• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.271-275
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• 2006

This paper describes the design and realization of full attitude system based on MEMS IMU and GPS carrier phase. The work can be divided into two parts: First, initial heading is determined by using two GPS receivers. And this paper discusses the usage of space geometry conditions to reduce the range of ambiguity search. The method presented in this paper was tested on the static. On the static condition, an accuracy better than 0.06 degrees for heading for 3.48m long baseline has been achieved. Integration of GPS and low cost MEMS IMU are used to realize the real-time heading attitude system. Second, level attitude (pitch and roll) is determined using the method of frequency-velocity for the feedback control. At the same time, the method using the attitude based on MEMS IMU to help determination of the range of ambiguity search is proposed. The results done on the sea show that an alternative means to provide real-time, cost-effective, accurate and reliable attitude information for attitude surveys. Though motivated by a big ships application, the design can be applied to other vehicles.

• 센서학회지
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• 제29권6호
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• pp.440-446
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• 2020

A well-known difficulty in attitude estimation based on inertial measurement unit (IMU) signals is the occurrence of external acceleration under dynamic motion conditions, as the acceleration significantly degrades the estimation accuracy. Lee et al. (2012) designed a Kalman filter (KF) that could effectively deal with the acceleration issue. Ahmed and Tahir (2017) modified this method by adjusting the acceleration-related covariance matrix because they considered covariance modeling as a pivotal factor in the estimation accuracy. This study investigates the effects of covariance modeling on estimation accuracy in an IMU-based attitude estimation KF. The method proposed by Ahmed and Tahir can be divided into two: one uses the covariance including only diagonal components and the other uses the covariance including both diagonal and off-diagonal components. This paper compares these three methods with respect to the motion condition and the window size, which is required for the methods by Ahmed and Tahir. Experimental results showed that the method proposed by Lee et al. performed the best among the three methods under relatively slow motion conditions, whereas the modified method using the diagonal covariance with a high window size performed the best under relatively fast motion conditions.

• 로봇학회논문지
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• 제14권1호
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• pp.65-73
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• 2019

In this paper, we propose an algorithm that estimates the location of lunar rover using IMU and vision system instead of the dead-reckoning method using IMU and encoder, which is difficult to estimate the exact distance due to the accumulated error and slip. First, in the lunar environment, magnetic fields are not uniform, unlike the Earth, so only acceleration and gyro sensor data were used for the localization. These data were applied to extended kalman filter to estimate Roll, Pitch, Yaw Euler angles of the exploration rover. Also, the lunar module has special color which can not be seen in the lunar environment. Therefore, the lunar module were correctly recognized by applying the HSV color filter to the stereo image taken by lunar rover. Then, the distance between the exploration rover and the lunar module was estimated through SIFT feature point matching algorithm and geometry. Finally, the estimated Euler angles and distances were used to estimate the current position of the rover from the lunar module. The performance of the proposed algorithm was been compared to the conventional algorithm to show the superiority of the proposed algorithm.

• 센서학회지
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• 제31권2호
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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.

• 로봇학회논문지
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• 제18권4호
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• pp.427-435
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• 2023

This paper proposes a method of estimating the pose of a mobile robot by using a learning model. When estimating the pose of a mobile robot, wheel encoder and inertial measurement unit (IMU) data are generally utilized. However, depending on the condition of the ground surface, slip occurs due to interaction between the wheel and the floor. In this case, it is hard to predict pose accurately by using only encoder and IMU. Thus, in order to reduce pose error even in such conditions, this paper introduces a pose estimation method based on a learning model using data of the wheel encoder and IMU. As the learning model, long short-term memory (LSTM) network is adopted. The inputs to LSTM are velocity and acceleration data from the wheel encoder and IMU. Outputs from network are corrected linear and angular velocity. Estimated pose is calculated through numerically integrating output velocities. Dataset used as ground truth of learning model is collected in various ground conditions. Experimental results demonstrate that proposed learning model has higher accuracy of pose estimation than extended Kalman filter (EKF) and other learning models using the same data under various ground conditions.

• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2009년도 춘계학술대회
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• pp.235-236
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• 2009

실시간 공중 자료획득 시스템은 재난 재해와 같은 긴급 상황에서 빠르게 자료를 취득하여 대상 지역의 정사 영상과 같은 공간정보를 취득하는 시스템이다. 이러한 시스템에서 GPS와 INS는 플랫폼의 위치와 자세정보를 획득 하는데 중요한 역할을 하며 이번 연구에서는 GPS/MEMS IMU 센서의 성능 평가를 실측 데이터를 통하여 실시간 공중 자료획득 시스템에 대한 적합성을 평가하였다.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2005년도 추계학술대회 논문집
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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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.990-994
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• 2003

When the low cost IMU is used, the result of the stationary self alignment is not suitable for navigation. In this paper, an in-motion alignment method is proposed to obtain an accurate initial attitude of a low cost IMU based GPS/INS integration system. To design Kalman filter for alignment, large heading error model is introduced. And then Kalman filter is designed to estimate initial attitude error as the indirect feedback filter. In order to assess performance of the alignment method, computer simulations are carried out. The simulation results show that initial attitude error rapidly reduces.

• 제어로봇시스템학회논문지
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• 제19권7호
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• pp.640-645
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• 2013

This paper presents a step length estimation algorithm for Pedestrian Dead Reckoning using linear calibrated ZUPT (zero velocity update) with a foot mounted IMU. The IMU consists of 3 axis accelerometer, gyro and magnetometer. Attitude of IMU is estimated using an inertial navigation algorithm. To increase accuracy of step length estimation algorithm, we propose a stance detection algorithm and an enhanced ZUPT. The enhanced ZUPT calculates firefighter's step length considering velocity error caused by sensor bias during one step. This algorithm also works efficiently at various motions, such as crawling, sideways and stair stepping. Through experiments, the step length estimation performance of the proposed algorithm is verified.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2004년도 Asia Navigation Conference
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• pp.45-53
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• 2004

In this paper, an INS compensation algorithm for auto sailing system is proposed, where low cost IMU (Inertial Measurement Unit) is used for measuring the accelerometer data. First, we denote the basic INS algorithm with IMU and show that how to compensate the error of position by using low cost IMU. Second, in considering the ship's characteristic and ocean environments, we consider with a factor as a periodic external disturbance which effects to the exact position. To develop the compensation algorithm, we use a repetitive method to reduce the external environment changes. Lastly, we verify the proposed algorithm by using experiments results.