• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.167-181
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• 2024

Utilization of a driving simulator in the development of autonomous driving technology allows us to perform various tests effectively in criticial environments, thereby reducing the development cost and efforts. However, there exists a serious drawback that the driving simulator has a big difference from the real environment, so a problem occurs when the autonomous driving algorithm developed using the driving simulator is applied directly to the real vehicle system. This is defined as so-called Sim2Real problem and can be classified into scenarios, sensor modeling, and vehicle dynamics. This Paper presensts on a method to solve the Sim2Real problem in autonomous driving simulator focusing on IMU sensor. In order to reduce the difference between emulated virtual IMU sensor real IMU sensor, IMU sensor emulation techniques through precision error modeling of IMU sensor are introduced. The error model of IMU sensors takes into account bias, scale factor, misalignmnet, and random walk by IMU sensor grades.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.8
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• pp.744-749
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• 2012

In this paper, we suggest that how to improve an accuracy of mobile robot's localization by using the sensor network information which fuses the machine vision camera, encoder and IMU sensor. The heading value of IMU sensor is measured using terrestrial magnetism sensor which is based on magnetic field. However, this sensor is constantly affected by its surrounding environment. So, we isolated template of ceiling using vision camera to increase the sensor's accuracy when we use IMU sensor; we measured the angles by pattern matching algorithm; and to calibrate IMU sensor, we compared the obtained values with IMU sensor values and the offset value. The values that were used to obtain information on the robot's position which were of Encoder, IMU sensor, angle sensor of vision camera are transferred to the Host PC by wireless network. Then, the Host PC estimates the location of robot using all these values. As a result, we were able to get more accurate information on estimated positions than when using IMU sensor calibration solely.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.1
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• pp.65-72
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• 2010

In this study, the performances of a medium grade IMU which is aimed for Mobile Mapping System and a low grade IMU for pedestrian navigation are analyzed through simulations under GPS signal blockage. In addition, an analysis on the accuracy improvement of barometer, electronic compass, or multi-sensor(combination of barometer and electronic compass) to correct medium grade or low grade IMU errors in the situation of GPS signal blockage is performed. With the medium grade IMU, the three dimensional positioning error from INS exceeds the demanded accuracy of 5m when the block time is over 30 seconds. When we correct IMU with barometer, compass, or multi-sensor, however, the demanded accuracy is maintained up to 60 seconds. In addition, barometer is more effective than the electronic compass when they are combined. In case of low grade IMU like MEMS IMU, the three dimensional positioning error from INS exceeds the demanded accuracy of 20m when the block time is over 15 seconds. When we correct INS with barometer, compass, or multi-sensor, however, the demanded accuracy is maintained up to 15 seconds in simulation results. On the contrary to medium grade IMU, electronic compass is more effective than the barometer in case of low velocity such as pedestrian navigation. It is expected that the analysis suggested a method to decrease position or attitude error using aided sensor integration when MMS or pedestrian navigation is operated under 1he environment of GPS signal blockage.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.186-192
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• 2014

This study presents the remote control of a board robot using an IMU sensor based on Bluetooth communication. The board robot is a kind of riding robot controlled throng wireless communication by a user. The user wears the proposed IMU sensor controller, and changes a direction of the robot by the angles of IMU sensor. Bluetooth is used for wireless communication between the board robot and its user. The IMU sensor in the remote controller is used for recognition of a number of actions, which are measured as analog signals. The user actions have five commands ('1'right '2'neutrality '3'left '4'operation '5'stop), which are transmitted from the user to the board robot through Bluetooth communication. Experimental results show that proposed IMU interface can effectively control the board robot.

• Journal of Navigation and Port Research
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• v.37 no.3
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• pp.309-313
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• 2013

This paper is about a complex sensor system of an automatic guided vehicle(AGV) for loading and unloading payloads. For the AGV to approach to the target rack for loading and unloading the payload, a way to identify the position and orientation was studied. To identify the position and orientation of the AGV accurately, a complex sensor system composed of RFID, IMU, and limit sensors was developed, and the performance of each sensor was undertaken. A model AGV was constructed, and the good performance of the developed complex sensor system was verified through performance experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.3011-3024
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• 2021

Pose estimation of the sensor is important issue in many applications such as robotics, navigation, tracking, and Augmented Reality. This paper proposes visual-inertial integration system appropriate for dynamically moving condition of the sensor. The orientation estimated from Inertial Measurement Unit (IMU) sensor is used to calculate the essential matrix based on the intrinsic parameters of the camera. Using the epipolar geometry, the outliers of the feature point matching are eliminated in the image sequences. The pose of the sensor can be obtained from the feature point matching. The use of IMU sensor can help initially eliminate erroneous point matches in the image of dynamic scene. After the outliers are removed from the feature points, these selected feature points matching relations are used to calculate the precise fundamental matrix. Finally, with the feature point matching relation, the pose of the sensor is estimated. The proposed procedure was implemented and tested, comparing with the existing methods. Experimental results have shown the effectiveness of the technique proposed in this paper.

• Journal of Integrative Natural Science
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• v.17 no.3
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• pp.113-122
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• 2024

Advancements in sensor technology, particularly Inertial Measurement Units (IMU), are crucial in modern pose estimation. IMUs typically consist of accelerometers and gyroscopes (6-axis), with some models including magnetometers (9-axis). This study investigates the impact of sensor frequency on pose estimation accuracy using data from a 256Hz IMU sensor. The data sets analyzed include "spiralStairs," "stairsAndCorridor," and "straightLine," with frequencies varied to 128Hz, 64Hz, and 32Hz, and conditions categorized as stationary or dynamic. The results indicate that sensitivity remains high at lower frequencies under stationary conditions but declines in dynamic conditions. Performance comparison, based on Root Mean Square Error (RMSE) values, showed that lower frequencies lead to increased RMSE, thus diminishing model accuracy. Additionally, the Extended Kalman Filter (EKF) was tested as an alternative to Madgwick's algorithm but faced challenges due to insufficient sensor noise data.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.920-922
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• 2018

This paper describes a method to classify simple circular artificial markers on surfaces of a box on the back of hand to detect the pose of user's hand for VR/AR applications by using a Leap Motion camera and two IMU sensors. One IMU sensor is located in the box and the other IMU sensor is fixed with the camera. Multi-layer Perceptron (MLP) algorithm is adopted to classify artificial markers on each surface tracked by the camera using IMU sensor data. It is experimented successfully in real-time, 70Hz, under PC environments.

• Journal of Practical Engineering Education
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• v.10 no.2
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• pp.119-124
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• 2018

Educational equipment that enables the user or device to recognize the indoor position by using the acceleration and angular velocity of the IMU (Inertial Measurement Unit) sensor is introduced. With this educational equipment, various position recognition and tracking algorithms can be learned and creative engineering design works can be realized. The data value of the IMU sensor is transmitted to the MCU (microcontroller unit) through $I^2C$ (Inter-Integrated Circuit), and the indoor position recognition algorithm is applied by processing the data value through the filter and numerical method. It is then designed to use wireless communication to send and receive processed values and to be recognized by the user. As an example using this equipament, the case of "Implementation and recognition of virtual position using computation of moving direction and distance using IMU sensor" is introduced, and various creative engineering design application is discussed.

• Journal of Sensor Science and Technology
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• v.27 no.2
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• pp.86-92
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• 2018

In recent times, motion capture technology using inertial measurement unit (IMU) sensors has been actively used in sports. In this study, we developed a canoe paddle, installed with an IMU and a water level sensor, as a system tool for training and calibration purposes in water sports. The hardware was fabricated to control an attitude heading reference system (AHRS) module, a water level sensor, a communication module, and a wireless charging circuit. We also developed an application program for the mobile device that processes paddling motion data from the paddling operation and also visualizes it. An AHRS module with acceleration, gyro, and geomagnetic sensors each having three axes, and a resistive water level sensor that senses the immersion depth in the water of the paddle represented the paddle motion. The motion data transmitted from the paddle device is internally decoded and classified by the application program in the mobile device to perform visualization and to operate functions of the mobile training/correction system. To conclude, we tried to provide mobile knowledge service through paddle sport data using this technique. The developed system works reasonably well to be used as a basic training and posture correction tool for paddle sports; the transmission delay time of the sensor system is measured within 90 ms, and it shows that there is no complication in its practical usage.