• The Journal of Korea Robotics Society
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• v.6 no.4
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• pp.334-343
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• 2011

This paper presents a new approach for mobile robot heading detection using MEMS Gyro north finding method in the indoor environment. Based on this, the robot heading angle measurement scheme is proposed; improved north finding theory and algorithm are also explained. Several approaches are applied to confirm system's precision and effectiveness. In order to find out the heading angle, a single axis MEMS gyroscope to sense the angle between the robot heading direction and the north is used. To reach enough estimation accuracy and reduce detection time, the least square method (LSM) for the signal fitting and parameter estimation is applied. Through a turn-table, we setup a carouseling system to decrease the substantial bias effect on gyroscope's heading angle. For the evaluation of the proposed method, this system is implemented to the Pioneer robot platform. The performance and heading error are analyzed after the test. From the simulation and experimental results, system's accuracy, usefulness and adaptability are shown.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.6
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• pp.470-476
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• 2016

This paper proposes a practical algorithm for the reduction of measurement errors due to drift in a micro-electromechanical system (MEMS) gyros that are used for a mobile robot. Any drift in a MEMS gyro will cause an unbounded growth of errors in the estimation of heading, which makes it nearly useless in applications that require high accuracy over a long operating time. In proposed method, maneuvers of a cleaning robot are observed through encoders' measurement process and a decision to correct bias drift will be made if necessary. The method used in this paper is called the "heading estimation filter". To evaluate the accuracy of the proposed method, a comparison was made between the estimation of the heading of the cleaning robot and one from a motion capture system.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.7
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• pp.1068-1079
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• 2008

Tilt and heading angles information of a remotely operated vehicle (ROV) are very important in underwater navigation. This paper presents a low.cost tilt and heading estimation system. Three single.axis rate gyros, a tri-axis accelerometer, and a tri-axis magnetometer are used. Output signals coming from these sensors are fused by two Kalman filters. The first Kalman filter is used to estimate roll and pitch angles and the other is for heading angle estimation. By using this method, we have obtained tilt (roll and pitch angles) and heading information which are reliable over long period of time. Results from experiments have shown the performance of the presented system.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1675-1682
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• 2017

This paper deals with three dimensional orientation estimation algorithm for an attitude and heading reference system (AHRS) based on nine-axis inertial/magnetic sensor signals. In terms of the orientation estimation based on the use of a Kalman filter (KF), the quaternion is arguably the most popular orientation representation. However, one critical drawback in the quaternion representation is that undesirable magnetic disturbances affect not only yaw estimation but also roll and pitch estimations. In this paper, a sequential direction cosine matrix-based orientation KF for AHRS has been presented. The proposed algorithm uses two linear KFs, consisting of an attitude KF followed by a heading KF. In the latter, the direction of the local magnetic field vector is projected onto the heading axis of the inertial frame by considering the dip angle, which can be determined after the attitude KF. Owing to the sequential KF structure, the effects of even extreme magnetic disturbances are limited to the roll and pitch estimations, without any additional decoupling process. This overcomes an inherent issue in quaternion-based estimation algorithms. Validation test results show that the proposed method outperforms other comparison methods in terms of the yaw estimation accuracy during perturbations and in terms of the recovery speed.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.11
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• pp.2021-2032
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• 1992

A redundant sensor system, which consists of two incremental encoders and a gyro sensor, has been proposed for the estimation of the posture of mobile robots. A hardware system was built for estimating the heading angle change of the mobile robot from outputs of the gyro sensor. The proposed hardware system of the gyro sensor produced an accurate estimate for the heading angle change of the robot. A sensor data fusion algorithm has been developed to find the optimal estimates of the heading angle change based on the stochastic measurement equations of our readundant sensor system. The maximum likelihood estimation method is applied to combine the noisy measurement data from both encoders and gyro sensor. The proposed fusion algorithm demonstrated a satisfactory performance, showing significantly reduced estimation error compared to the conventional method, in various navigation experiments.

• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.1
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• pp.84-92
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• 2015

This paper proposes a new approach to reduce the target estimation error of the measurement angle, especially applied to the medium and long range surveillance radar. If the target has no maneuver and no change in heading direction for a certain time interval, the predicted angle of interacting multiple model(IMM) from the previous track information can be used to reduce the angle estimation error. The proposed method is simulated in 2 scenarios, a scenario with a non-maneuvering target and a scenario with a maneuvering target. The result shows that the new fusion solution(weighted IMM) with the predicted azimuth and the measured azimuth is worked properly in the two scenarios.

• The Journal of Korea Robotics Society
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• v.5 no.4
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• pp.279-285
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• 2010

This paper presents the use of 3 axis accelerometer for getting the gait information including the number of gaits, stride and walking distance. Travel distance is usually calculated from the double integration of the accelerometer output with respect to time; however, the accumulated errors due to the drift are inevitable. The orientation change of the accelerometer also causes error because the gravity is added to the measured acceleration. Unless three axis orientations are completely identified, the accelerometer alone does not provide correct acceleration for estimating the travel distance. We proposed a way of minimizing the error due to the change of the orientation. Pedestrian localization is implemented with the heading angle and the travel distance. Heading angle is estimated from the rate gyro and the magnetic compass measurements. The performance of the localization is presented with experimental data.

• Journal of Hydrospace Technology
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• v.1 no.1
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• pp.75-88
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• 1995

Auto-Pilot System uses heading angle information via the position sensor and the rudder device to control the ship's direction. Most of the control logics are composed of the state estimation and control algorithms assuming that the measurement device and the actuator have no fault except the measurement noise. But such asumptions could bring the danger in real situation. For example, if the heading angle measuring device is out of order the control action based on those false position information could bring serious safety problem. In this study, the control system including improved method for processing the position information is applied to the Auto-Pilot System. To show the difference between general state estimator and F.D.F., BJDFs for the sensor and the actuator failure detection are designed and the performance are tested. And it is shown that bias error in sensor could be detected by state-augmented estimator. So the residual confined in the 2-dimension in the presence of the sensor failure could be unidirectional in output space and bias sensor error is much easier to be detected.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.130-138
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• 2024

High-precision 3D Object Detection is a crucial component within autonomous driving systems, with far-reaching implications for subsequent tasks like multi-object tracking and path planning. In this paper, we propose a novel approach designed to enhance the performance of 3D Object Detection, especially in heading angle estimation by employing a moving object segmentation technique. Our method starts with extracting point-wise moving labels via a process of moving object segmentation. Subsequently, these labels are integrated into the LiDAR Pointcloud data and integrated data is used as inputs for 3D Object Detection. We conducted an extensive evaluation of our approach using the KITTI-road dataset and achieved notably superior performance, particularly in terms of AOS, a pivotal metric for assessing the precision of 3D Object Detection. Our findings not only underscore the positive impact of our proposed method on the advancement of detection performance in lidar-based 3D Object Detection methods, but also suggest substantial potential in augmenting the overall perception task capabilities of autonomous driving systems.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.4
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• pp.414-421
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• 2014

This paper presents a method of ballistic missile launch point estimation using phase division least squares. The proposed algorithm employs smoothing to enhance estimation accuracy and generates functions of time for total velocity, flight path angle and heading angle, allowing extrapolation to estimate the launch point. Performance of the proposed algorithm is tested in conjunction with the extended Kalman filter and the Kalman filter.