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Development of a Hover-capable AUV System for In-water Visual Inspection via Image Mosaicking

영상 모자이킹을 통한 수중 검사를 위한 호버링 타입 AUV 시스템 개발

  • Received : 2015.11.09
  • Accepted : 2016.06.24
  • Published : 2016.06.30

Abstract

Recently, UUVs (unmanned underwater vehicles) have increasingly been applied in various science and engineering applications. In-water inspection, which used to be performed by human divers, is a potential application for UUVs. In particular, the operational safety and performance of in-water inspection missions can be greatly improved by using an underwater robotic vehicle. The capabilities of hovering maneuvers and automatic image mosaicking are essential for autonomous underwater visual inspection. This paper presents the development of a hover-capable autonomous underwater vehicle system for autonomous in-water inspection, which includes both a hardware platform and operational software algorithms. Some results from an experiment in a model basin are presented to demonstrate the feasibility of the developed system and algorithms.

Keywords

Hover-capable AUV;In-water inspection;Autonomous navigation;Image mosaicking;Augmented state Kalman filter

References

  1. Bay, H., T. Tuytelaars, L. Van Gool, 2006. SURF: Speeded up Robust Features. Proceedings of European Conference on Computer Vision, Graz Austria, 404-417.
  2. Haralick, R.M., 1996. Propagating Covariance in Computer Vision. International Journal of Pattern Recognition and Artificial Intelligence, 10(5), 561-572. https://doi.org/10.1142/S0218001496000347
  3. Faugeras, O.D., Lustman, F., 1988. Motion and Structure from Motion in a Piecewise Planar Environment. International Journal of Pattern Recognition and Artificial Intelligence, 2(03), 485-508. https://doi.org/10.1142/S0218001488000285
  4. Fossen, T.I., 2011. Handbook of Marine Craft Hydrodynamics and Motion Control. John Wiley & Sons, UK.
  5. Garcia, R., Puig, J., Ridao, P., Cufi, X., 2002. Augmented State Kalman Filtering for AUV Navigation. Proceedings of IEEE International Conference on Robotics and Automation, Washington D.C., 4010-4015.
  6. Hartley, R., Zisserman, A., 2004. Multiple View Geometry in Computer Vision. 2nd Edition, Cambridge University Press, Cambridge UK.
  7. Hong, S., Kim, T., Kim, J., 2015. Underwater Visual SLAM with Loop-Closure using Image-to-Image Link Recovery. Proceedings of MTS/IEEE OCEANS Conference, Genova Italy.
  8. Kaess, M., Ranganathan, A., Dellaert, F., 2008. iSAM: Incremental Smoothing and Mapping. IEEE Transaction on Robotics, 24(6), 1365-1378. https://doi.org/10.1109/TRO.2008.2006706
  9. Leutenegger, S., Chli, M., Siegwart, R.Y., 2011. BRISK: Binary Robust Invariant Scalable Keypoints. Proceedings of International Conference on Computer Vision, Barcelona Spain, 2548-2555.
  10. Lowe, D.G., 2004. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 60(2), 91-110. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  11. Sawhney, H.S., Hsu, H., Kumar, R., 1998. Robust Video Mosaicing through Topology Inference and Local to Global Alignment. Proceedings of European Conference on Computer Vision.

Acknowledgement

Grant : 미래해양기술개발

Supported by : 한국과학기술원