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Motion and Structure Estimation Using Fusion of Inertial and Vision Data for Helmet Tracker
Sejong Heo,Ok shik Shin,Chan Gook Park 한국항공우주학회 2010 International Journal of Aeronautical and Space Sc Vol.11 No.1
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.
Consistent EKF-Based Visual-Inertial Navigation Using Points and Lines
Heo, Sejong,Jung, Jae Hyung,Park, Chan Gook IEEE 2018 IEEE SENSORS JOURNAL Vol.18 No.18
<P>In this paper, we present a novel visual-inertial navigation system (VINS) algorithm using points and lines for low cost and computationally constrained systems in GPS-denied environments. Generally, extended Kalman filter (EKF)-based VINS algorithms exploit points as visual information and suffer from an inconsistent state estimates resulting in obtaining spurious information along the unobservable direction, especially along the rotation about the gravity direction. While point features are simple and rich visual information, line features are alternative visual information in low-texture environments, such as indoors or urban areas. To improve the robustness and consistency, we simultaneously exploit the points and lines as visual information for the VINS algorithm and model the state space as a matrix Lie group, based on the theory of the invariant EKF. In particular, as the main theoretical contributions of this paper, we employ the line observations to the VINS algorithm on the matrix Lie group and analytically derive the right null space of the corresponding observability matrix for the first time. By leveraging this analysis, we prove that it has a consistent property for the rotation about the gravity direction without any artificial remedies. Therefore, the proposed VINS algorithm on the matrix Lie group using points and lines naturally enforces the state vector to remain in the unobservable subspace. The performance of the proposed method is validated through Monte-Carlo simulations and real-world experiments.</P>
Consistent EKF-Based Visual-Inertial Odometry on Matrix Lie Group
Heo, Sejong,Park, Chan Gook IEEE 2018 IEEE SENSORS JOURNAL Vol.18 No.9
<P>In this paper, we present a novel visual-inertial navigation algorithm for low-cost and computationally constrained vehicle in global positioning system denied environments by modeling the state space as the matrix Lie group (LG), based on the recent theory of the invariant Kalman filter. The multistate constraint Kalman filter (MSCKF) is a well-known visual-inertial odometry algorithm that performs the fusion of the visual and inertial information by constraining each other through the stochastically cloned pose within a sliding window. However, conventional MSCKF (MSCKF-Conv) suffers from the inconsistent state estimates caused by the spurious gain along the unobservable directions, resulting in large estimation errors. To tackle this problem, we extend the concepts of the state and noise of the MSCKF from Euclidean space to matrix LG. We model the state of the MSCKF as the element of the specially customized matrix LG and use the noise uncertainty modeling with the corresponding Lie algebra. The detailed derivation and observability analysis of the proposed filter are provided to prove that the proposed filter is more consistent than the MSCKF-Conv. The proposed MSCKF on matrix LG naturally enforces the state vector to exist in the state space that maintains the unobservability characteristics without any artificial remedies. The performance of the proposed filter is validated through the Monte-Carlo simulation and the real-world experimental dataset.</P>
Coarse Alignment of Lunar Exploration Rover Using Accelerometer and Sun Sensor
Jaehyuck Cha,Sejong Heo,Chan Gook Park 제어로봇시스템학회 2017 제어로봇시스템학회 국제학술대회 논문집 Vol.2017 No.10
Lunar rover plays a key role in lunar exploration based on its maneuverability. For the successful operation of the lunar rover, a high-accuracy navigation technique has to be obtained. In order to perform high-accuracy inertial navigation, the accuracy of initial alignment is important. In general, initial alignment can be divided into two processes, coarse and fine alignment. In the general coarse alignment process, the acceleration and rotation rate of the stationary rover are used as reference vectors. However, the acceleration and rotation rate of the Moon are 1/6 and 1/27 times smaller than those of the Earth, respectively. As a result, even though the same sensors are used, the performance of initial coarse alignment gets worse on the Moon. In this paper, three coarse alignment methods using accelerometers and sun sensor, instead of accelerometers and gyros, are proposed and the associated errors are analyzed. For comparison, the existing general coarse alignment algorithms are summarized. The analyses are verified by appropriate computer simulations, and it shows that the proposed algorithms greatly improve the performance of coarse alignment, outperforming the existing algorithms at least on the Moon. Therefore, the proposed algorithm is suitable for lunar rover application, and can also be applied to other planetary explorations.
신옥식(Okshik Shin),허세종(Sejong Heo),박찬국(Chan Gook Park) 한국항공우주학회 2010 韓國航空宇宙學會誌 Vol.38 No.7
본 논문에서는 스테레오 카메라 시스템을 이용하여 헬멧의 자세 및 위치를 추정하는 알고리즘을 제안한다. 본 논문에서 구축한 시스템은 두 대의 CCD카메라와 헬멧 그리고 적외선 LED, 영상편집 보드로 구성된다. 이 중 15개의 적외선 LED는 헬멧에 서로 다른 길이로 삼각형 패턴으로 고정되어, 헬멧의 자세 및 위치를 결정하기 위한 특징점이 된다. 본 논문에서 제안한 알고리즘은 특징점 추출, 투영 재구성, 모델 인덱싱 과정으로 구성되며, 단위 쿼터니언(UQ, Unit Quaternion)을 이용하여 자세 및 위치를 추정한다. UQ를 이용하여 회전행렬를 구하면, 회전 행렬이 유니터리 행렬(Unitary Matrix)이 되는 것을 보장할 수 있다. 제안된 알고리즘은 시뮬레이션과 실제 실험 데이터를 이용하여 그 성능을 검증하였다. 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.