• Journal of Institute of Control, Robotics and Systems
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• v.20 no.11
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• pp.1098-1102
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• 2014

Localization of aerial vehicles and map building of flight environments are key technologies for the autonomous flight of small UAVs. In outdoor environments, an unmanned aircraft can easily use a GPS (Global Positioning System) for its localization with acceptable accuracy. However, as the GPS is not available for use in indoor environments, the development of a SLAM (Simultaneous Localization and Mapping) system that is suitable for small UAVs is therefore needed. In this paper, we suggest a vision-based SLAM system that uses vision sensors and an AHRS (Attitude Heading Reference System) sensor. Feature points in images captured from the vision sensor are obtained by using GPU (Graphics Process Unit) based SIFT (Scale-invariant Feature Transform) algorithm. Those feature points are then combined with attitude information obtained from the AHRS to estimate the position of the small UAV. Based on the location information and color distribution, a Gaussian process model is generated, which could be a map. The experimental results show that the position of a small unmanned aircraft is estimated properly and the map of the environment is constructed by using the proposed method. Finally, the reliability of the proposed method is verified by comparing the difference between the estimated values and the actual values.

• Journal of Ocean Engineering and Technology
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• v.24 no.3
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• pp.46-51
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• 2010

This paper describes the implementation of a precise underwater navigation solution using a multi-sensor fusion technique based on USBL, DVL, and IMU measurements. To implement this precise underwater navigation solution, three strategies are chosen. The first involves heading alignment angle identification to enhance the performance of a standalone dead-reckoning algorithm. In the second, the absolute position is found quickly to prevent the accumulation of integration error. The third one is the introduction of an effective outlier rejection algorithm. The performance of the developed algorithm was verified with experimental data acquired by the deep-sea ROV, Hemire, in the East-sea during a survey of a methane gas seepage area at a 1,500 m depth.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.251-256
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• 2006

In the work package 'Integrated Positioning' of the research project NAVIO (Pedestrian Navigation Systems in Combined Indoor/Outdoor Environements) we are dealing with the navigation and guidance of visitors of our University. Thereby start points are public transport stops in the surroundings of the Vienna University of Technology and the user of the system should be guided to certain office rooms or persons. For the position determination of the user different location sensors are employed, i.e., for outdoor positioning GPS and dead reckoning sensors such as a digital compass and gyro for heading determination and accelerometers for the determination of the travelled distance as well as a barometric pressure sensor for altitude determination and for indoor areas location determination using WiFi fingerprinting. All sensors and positioning methods are combined and integrated using a Kalman filter approach. Then an optimal estimate of the current location of the user is obtained using the filter. To perform an adequate weighting of the sensors in the stochastic filter model, the sensor characteristics and their performance was investigated in several tests. The tests were performed in different environments either with free satellite visibility or in urban canyons as well as inside of buildings. The tests have shown that it is possible to determine the user's location continuously with the required precision and that the selected sensors provide a good performance and high reliability. Selected tests results and our approach will be presented in the paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1986-1991
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• 2007

In this paper, we discussed, intelligent soft filter for MR(magnetoresistive) sensor. Most navigation systems today use some type of compass to determine heading direction. Using the earth's magnetic field, electronic compass based on MR(magnetoresistive) sensors can electrically resolve better then 0.1 degree rotation. Intelligent methode for soft building a one degree compass using MR(magnetoresistive) sensors will also be discussed. Compensation techniques are shown to correct for compass tilt angels and nearby ferrous material disturbances. we proved the fuzzy logic that based on the way the ham deals with inexact information is useful for MR sensors.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.7-13
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• 2021

This paper presents an vulnerable road user (VRU) classification and tracking algorithm using vision and LiDAR sensor fusion method for urban autonomous driving. The classification and tracking for vulnerable road users such as pedestrian, bicycle, and motorcycle are essential for autonomous driving in complex urban environments. In this paper, a real-time object image detection algorithm called Yolo and object tracking algorithm from LiDAR point cloud are fused in the high level. The proposed algorithm consists of four parts. First, the object bounding boxes on the pixel coordinate, which is obtained from YOLO, are transformed into the local coordinate of subject vehicle using the homography matrix. Second, a LiDAR point cloud is clustered based on Euclidean distance and the clusters are associated using GNN. In addition, the states of clusters including position, heading angle, velocity and acceleration information are estimated using geometric model free approach (GMFA) in real-time. Finally, the each LiDAR track is matched with a vision track using angle information of transformed vision track and assigned a classification id. The proposed fusion algorithm is evaluated via real vehicle test in the urban environment.

• Journal of Navigation and Port Research
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• v.41 no.4
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• pp.189-194
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• 2017

The Moving Search Radar System (MSRS) monitors sea areas by moving along the coast. Since the radar is initially aligned to the front of the vehicle, it is important to know the changes in the heading azimuth of the vehicle to quickly acquire the target azimuth from the radar after the MSRS has moved. The heading azimuth can be obtained using the gyro compass, the GPS compass or the electronic compass. The electronic compass is suitable for MSRS requiring fast maneuverability due to its small volume, short stabilization time and low price. However, using a geomagnetic sensor may result in an error due to the surrounding magnetic field. Errors can make early automatic tracking of the satellites difficult and can reduce the radar detection accuracy. Therefore, this paper proposes a method to automatically compensate for the error reflecting the correction value on the radar obtained by comparing the reference azimuth calculated by solving the geodesic inverse problem using two coordinates between the radar and the geostationary satellite with the actually-directed azimuth angle of the satellite antenna. The feasibility and convenience of the proposed method were verified by applying it to the MSRS in the field.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.9 no.2
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• pp.137-144
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• 2015

In this paper, we present an inertial sensor-based gait analysis system to measure and analyze lower-limb movements. We developed an integral AHRS(Attitude Heading Reference System) using a combination of rate gyroscope, accelerometer and magnetometer sensor signals. Several AHRS modules mounted on segments of the patient's body provide the quaternions representing the patient segments's orientation in space. And a method is also proposed for calculating three-dimensional inter-segment joint angle which is an important bio-mechanical measure for a variety of applications related to rehabilitation. To evaluate the performance of our AHRS module, the Vicon motion capture system, which offers millimeter resolution of 3D spatial displacements and orientations, is used as a reference. The evaluation resulted in a RMSE(Root Mean Square Error) of 1.08 and 1.72 degree in yaw and pitch angle. In order to evaluate the performance of our the gait analysis system, we compared the joint angle for the hip, knee and ankle with those provided by Vicon system. The result shows that our system will provide an in-depth insight into the effectiveness, appropriate level of care, and feedback of the rehabilitation process by performing real-time limb or gait analysis during the post-stroke recovery.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.6
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• pp.33-42
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• 2010

This research propose a practical guidance system considering ocean currents in real sea operation. Optimality of generated path is not an issue in this paper. Way-points from start point to possible goal positions are selected by experienced human supervisors considering major ocean current axis. This paper also describes the implementation of a precise underwater navigation solution using multi-sensor fusion technique based on USBL, GPS, DVL and AHRS measurements in detail. To implement the precise, accurate and frequent underwater navigation solution, three strategies are chosen. The first one is the heading alignment angle identification to enhance the performance of standalone dead-reckoning algorithm. The second one is that absolute position is fused timely to prevent accumulation of integration error, where the absolute position can be selected between USBL and GPS considering sensor status. The third one is introduction of effective outlier rejection algorithm. The performance of the developed algorithm is verified with experimental data of mine disposal vehicle and deep-sea ROV.

• Current Research on Agriculture and Life Sciences
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• v.32 no.2
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• pp.79-84
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• 2014

The precision aerial application of agricultural unmanned helicopters has become a new paradigm for small farms with orchards, paddy, and upland fields. The needs of agricultural applications require easy and affordable control systems. Recent developments of MEMS technology based on inertial sensors and high speed DSP have enabled the fabrication of low-cost attitude system. Therefore, this study evaluates inertial MEMS sensors for estimating the attitude of an agricultural unmanned helicopter. The accuracies and errors of gyro and acceleration sensors were verified using a pendulum system. The true motion values were calculated using a theoretical estimation and absolute encoder measurement of the pendulum, and then the sensor output was compared with reference values. When comparing the sensor measurements and true values, the errors were determined to be 4.32~5.72%, 3.53~6.74%, and 3.91~4.16% for the gyro rate and x-, z- accelerations, respectively. Thus, the measurement results confirmed that the inertial sensors are effective for establishing an attitude and heading reference system (AHRES). The sensors would be constructed in gimbals for the estimating and proving attitude measurements in the following paper.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1099-1104
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• 2003

In this paper, the position control of a car-like mobile robot using neural network is proposed. The positional information of the mobile robot is given by a laser range finder located remotely through wireless communication. The heading angle is measured by a gyro sensor. Considering these two sensor information as references, the robot posture by localization is corrected by a cascaded controller. In order to improve the tracking performance, a neural network with a cascaded controller is used to compensate for any uncertainty in the robot. The remotely located neural network filter modifies the reference trajectories to minimize the positional errors by wireless communication. A car-like mobile robot is built as a test-bed and experimental studies of proposed several control algorithms are performed. It turns out that the best position control can be achieved by a cascaded controller with neural network.