The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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Side Scan Sonar based Pose-graph SLAM

사이드 스캔 소나 기반 Pose-graph SLAM

  • Received : 2017.07.31
  • Accepted : 2017.10.11
  • Published : 2017.11.30
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Abstract

Side scanning sonar (SSS) provides valuable information for robot navigation. However using the side scanning sonar images in the navigation was not fully studied. In this paper, we use range data, and side scanning sonar images from UnderWater Simulator (UWSim) and propose measurement models in a feature based simultaneous localization and mapping (SLAM) framework. The range data is obtained by echosounder and sidescanning sonar images from side scan sonar module for UWSim. For the feature, we used the A-KAZE feature for the SSS image matching and adjusting the relative robot pose by SSS bundle adjustment (BA) with Ceres solver. We use BA for the loop closure constraint of pose-graph SLAM. We used the Incremental Smoothing and Mapping (iSAM) to optimize the graph. The optimized trajectory was compared against the dead reckoning (DR).

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References

  1. M. Prats, J. Perez, J.J. Fernandez, and P.J. Sanz, "An open source tool for simulation and supervision of underwater intervention missions," IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2577-2582, 2012.
  2. H. Ragheb and E.R. Hancock. "Surface radiance correction for shape from shading," Pattern Recognition, Vol. 38, No. 10, pp. 1574-1595, 2005. https://doi.org/10.1016/j.patcog.2005.03.025
  3. P.F. Alcantarilla, A. Bartoli, and A.J. Davison. "KAZE features," European Conference on Computer Vision. Springer, Berlin, Heidelberg, pp. 214-227, 2012.
  4. M. Kaess, A. Ranganathan, and F. Dellaert, "iSAM: Incremental Smoothing and Mapping," IEEE Transactions on Robotics, Vol. 24, No. 6, pp. 1365-1378, 2008. https://doi.org/10.1109/TRO.2008.2006706
  5. M.F. Fallon, M. Kaess, H. Johannsson, and J.J. Leonard, "Efficient AUV navigation fusing acoustic ranging and sidescan sonar," IEEE International Conference on Robotics and Automation, Shanghai, pp. 2398-2405, 2011.
  6. D. Langer and M. Hebert, "Building qualitative elevation maps from side scan sonar data for autonomous underwater navigation," Proceedings IEEE International Conference on Robotics and Automation, pp. 2478-2483, 1991.
  7. Y. Pailhas, Y. Petillot, C. Capus, and K. Brown, "Real-time sidescan simulator and applications," OCEANS 2009-EUROPE, pp. 1-6, 2009.
  8. H.P. Johnson and M. Helferty. "The geological interpretation of side‐scan sonar," Reviews of Geophysics, Vol. 28, No. 4, pp. 357-380, 1990. https://doi.org/10.1029/RG028i004p00357
  9. V.S. Blake, "The simulation of side‐scan sonar images," Archaeological Prospection, Vol. 2, No. 1, pp. 29-56, 1995. https://doi.org/10.1002/1099-0763(199503)2:1<29::AID-ARP6140020105>3.0.CO;2-P
  10. S. Anstee, "Removal of range-dependent artifacts from sidescan sonar imagery," DTIC Document, Tech. Rep. 2001.
  11. E. Coiras, Y. Petillot, and D. M. Lane, "Multiresolution 3-D Reconstruction From Side-Scan Sonar Images," IEEE Transactions on Image Processing, Vol. 16, No. 2, pp. 382-390, 2007. https://doi.org/10.1109/TIP.2006.888337
  12. N. Neretti, N. Intrator, and Q. Huynh, "Target detection in side-scan sonar images: expert fusion reduces false alarms," 2002.
  13. X.-F. Ye, P. Li, J.-G. Zhang, J. Shi, and S.-X. Guo, "A feature-matching method for side-scan sonar images based on nonlinear scale space," Journal of Marine Science and Technology, Vol. 21, No. 1, pp. 38-47, 2016. https://doi.org/10.1007/s00773-015-0330-5
  14. D. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  15. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, "Speeded-up robust features (SURF)," Computer Vision and Image Understanding, Vol. 110, No. 3, pp. 346-359, 2008. https://doi.org/10.1016/j.cviu.2007.09.014
  16. E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, "ORB: An efficient alternative to SIFT or SURF," Proceedings of the IEEE International Conference on Computer Vision, pp. 2564-2571, 2011.
  17. I. T. Ruiz, S. De Raucourt, Y. Petillot, and D. M. Lane, "Concurrent mapping and localization using sidescan sonar," IEEE Journal of Oceanic Engineering, Vol. 29, No. 2, pp. 442-456, 2004. https://doi.org/10.1109/JOE.2004.829790
  18. C. de Jong, G. Lachapelle, S. Skone, and I. Elema, "Multibeam sonar theory of operation," Delft University Press, Delft, the Netherlands, Tech. Rep. 2002.
  19. Antonelli, Gianluca, Thor I. Fossen, and Dana R. Yoerger. "Underwater robotics," Springer handbook of robotics. Springer Berlin Heidelberg, pp. 987-1008, 2008.
  20. S. Agarwal, N. Snavely, S. M. Seitz and R. Szeliski, "Bundle Adjustment in the Large," Proceedings of the European Conference on Computer Vision, pp. 29-42, 2010.
  21. D. H. Gwon, J. Kim, M. H. Kim, H. G. Park, T. Y. Kim, and A. Kim, "Development of a side scan sonar module for the underwater simulator," 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 662-665, 2017.

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