• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.439-439
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• 2000

In this paper, the relationship among the vertical acceleration, measuring points and walking patterns is analyzed. To measure acceleration, the acceleration measurement unit and communication board is constructed. It uses MEMS accelerometer ADXL-202 that detects 2-axis acceleration simultaneously. It is shown by the experiment test that the walking pattern is recognized and walking step is detected at easy when acceleration measurement unit is mounted on leg.. This results can be directly utilized in designing the personal navigation system with low-cost inertial sensor.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.445-452
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• 2024

This paper analyzes the navigation error for each rotational motion in order to design an optimal rotation sequence, which is a key technology in the rotational inertial navigation. Rotational inertial navigation system is designed to cancel out navigation errors caused by inertial sensor errors by periodically rotating the inertial measurement unit. A properly sequenced rotational motion cancels out the maximum amount of navigation error and is known as an optimal rotation sequence. To design such an optimal turning procedure, this paper identifies the feasible rotational motions that can be implemented in a rotational inertial navigation system and analyzes the navigation error introduced by each rotational motion. In addition, by analyzing the characteristics of the navigation error generated during a rotation sequence in combination, this paper presents the conditions for designing an optimal rotation sequence.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.50-56
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• 2023

INS(Inertial Navigation System) calculates navigation information using a vehicle's acceleration and angular velocity without the outside information. However, when navigation is performed for a long time, navigation error gradually diverges and the performance decreases. To enhance INS's performance, the rotation of inertial measurement unit is developed to compensate error sources of inertial sensors, which is called RINS(Rotational Inertial Navigation System). This paper analyzes the influence of several errors for dual-axis RINS and the shows the results using simulation.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.4
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• pp.185-193
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• 2024

Visual-inertial odometry (VIO) is a method that leverages sensor data from a camera and an inertial measurement unit (IMU) for state estimation. Whereas conventional VIO has limited capability to estimate scale of translation, the performance of recent approaches has been improved by utilizing depth maps obtained from RGB-D camera, especially in indoor environments. However, the depth map obtained from the RGB-D camera tends to rapidly lose accuracy as the distance increases, and therefore, it is required to develop alternative method to improve the VIO performance in wide environments. In this paper, we argue that leveraging depth map estimated from a deep neural network has benefits to state estimation. To improve the reliability of depth information utilized in VIO algorithm, we propose a kernel-based sampling strategy to filter out depth values with low confidence. The proposed method aims to improve the robustness and accuracy of VIO algorithms by selectively utilizing reliable values of estimated depth maps. Experiments were conducted on real-world custom dataset acquired from underground parking lot environments. Experimental results demonstrate that the proposed method is effective to improve the performance of VIO, exhibiting potential for the use of depth estimation network for state estimation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.10
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• pp.1822-1829
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• 2008

Calibration of an MEMS inertial measurement unit is very important process for obtaining precise navigation performance. In this paper, one method is proposed to overcome a limitations on cost and efficiency using a relatively higher grade sensor and a rate table. The same dynamic input is applied to both the reference and the target sensors during and after calibration process, then the results are analyzed. The experimental results show that the proposed method is very effective and useful in practice.

• Journal of Sensor Science and Technology
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• v.31 no.2
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• pp.96-101
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• 2022

As the elderly population gradually increases, the risk of fatal fall accidents among the elderly is increasing. One way to cope with a fall accident is to determine the fall direction before impact using a wearable inertial measurement unit (IMU). In this context, a previous study proposed a method of classifying fall directions using a support vector machine with sensor velocity, acceleration, and tilt angle as input parameters. However, in this method, the IMU signals are processed through several processes, including a Kalman filter and the integration of acceleration, which involves a large amount of computation and error factors. Therefore, this paper proposes a machine learning-based method that classifies the fall direction before impact using IMU raw signals rather than processed data. In this study, we investigated the effects of the following two factors on the classification performance: (1) the usage of processed/raw signals and (2) the selection of machine learning techniques. First, as a result of comparing the processed/raw signals, the difference in sensitivities between the two methods was within 5%, indicating an equivalent level of classification performance. Second, as a result of comparing six machine learning techniques, K-nearest neighbor and naive Bayes exhibited excellent performance with a sensitivity of 86.0% and 84.1%, respectively.

• 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 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 Drive and Control
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• v.12 no.2
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• pp.27-33
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• 2015

Excavators are versatile earthmoving equipment that are used in civil engineering, hydraulic engineering, grading and landscaping, pipeline construction and mining. Effective operator training is essential to ensure safe and efficient operating of the machine. The virtual reality excavator based on simulation using conventional large size monitors is limited by the inability to provide a realistic real world training experience. We proposed a flexible observation method with a head tracking system to improve user feeling and sensation when operating a virtual reality excavator. First, an excavation simulator is designed by combining an excavator SimMechanics model and the virtual world. Second, a head mounted display (HMD) device is presented to replace the cumbersome large screens. Moreover, an Inertial Measurement Unit (IMU) sensor is mounted to the HMD for tracking the movement of the operator's head. These signals consequently change the virtual viewpoint of the virtual reality excavator. Simulation results were used to analyze the performance of the proposed system.

• Korean Journal of Applied Biomechanics
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• v.31 no.3
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• pp.199-204
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• 2021

Objective: The purpose of this study was to present a new idea of methodology to evaluate Poomsae performance using inertial measurement unit (IMU) sensors in terms of signal processing techniques. Method: Ten collegian Taekwondo athletes, consisting of five Poomsae elite athletes (age: 21.4 ± 0.9 years, height: 168.4 ± 11.3 cm, weight: 65.0 ± 10.6 kg, experience: 12 ± 0.7 years) and five breaking demonstration athletes (age: 21.0 ± 0.0 years, height: 168.4 ± 4.7 cm, weight: 63.8 ± 8.2 kg, experience: 13.0 ± 2.1 years), voluntarily participated in this study. They performed three different black belt Poomsae such as Goryeo, Geumgang, and Taebaek Poomsae repeatedly twice. Repeated measured motion data on the wrist and ankle were calculated by the methods of cosine similarity and Euclidean distance. Results: The Poomsse athletes showed superior performance in terms of temporal consistency at Goryeo and Taebaek Poomsae, cosine similarity at Geumgang and Taebaek Poomsae, and Euclidian distance at Geumgang Poomsae. Conclusion: IMU sensor would be a useful tool for monitoring and evaluating within-subject temporal variability of Taekwondo Poomsae motions. As well it distinguished spatiotemporal characteristics among three different Poomsae.