• International Journal of Aeronautical and Space Sciences
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• v.11 no.1
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• pp.31-40
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• 2010

For weapon cueing and Head-Mounted Display (HMD), it is essential to continuously estimate the motion of the helmet. The problem of estimating and predicting the position and orientation of the helmet is approached by fusing measurements from inertial sensors and stereo vision system. The sensor fusion approach in this paper is based on nonlinear filtering, especially expended Kalman filter(EKF). To reduce the computation time and improve the performance in vision processing, we separate the structure estimation and motion estimation. The structure estimation tracks the features which are the part of helmet model structure in the scene and the motion estimation filter estimates the position and orientation of the helmet. This algorithm is tested with using synthetic and real data. And the results show that the result of sensor fusion is successful.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.7
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• pp.693-701
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• 2010

In this paper, it is proposed that an attitude and position estimation algorithm based on a stereo camera system for a helmet tracker. Stereo camera system consists of two CCD camera, a helmet, infrared LEDs and a frame grabber. Fifteen infrared LEDs are feature points which are used to determine the attitude and position of the helmet. These features are arranged in triangle pattern with different distance on the helmet. Vision-based the attitude and position algorithm consists of feature segmentation, projective reconstruction, model indexing and attitude estimation. In this paper, the attitude estimation algorithm using UQ (Unit Quaternion) is proposed. The UQ guarantee that the rotation matrix is a unitary matrix. The performance of presented algorithm is verified by simulation and experiment.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.7
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• pp.680-687
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• 2008

This paper presents map creation algorithm and initial attitude estimation method for the proposed optical head tracker system. The optical head tracker system consists of the IR stereo cameras and infrared LEDs as features on the helmet. In order for the stereo camera to track the luminous LEDs, it must take in to account the light radiation from the LEDs to determine the position of the center points. The proposed map creation algorithm makes map data about the position of features center points on the helmet frame. Also, initial attitude estimation method is proposed to estimate the initial attitude and position of a pilot head from the camera frame by the use of the feature pattern on the helmet. Therefore, the head motion can be expressed with respect to the body frame of a flight.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.5
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• pp.470-475
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• 2009

This paper is the study which is head tracker using pattern matching. Proposal algorithm obtains initial attitude of head tracker using pattern matching. Optical head tracker consists of infrared LEDs(features) which are attached helmet as pattern, stereo infrared cameras. Proposal algorithm analyzes patterns by error rate of feature distance and estimates feature characteristic number. Initial attitude of head tracker is obtained to compare map data and feature characteristic number.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.90-95
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• 2010

This paper describes the development of integrated head and eye tracker system. Vision based head tracker is performed and it has 7mm error in 300mm translation. The epi-polar method and point matching are used for determining a position of head and rotational degree. High brightness LEDs are installed on helmet and the installed pattern is very important to match the points of stereo system. Eye tracker also uses LED for constant illumination. A Position of gazed object(3m distance) is determined by pupil tracking and eye tracker has 1~5 pixel error. Integration of result data of each tracking system is important. RS-232C communication is applied to integrated system and triggering signal is used for synchronization.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.7
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• pp.63-70
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• 2006

This paper describes research of using area of artificial mark on vision based head tracker system. A head tracker system consists of the translational and rotational motions which are detected by web camera. Results of the motion are taken from image processing and neural network. Because of the characteristics of cockpit, the specific color on the helmet is tracked for translational motion. And rotational motion is tracked via neural network. Ratio of two different colored area on the helmet is used as input of network. Neural network algorithms used, such as back-propagation and RBFN (Radial Basis Function Network). Both back-propagation using a characteristic of feedback and RBFN using a characteristic of statistics have a good performances for the tracking of nonlinear system such as a head motion. Finally, this paper analyzes and compares with tracking performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.529-535
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• 2020

It is important to secure the driver's external field view in armored vehicles surrounded by iron armor for preparation for the enemy's firepower. For this purpose, a 360 degree rotatable surveillance camera is mounted on the vehicle. In this case, the key idea is to recognize the head of the driver wearing a helmet so that the external camera rotated in exactly the same direction. In this paper, we introduce a method that uses a MEMS-based AHRS sensor and a illuminance sensor to compensate for the disadvantages of the existing optical method and implements it with low cost. The key idea is to set the direction of the camera by using the difference between the Euler angles detected by two sensors mounted on the camera and the helmet, and to adjust the direction with illuminance sensor from time to time to remove the drift error of sensors. The implemented prototype will show the camera's direction matches exactly in driver's one.

• Investigative Magnetic Resonance Imaging
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• v.18 no.2
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• pp.107-119
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• 2014

Purpose : To efficiently evaluate phased array coil performance using a software tool box with which we can make visual comparison of the sensitivity of every coil element between the real experiment and EM simulation. Materials and Methods: We have developed a $C^{{+}{+}}$- and MATLAB-based software tool called Phased Array Coil Evaluator (PACE). PACE has the following functions: Building 3D models of the coil elements, importing the FDTD simulation results, and visualizing the coil sensitivity of each coil element on the ordinary Cartesian coordinate and the relative coil position coordinate. To build a 3D model of the phased array coil, we used an electromagnetic 3D tracker in a stylus form. After making the 3D model, we imported the 3D model into the FDTD electromagnetic field simulation tool. Results: An accurate comparison between the coil sensitivity simulation and real experiment on the tool box platform has been made through fine matching of the simulation and real experiment with aids of the 3D tracker. In the simulation and experiment, we used a 36-channel helmet-style phased array coil. At the 3D MRI data acquisition using the spoiled gradient echo sequence, we used the uniform cylindrical phantom that had the same geometry as the one in the FDTD simulation. In the tool box, we can conveniently choose the coil element of interest and we can compare the coil sensitivities element-by-element of the phased array coil. Conclusion: We expect the tool box can be greatly used for developing phased array coils of new geometry or for periodic maintenance of phased array coils in a more accurate and consistent manner.