The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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Planetary Long-Range Deep 2D Global Localization Using Generative Adversarial Network

생성적 적대 신경망을 이용한 행성의 장거리 2차원 깊이 광역 위치 추정 방법

  • Ahmed, M.Naguib (Intelligent Systems Research Institute, School of Information and Communication Engineering Sungkyunkwan University) ;
  • Nguyen, Tuan Anh (Intelligent Systems Research Institute, School of Information and Communication Engineering Sungkyunkwan University) ;
  • Islam, Naeem Ul (Intelligent Systems Research Institute, School of Information and Communication Engineering Sungkyunkwan University) ;
  • Kim, Jaewoong (Intelligent Systems Research Institute, School of Information and Communication Engineering Sungkyunkwan University) ;
  • Lee, Sukhan (Intelligent Systems Research Institute, School of Information and Communication Engineering Sungkyunkwan University)
  • Received : 2018.01.15
  • Accepted : 2018.02.13
  • Published : 2018.02.28
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Abstract

Planetary global localization is necessary for long-range rover missions in which communication with command center operator is throttled due to the long distance. There has been number of researches that address this problem by exploiting and matching rover surroundings with global digital elevation maps (DEM). Using conventional methods for matching, however, is challenging due to artifacts in both DEM rendered images, and/or rover 2D images caused by DEM low resolution, rover image illumination variations and small terrain features. In this work, we use train CNN discriminator to match rover 2D image with DEM rendered images using conditional Generative Adversarial Network architecture (cGAN). We then use this discriminator to search an uncertainty bound given by visual odometry (VO) error bound to estimate rover optimal location and orientation. We demonstrate our network capability to learn to translate rover image into DEM simulated image and match them using Devon Island dataset. The experimental results show that our proposed approach achieves ~74% mean average precision.

Keywords

References

  1. P. JF. Carle, P. T. Furgale, and T. D. Barfoot, "Long‐range rover localization by matching LIDAR scans to orbital elevation maps," Journal of Field Robotics, vol. 27, no. 3, pp. 344-370, 2010.
  2. F. Stein and G. Medioni, "Map-based localization using the panoramic horizon," IEEE Transactions on Robotics and Automation, vol. 11, no. 6, pp. 892-896, 1995. https://doi.org/10.1109/70.478436
  3. F. Cozman and E. Krotkov, "Automatic mountain detection and pose estimation for teleoperation of lunar rovers," International Conference on Robotics and Automation, Albuquerque, NM, USA, pp. 2452-2457, 1997.
  4. F. Cozman, E. Krotkov, and C. Guestrin, "Outdoor visual position estimation for planetary rovers," Autonomous Robots, vol. 9, no. 2, pp. 135-150, September, 2000. https://doi.org/10.1023/A:1008966317408
  5. L. Wei and S. Lee, "3D peak based long range rover localization," 2016 7th International Conference on Mechanical and Aerospace Engineering, London, UK, pp. 600-604, 2016.
  6. J. Kim and S. Lee, "Extracting major lines by recruiting zero-threshold canny edge links along sobel highlights," IEEE Signal Processing Letters, vol. 22, no. 10, pp. 1689-1692, October, 2015. https://doi.org/10.1109/LSP.2015.2400211
  7. Y. Tian, C. Chen, and M. Shah, "Cross-View Image Matching for Geo-localization in Urban Environments," arXiv:1703. 07815, 2017.
  8. T.-Y. Lin, Y. Cui, S. Belongie, and J. Hays, "Learning deep representations for ground-to-aerial geolocalization," 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, pp. 5007-5015, 2015.
  9. Workman, Scott, Richard Souvenir, and Nathan Jacobs. "Wide-area image geolocalization with aerial reference imagery." Proceedings of the IEEE International Conference on Computer Vision. P, 3961-3969, 2015.
  10. P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, "Image-to-Image Translation with Conditional Adversarial Networks," 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp. 5967-5976, 2017.
  11. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, "Imagenet large scale visual recognition challenge," International Journal of Computer Vision, vol. 115, no. 3, pp. 211-252, Dec., 2015. https://doi.org/10.1007/s11263-015-0816-y
  12. B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva, "Learning deep features for scene recognition using places database," Advances in Neural Information Processing Systems 27, pp. 487-495, 2014.
  13. P. Furgale, P. Carle, J. Enright, and T. D Barfoot, "The Devon Island rover navigation dataset," The International Journal of Robotics Research, vol. 31, no. 6, pp. 707-713, April, 2012. https://doi.org/10.1177/0278364911433135