure the movements of the upper body and the head and to provide the user's line of sight.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.9
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• pp.774-781
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• 2013

The purpose of this paper is to design a DVL-RPM based VKF (Velocity Kalman Filter) design for a performance improvement underwater integrated navigation system. The proposed approach relies on a VKF, augmented by a altitude from Echo-sounder based switching architecture to yield robust performance, even when DVL (Doppler Velocity Log) exceeds the measurement range and the measured value is unable to be valid. The proposed approach relies on two parts: 1) Indirect feedback navigation Kalman filter design, 2) VKF design. To evaluate proposed method, we compare the results of the VKF aided navigation system with simulation result from a PINS (Pure Inertial Navigation System) and conventional INS-DVL method. Simulations illustrate the effectiveness of the underwater navigation system assisted by the additional DVL-RPM based VKF in underwater environment.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.304-307
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• 1993

In this paper, we formulate Kalman filter for calibration of strapdown inertial measurment unit(SDIMU) on navigation system level and analyize its performance by covariance simulation method. It has been shown that the calibration method suggested in this paper is not largely influenced by accuracy of a mounting axis alignment required in calibration of SDIMU on IMU level.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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• pp.149-156
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• 2003

This paper presents an underwater hybrid navigation system for a semi-autonomous underwater vehicle (SAUV). 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. Simulation was performed with the 6-d.o.f. equations of motion of SAUV in a lawn-mowing survey mode. The hybrid underwater navigation system shows good tracking performance by updating the error covariance and correcting the system's states with the measurement errors from a DVL, a magnetic compass and a depth senor. The error of the estimated position still slowly drifts in horizontal plane about 3.5m for 500 seconds, which could be eliminated with the help of additional USBL information.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.4
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• pp.239-247
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• 2022

The existing studies on the localization in the GNSS (Global Navigation Satellite System) denied environment usually exploit low-cost MEMS IMU (Micro Electro Mechanical Systems Inertial Measurement Unit) sensors to replace the GNSS signals. However, the navigation system still requires GNSS signals for the normal environment. This paper presents an integrated GNSS/INS (Inertial Navigation System) navigation system which combines GNSS and multiple IMU sensors using extended Kalman filter in partially GNSS-denied environments. The position and velocity of the INS and GNSS are used as the inputs to the integrated navigation system. The Mahalanobis distance is used for novelty detection to detect the outlier of GNSS measurements. When the abnormality is detected in GNSS signals, GNSS data is excluded from the fusion process. The performance of the proposed method is evaluated using MATLAB/Simulink. The simulation results show that the proposed algorithm can achieve a higher degree of positioning accuracy in the partially GNSS-denied environment.