DOI QR코드

DOI QR Code

Vision Aided Inertial Sensor Bias Compensation for Firing Lane Alignment

사격 차선 정렬을 위한 영상 기반의 관성 센서 편차 보상

  • Received : 2020.03.10
  • Accepted : 2022.06.22
  • Published : 2022.09.01

Abstract

This study investigates the use of movable calibration target for gyroscopic and accelerometer bias compensation of inertial measurement units for firing lane alignment. Calibration source is detected with the help of vision sensor and its information in fused with other sensors on launcher for error correction. An algorithm is proposed and tested in simulation. It has been shown that it is possible to compensate sensor biases in firing launcher in few seconds by accurately estimating the location of calibration target in inertial frame of reference.

본 논문은 사격 차선 정렬을 위하여 움직일 수 있는 교정 대상을 이용해 각속도계와 가속도계의 편차를 보상하는 방법을 다룬다. 교정 대상에 대한 정보는 영상 센서를 통해 획득하며 이를 이용해 발사장치에 부착된 관성측정 장치의 오차를 보정한다. 시뮬레이션을 통해 제안한 알고리즘의 성능을 검증하였으며, 특히 관성 좌표계에서 교정 대상에 대한 위치 정보를 정확하게 획득함으로써 발사장치의 관성 센서 편차를 효과적으로 보상할 수 있음을 보인다.

Keywords

Acknowledgement

This research was supported by LIG Nex1 as a part of the research project under the contract number LIGNEX1-2018-0362.

References

  1. Glitscher, K., "Indicator," U.S. Patent No. 1932210, 24 October 1933.
  2. Rafferty, C. A., "Vertical gyro erection system," U.S. Patent No. 3,285,077. 15 November 1966.
  3. Shaw, J. C., Gilbert, J. F., Olbrechts, G. R. and McIntyre, M. D., "Integrated Strapdown Air Data Sensor System," U.S. Patent No. 4303978, 1 December 1981.
  4. Salychev, O., Applied Inertial Navigation: Problems and Solutions, Moscow, Russia, BMSTU press, 2004.
  5. Kalman, R. E., "A New Approach to Linear Filtering and Prediction Problems," Transactions of the ASME - Journal of Basic Engineering, Vol. 82, No. 1, 1960, pp. 35~45 https://doi.org/10.1115/1.3662552
  6. Farrell, J. and Barth, M., The Global Positioning System and Inertial Navigation, New York, NY, USA, Mcgraw-Hill, 1999.
  7. Grewal, M. S., Weill, L. R. and Andrews, A. P., Global Positioning Systems, Inertial Navigation, and Integration, John Wiley & Sons, 2007.
  8. Grewal, M. S. and Andrews, A. P., Kalman filtering: Theory and Practice with MATLAB, John Wiley & Sons, 2014.
  9. Sabatini, A. M., "Quaternion-based Extended Kalman Filter for Determining Orientation by Inertial and Magnetic Sensing," IEEE transactions on Biomedical Engineering, Vol. 53, No. 7, 2006, pp. 1346~1356. https://doi.org/10.1109/TBME.2006.875664
  10. Geiger, A., Lenz, P., Stiller, C. and Urtasun, R., "Vision Meets Robotics: The KITTI Dataset," The International Journal of Robotics Research, Vol. 32, No. 11, 2013, pp. 1231~1237. https://doi.org/10.1177/0278364913491297
  11. Studio, A. N., OpenIMU Developer Manual: State Transition Models, 5 February 2020.