JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Development of a Hover-capable AUV System for In-water Visual Inspection via Image Mosaicking
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Development of a Hover-capable AUV System for In-water Visual Inspection via Image Mosaicking
Hong, Seonghun; Park, Jeonghong; Kim, Taeyun; Yoon, Sukmin; Kim, Jinwhan;
  PDF(new window)
 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;
 Language
Korean
 Cited by
 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.
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. crossref(new window)

3.
Fossen, T.I., 2011. Handbook of Marine Craft Hydrodynamics and Motion Control. John Wiley & Sons, UK.

4.
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.

5.
Haralick, R.M., 1996. Propagating Covariance in Computer Vision. International Journal of Pattern Recognition and Artificial Intelligence, 10(5), 561-572. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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.