Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Pose Estimation Method Using Sensor Fusion based on Extended Kalman Filter

센서 결합을 이용한 확장 칼만 필터 기반 자세 추정 방법

  • Received : 2016.06.23
  • Accepted : 2017.01.22
  • Published : 2017.02.25
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Abstract

In this paper, we propose the method of designing an extended kalman filter in order to accurately measure the position of the spatial-phase system using sensor fusion. We use the quaternion as a state variable in expressing the attitude of an object. Then, the attitude of rigid body can be calculated from the accelerometer and magnetometer by applying the Gauss-Newton method. We estimate the changes of state by using the measurements obtained from the gyroscope, the quaternion, and the vision informations by ARVR_SDK. To increase the accuracy of estimation, we designed and implemented the extended kalman filter, which showed excellent ability to adjust and compensate the sensor error. As a result, we could experimentally demonstrate that the reliability of the attitude estimation value can be significantly increased.

본 논문에서는 센서 결합을 이용하여 공간상의 시스템 자세를 정확히 추정할 수 있는 확장 칼만 필터를 설계하는 방법에 대해서 제안한다. 시스템 자세는 쿼터니언 상태 변수를 이용하여 표현하며, 이는 Gauss-Newton방법을 적용하여 가속도 센서와 지자기 센서로 부터 강체의 자세를 획득 하게 된다. 측정된 쿼터니언 값과 속도 센서 값, ARVR_SDK에 의한 영상 정보 값을 이용함으로써, 상태 변화를 추정 하게 되는데, 자세 추정의 정밀도를 높이기 위해 입력 값에 대한 에러를 보정하는 과정을 추가하여 적응적으로 입력 값을 조절하는 확장 칼만 필터를 설계 적용 하였다. 그 결과, 설계된 필터에 입력 값에 대한 오차가 있어도 일정부분 이를 보정하여 추정 값에 대한 신뢰도를 높이는 결과를 실험적으로 확인 할 수 있었다.

Keywords

References

  1. Kato, H., Billinghurst, M., "Marker tracking and hmd calibration for a video-based augmented reality conferencing system." Augmented Reality, 1999.(IWAR'99) Proceedings. 2nd IEEE and ACM International Workshop on. IEEE, pp. 85-94, 1999.
  2. Daniel, W., Schmalstieg, D., "ARToolKitPlus for pose tracking on mobile devices." Proceedings of 12th Computer Vision Winter Workshop. pp. 139-146, 2007.
  3. Vuforia Developer, Vuforia SDK 5.5, (https: //developer.vuforia.com/downloads/sdk), 2016.
  4. Rekimoto, J., Yuji A., "CyberCode: designing augmented reality environments with visual tags." Proceedings of DARE 2000 on Designing augmented reality environments. ACM, pp. 1-10, 2000.
  5. LotusEco, ARVR_SDK 1.0, 2016.
  6. Drews, T. M., Kry, P. G., Forbes, J. R., Verbrugge, C., "Sequential Pose Estimation Using Linearized Rotation Matrices", In Computer and Robot Vision (CRV), 2013 International Conference on. IEEE, pp. 113-120, 2013.
  7. Kumar, K., Varghese, A., Reddy, P.K., Narendra, N., Swamy, P., Chandra, M.G., Balamuralidhar, P., "An Improved Tracking using IMU and Vision Fusion for Mobile Augmented Reality Applications.", International Journal of Multimedia and its Applications, Vol. 6, No.5, pp. 13-29, 2014. https://doi.org/10.5121/ijma.2014.6502
  8. Ligorio, G., Sabatini, A. M., "Extended Kalman Filter-Based Methods for Pose Estimation Using Visual, Inertial and Magnetic Sensors: Comparative Analysis and Performance Evaluation.", Sesors, 13, pp. 191-1941, 2013.
  9. Servant, F., Houlier, P., Marchand, E., "Improving monocular plane-based SLAM with inertial measures.", In Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on. IEEE. pp. 3810-3815, 2010.
  10. Foxlin, Eric. "Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter." Virtual Reality Annual International Symposium, 1996., Proceedings of the IEEE 1996. IEEE, 1996.
  11. VINCE J.: Quaternions for Computer Graphics", Springer, ISBN 978-0-85729-760-0, 2011
  12. Comotti, D., "Orientation estimation based on gauss-newton method and implementation of a quaternion complementary filter." tech. rep., pp. 1-8, 2011.
  13. Sabatini A. M., "Estimating Three-dimensional Orientation of Human Body Parts by Inertial/Magnetic Sensing.", Sensors, 11(2), pp. 1489-1525, 2011. https://doi.org/10.3390/s110201489