DOI QR코드

DOI QR Code

Cross-covariance 3D Coordinate Estimation Method for Virtual Space Movement Platform

가상공간 이동플랫폼을 위한 교차 공분산 3D 좌표 추정 방법

  • 정하형 ((주)코어센스 연구원) ;
  • 박진하 ((주)코어센스 연구원) ;
  • 김민경 (전주대학교 소방방제학과) ;
  • 장민혁 (한국전자기술연구원 IT융합부품연구센터)
  • Received : 2020.09.14
  • Accepted : 2020.10.05
  • Published : 2020.10.31

Abstract

Recently, as the demand for the mobile platform market in the virtual/augmented/mixed reality field is increasing, experiential content that gives users a real-world felt through a virtual environment is drawing attention. In this paper, as a method of tracking a tracker for user location estimation in a virtual space movement platform for motion capture of trainees, we present a method of estimating 3D coordinates of the 3D cross covariance through the coordinates of the markers projected on the image. In addition, the validity of the proposed algorithm is verified through rigid body tracking experiments.

최근 가상/증강/혼합현실 분야의 이동 플랫폼 시장 수요 커지면서 가상환경을 이용한 다중 체험이 가능한 콘텐츠를 통해 사용자에게 실제 현장과 같은 느낌을 부여하는 체험형 콘텐츠가 주목받고 있다. 본 논문에서는 교육훈련생의 모션 캡쳐를 위한 가상공간 이동플랫폼에서 사용자 위치 추정을 위한 트래커의 추적하는 방법으로, 2차원 영상 평면에 투영된 마커의 좌표를 통한 3차원 교차 공분산의 3D 좌표 추정 방법을 제시한다. 또한, 강체 추적실험을 통해 제안한 알고리즘의 유효성을 검증하여 낮은 해상도의 카메라를 통해서도 3D 좌표 추정이 가능함을 보인다.

Keywords

References

  1. Angeli, A., Doncieux S., and Meyer J. (2009). Visual Topological SLAM and Global Localization, Proceedings of the IEEE International Conference on Robotics and Automation, May. 12-17, Kobe, Japan, pp. 4300-4305.
  2. Chang, K. C., Chong, C. Y., and Mori, S. (2010). Analytical and Computational Evaluation of Scalable Distributed Fusion Algorithms, IEEE Transactions on Aerospace and Electronic Systems, 46(4), 2022-2034. https://doi.org/10.1109/TAES.2010.5595611
  3. Deng, Z., Zhang, P., Qi, W., Liu, J., and Gao, Y. (2012). Sequential Covariance Intersection Fusion Kalman Filter, Information Sciences. 189, 293-309. https://doi.org/10.1016/j.ins.2011.11.038
  4. Jung, H, H. (2016). Multi-Camera Calibration Techniques for Motion Capture, Proceedings of the Conference on the Institute of Electronics and Information Engineers, Apr. 29-30, Yongin, South Korea, pp. 227-230.
  5. Julier, J., and Uhlmann, K. (2007). Using Covariance Intersection for SLAM, Robotics and Autonomous Systems. 55(7), 3-20. https://doi.org/10.1016/j.robot.2006.06.011
  6. Kim, M, K. (2015). Performance Improvement of an AHRS for Motion Capture, Journal of Institute of Control, Robotics and Systems, 21(12), 521-537.
  7. Kim, S., and Lee, H. (2018). Implementation of Pattern Recognition Algorithm using Line Scan Camera for Recognition of Path and Location of AGV, Korea Society of Industrial Information Systems, 23(1), 13-21.
  8. Lee, S., and Lee, C. (2014). Implementation of Game Interface using Human Head Motion Recognition, Korea Society of Industrial Information Systems, 19(5), 9-14.
  9. Hong, D., Cheon, M., and Lee, D. (2019). Image Processing based Virtual Reality Input Method using Gesture, Korea Society of Industrial Information Systems, 24(5), 129-137.
  10. Sturm, P., and Maybank, S. (1999). On Plane-based Camera Calibration: A General Algorithm, Singularities, Applications. CVPR '99: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1. June, 23-25, Fort Collins, USA, pp. 432-437.
  11. Weng, Z., and Petar, D. (2012). A Bayesian Approach to Covariance Estimation and Data Fusion, 2012 Proceedings of the 20th European Signal Processing Conference, Aug, 27-31, Bucharest, Romania, pp. 2352-2356.
  12. Zhang, Z (2000). A Fexible New Technique for Camera Calibration, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330-1334. https://doi.org/10.1109/34.888718