• 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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.428-433
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• 1999

The errors of IMU(Inertial Measurement Unit) can be divided into deterministic and random errors. Since the required accuracy of the IMU is very high, the errors must be compensated by using an accurate error mode. In this paper, we present a method to get a more accurate error model in a laboratory test. This was done by considering the setting misalignment in the laboratory test in the IMU error model. We considered here the IMU which consits of DTG(dynamically tuned gyroscope) and pendulum type accelerometer. First, it was shown that the estimation result from the model which does not contain the setting misalignment gives considerable estimation error at the validation test. Second, a new model considering the setting misalignment was derived. Finally, by validation test using the estimation results from new model the validity of it was proved.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.1
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• pp.17-25
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• 2023

As the range of use of urban unmanned aerial vehicles (UAV) expands, it is necessary to operate UAVs efficiently because of its limited battery capacity. For this, it is required to find the optimal flight profile with various simulations. Therefore, it is important to predict the power and energy consumption of the UAV battery. In this paper, we analyzed the relationship between the speed and acceleration of the UAV and power consumption during the flight. Then, we derived a linear model, which is easily utilized. In addition, we also derived an accurate power consumption model based on deep neural network learning. To find the efficient model, we used learning data as 1) the GPS 3-axis velocity and acceleration data, 2) the IMU 3-axis velocity only, and 3) the IMU 3-axis velocity and acceleration data. The final model shows 5.86% error rate for power consumption and 1.50% error rate for the cumulative energy consumption.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.10
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• pp.64-76
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• 1998

In this paper, we propose the dynamic modeling, path planning and tracking algorithms of 4-wheeled 2-d.o.f.(degree of freedom) mobile robot(WMR). The gaussian functions are applied to design the smooth path of WMR. To calculate the WMR position in real time, we use three components of inertial measurement units(IMU). These units have initial error because of the rotation rate of earth, gravity acceleration and so on. Therefore we derive the initial error model of IMU, and compare the fitness diagnosis about probability characteristics of real data adn estimated data. The performance of IMU with error model and Kalman filter is compared to that without filter and error model. The simulation results show that the proposed dynamic model, path planning and tracking algorithms are more useful than the conventional control algorithm.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.8
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• pp.566-573
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• 2019

In order to obtain high-quality images by motion compensation in the airborne synthetic aperture radar (SAR), accurate motion sensing in image acquisition section is necessary. Especially, reducing relative position error and discontinuity in motion sensing is important. To overcome the problem, we propose a pre-parametric error modeling (P-PEM) algorithm which is a real-time motion sensing algorithm for the airborne SAR in this paper. P-PEM is an extended version of parametric error modeling (PEM) method which is a motion sensing algorithm to mitigate the errors in the previous work. PEM estimates polynomial coefficients of INS error which can be assumed as a polynomial in the short term. Otherwise, P-PEM estimates polynomial coefficients in advance and uses at image acquisition section. Simulation results show that the P-PEM reduces relative position error and discontinuity effectively in real-time.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.3
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• pp.311-320
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• 2008

A LIDAR can rapidly generate 3D points by densely sampling the surfaces of targets using laser pulses, which has been efficiently utilized to reconstruct 3D models of the targets automatically. Due to this advantage, LIDARs are increasingly applied to the fields of Defense and Security, for examples, being employed to intelligently guided missiles and manned/unmanned reconnaissance planes. For the prior verification of the LIDAR applicability, this study aims at generating simulated LIDAR data. Here, we derived the sensor equation by modelling the geometric relationships between the LIDAR sub-modules, such as GPS, IMU, LS and the systematic errors associated with them. Based on this equation, we developed a program to generate simulated data with the system parameters, the systematic errors, the flight trajectories and attitudes, and the reference terrain model given. This program had been applied to generating simulated LIDAR data for urban areas. By analyzing these simulated data, we verified the accuracy and usefulness of the simulation. The simulator developed in this study will provide economically various test data required for the development of application algorithms and contribute to the optimal establishment of the flight and system parameters.