• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.143-144
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• 2016

본 논문은 보행자의 각 발에 부착된 2개의 IMU(Inertial Measurement Unit) 정보를 융합하여 위치 추적 성능을 향상시키는 보행자 추측 항법 알고리즘을 제안하였다. 센서내의 방향드리프트로 인해 IMU기반 보행자 위치추적은 시간이 지남에 따라 성능이 크게 저하된다. 제안하는 알고리즘은 방향 드리프트로 인해 각 발의 이동경로가 발산하는 점에 착안하여, 보폭이 일정 값을 초과할 시 이를 보정하고 사용자의 위치를 계산한다. 실험을 통해 제안하는 알고리즘이 방향 드리프트를 효과적으로 감소시키는 것을 확인하였다.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.11
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• pp.937-943
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• 2016

Vertical velocity is critical in many areas, such as the control of unmanned aerial vehicles, fall detection, and virtual reality. Conventionally, the integration of GPS (Global Positioning System) with an IMU (Inertial Measurement Unit) was popular for the estimation of vertical components. However, GPS cannot work well indoors and, more importantly, has low accuracy in the vertical direction. In order to overcome these issues, IMU-barometer integration has been suggested instead of IMU-GPS integration. This paper proposes a new complementary filter for the estimation of vertical velocity based on IMU-barometer integration. The proposed complementary filter is designed to minimize drift error in the estimated velocity by adding PID control in addition to a zero velocity update technique.

• ETRI Journal
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• v.26 no.5
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• pp.497-500
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• 2004

In this paper, we present a novel idea to integrate a low cost inertial measurement unit (IMU) and Global Positioning System (GPS) for land vehicle localization. By taking advantage of positioning data calculated from an image based on photogrammetry and stereo-vision techniques, errors caused by a GPS outage for land vehicle localization were significantly reduced in the proposed bimodal approach. More specifically, positioning data from the photogrammetric approach are fed back into the Kalman filter to reduce and compensate for IMU errors and improve the performance. Experimental results are presented to show the robustness of the proposed method, which can be used to reduce positioning errors caused by a low cost IMU when a GPS signal is not available in urban areas.

• Journal of Sensor Science and Technology
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• v.28 no.6
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• pp.345-354
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• 2019

The 3D position of pedestrians is a physical quantity used in various fields, such as automotive navigation and augmented reality. An inertial navigation system (INS) based pedestrian dead reckoning (PDR), hereafter INS-PDR, estimates the relative position of pedestrians using an inertial measurement unit (IMU). Since an INS-PDR integrates the accelerometer signal twice, cumulative errors occur and cause a rapid increase in drifts. Various correction methods have been proposed to reduce drifts. For example, one of the most commonly applied correction method is the zero velocity update (ZUPT). This study investigated the characteristics of the existing INS-PDR methods based on shoe-mounted IMU and compared the estimation performances under various conditions. Four methods were chosen: (i) altitude correction (AC); (ii) step length correction (SLC); (iii) advanced heuristic drift elimination (AHDE); and (iv) magnetometer-based heading correction (MHC). Experimental results reveal that each of the correction methods shows condition-sensitive performance, that is, each method performs better under the test conditions for which the method was developed than it does under other conditions. Nevertheless, AC and AHDE performed better than the SLC and MHC overall. The AC and AHDE methods were complementary to each other, and a combination of the two methods yields better estimation performance.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.129-140
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• 2023

Autonomous driving systems are likely to be operated in various complex environments. However, the well-known integrated Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS), which is currently the major source for absolute position information, still has difficulties in accurate positioning in harsh signal environments such as urban canyons. To overcome these difficulties, integrated Visual/Inertial/GNSS (VIG) navigation systems have been extensively studied in various areas. Recently, a Compressed-State Constraint Kalman Filter (CSCKF)-based VIG navigation system (CSCKF-VIG) using a monocular camera, an Inertial Measurement Unit (IMU), and GNSS receivers has been studied with the aim of providing robust and accurate position information in urban areas. For this new filter-based navigation system, on the basis of time-propagation measurement fusion theory, unnecessary camera states are not required in the system state. This paper presents a performance evaluation of the CSCKF-VIG system compared to other conventional navigation systems. First, the CSCKF-VIG is introduced in detail compared to the well-known Multi-State Constraint Kalman Filter (MSCKF). The CSCKF-VIG system is then evaluated by a field experiment in different GNSS availability situations. The results show that accuracy is improved in the GNSS-degraded environment compared to that of the conventional systems.

• 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.

• Physical Therapy Korea
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• v.30 no.2
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• pp.102-109
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• 2023

Background: While efforts have been made to differentiate fall risk in older adults using wearable devices and clinical methodologies, technologies are still infancy. We applied a decision tree (DT) algorithm using inertial measurement unit (IMU) sensor data and clinical measurements to generate high performance classification models of fall risk of older adults. Objects: This study aims to develop a classification model of fall risk using IMU data and clinical measurements in older adults. Methods: Twenty-six older adults were assessed and categorized into high and low fall risk groups. IMU sensor data were obtained while walking from each group, and features were extracted to be used for a DT algorithm with the Gini index (DT1) and the Entropy index (DT2), which generated classification models to differentiate high and low fall risk groups. Model's performance was compared and presented with accuracy, sensitivity, and specificity. Results: Accuracy, sensitivity and specificity were 77.8%, 80.0%, and 66.7%, respectively, for DT1; and 72.2%, 91.7%, and 33.3%, respectively, for DT2. Conclusion: Our results suggest that the fall risk classification using IMU sensor data obtained during gait has potentials to be developed for practical use. Different machine learning techniques involving larger data set should be warranted for future research and development.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.4
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• pp.377-386
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• 2015

Joint force/torque estimation by inverse dynamics is a traditional tool in biomechanical studies. Conventionally for this, kinematic data of human body is obtained by motion capture cameras, of which the bulkiness and occlusion problem make it hard to capture a broad range of movement. As an alternative, inertial motion sensing using cheap and small inertial sensors has been studied recently. In this research, the performance of inertial motion sensing especially to calculate inverse dynamics is studied. Kinematic data from inertial motion sensors is used to calculate ground reaction force (GRF), which is compared to the force plate readings (ground truth) and additionally to the estimation result from optical method. The GRF estimation result showed high correlation and low normalized RMSE(R=0.93, normalized RMSE<0.02 of body weight), which performed even better than conventional optical method. This result guarantees enough accuracy of inertial motion sensing to be used in inverse dynamics analysis.