Journal of the Korean Society for Aeronautical & Space Sciences (한국항공우주학회지)

The Korean Society for Aeronautical and Space Sciences (한국항공우주학회)
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Particle Filters using Gaussian Mixture Models for Vision-Based Navigation

영상 기반 항법을 위한 가우시안 혼합 모델 기반 파티클 필터

  • Received : 2019.01.03
  • Accepted : 2019.03.26
  • Published : 2019.04.01
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Abstract

Vision-based navigation of unmaned aerial vehicle is a significant technology that can reinforce the vulnerability of the widely used GPS/INS integrated navigation system. However, the existing image matching algorithms are not suitable for matching the aerial image with the database. For the reason, this paper proposes particle filters using Gaussian mixture models to deal with matching between aerial image and database for vision-based navigation. The particle filters estimate the position of the aircraft by comparing the correspondences of aerial image and database under the assumption of Gaussian mixture model. Finally, Monte Carlo simulation is presented to demonstrate performance of the proposed method.

무인항공기의 영상 기반 항법은 널리 사용되는 GPS/INS 통합 항법 시스템의 취약점을 보강할 수 있는 중요한 기술로 이에 대한 연구가 활발히 이루어지고 있다. 하지만 일반적인 영상 대조 기법은 실제 항공기 비행 상황들을 적절하게 고려하기 힘들다는 단점이 있다. 따라서 본 논문에서는 영상기반 항법을 위한 가우시안 혼합 모델 기반의 파티클 필터를 제안한다. 제안한 파티클 필터는 영상과 데이터베이스를 가우시안 혼합 모델로 가정하여 둘 간의 유사도를 이용하여 항체의 위치를 추정한다. 또한 몬테카를로 시뮬레이션을 통해 위치 추정 성능을 확인한다.

Keywords

References

  1. Brown, A., Bockius, B., Johnson, B., Holland, H., and Wetlesen, D., "Flight test results of a video-aided GPS/inertial navigation system," Proceedings of the 2007 ION GNSS Conference, pp.1111-1117.
  2. Wu, A. D., "Vision-based navigation and mapping for flight in GPS-denied environments," PhD Thesis, Georgia Institute of Technology, 2010.
  3. Yol, A., Delabarre, B., Dame, A., Dartois, J. E., and Marchand, E., "Vision-based absolute localization for unmanned aerial vehicles," Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on, IEEE, 2014, pp.3429-3434.
  4. Conte, G., and Doherty, P., "An integrated UAV navigation system based on aerial image matching," Aerospace Conference, 2008 IEEE IEEE, 2008, pp.1-10.
  5. Conte, G., and Doherty, P., "Vision-based unmanned aerial vehicle navigation using geo-referenced information," EURASIP Journal on Advances in Signal Processing, 2009, 10.
  6. Dumble, S. J., and Gibbens, P. W., "Airborne vision-aided navigation using road intersection features," Journal of Intelligent & Robotic Systems, Vol. 78, No. 2, May 2015, pp.185-204. https://doi.org/10.1007/s10846-014-0056-3
  7. Koch, T., Zhuo, X., Reinartz, P., and Fraundorfer, F., "A NEW PARADIGM FOR MATCHING UAV-AND AERIAL IMAGES," ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, Vol. 3, No. 3, July 2016, pp.83-90
  8. Stepanov, O. A., and Toropov, A. B., "Nonlinear filtering for map-aided navigation. Part 1. An overview of algorithms," Gyroscopy and Navigation, Vol. 6, No. 4, October 2015, pp.324-337. https://doi.org/10.1134/S2075108715040148
  9. Stepanov, O. A., and Toropov, A. B., "Nonlinear filtering for map-aided navigation Part 2. Trends in the algorithm development," Gyroscopy and Navigation, Vol. 7, No. 1, January 2016, pp.82-89. https://doi.org/10.1134/S2075108716010132
  10. Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., and Garcia-Rodriguez, J., "A review on deep learning techniques applied to semantic segmentation," arXiv preprint arXiv:1704.06857.
  11. He, K., Gkioxari, G., Dollar, P., and Girshick, R., "Mask r-cnn," In Computer Vision (ICCV), 2017 IEEE International Conference on, IEEE, 2017 pp.2980-2988.
  12. Sofman, B., Bagnell, J., Stentz, A., and Vandapel, N., "Terrain classification from aerial data to support ground vehicle navigation," Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, Tech. Rep. CMURI-TR-05-39.
  13. Montoya-Zegarra, J. A., Wegner, J. D., Ladicky, L., and Schindler, K., "Semantic segmentation of aerial images in urban areas with class-specific higher-order cliques," ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 2, No. 3, March 2015, pp.127-133.
  14. Hong, K. W., Kim, S. J., and Bang, H. C., "Aerial Image Segmentation and Template Matching for Vision-aided UAV Navigation," Proceeding of The Korean Society for Aeronautical and Space Sciences Fall Conference, November 2018, pp.599-600.
  15. Rogers, R. M., "Applied mathematics in integrated navigation systems," American Institute of Aeronautics and Astronautics, 2007.
  16. Papoulis, A., and Pillai, S. U., "Probability, random variables, and stocahastic processes," Tata McGraw-Hill Education, 2002.
  17. Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T., "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking," IEEE Transactions on signal processing, Vol. 50, No. 2, February 2002, pp.174-188. https://doi.org/10.1109/78.978374
  18. Jian, B., and Vemuri, B. C., "Robust point set registration using gaussian mixture models," IEEE transactions on pattern analysis and machine intelligence, Vol. 33, No. 8, August 2011, pp.1633-1645. https://doi.org/10.1109/TPAMI.2010.223