Abstract
We address the problem of estimating 3-D motion from acoustic images acquired by high-frequency 2-D imaging sonars deployed in underwater. Utilizing a planar approximation to scene surfaces, two-view homography is the basis of a nonlinear optimization method for estimating the motion parameters. There is no scale factor ambiguity, unlike the case of monocular motion vision for optical images. Experiments with real images demonstrate the potential in a range of applications, including target-based positioning in search and inspection operations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
http://www.videoray.com/ftp/HULSFest/spawar_incoming/HF_Conf_Granger.pdf
Negahdaripour, S.: Epipolar geometry of optic-acoustic stereo imaging. In print, On IEEE PAMI (also appeared in Proc. IEEE Oceans 05)
Negahdaripour, S., Firoozfam, P., Sabzmeydani, P.: On Processing and registration of forward-scan acoustic video imagery. In: Proc. Canadian Conf. Computer & Robot Vision, Victoria, BC, Canada (May 2005)
Sekkati, H., Negahdaripour, S.: Direct and Indirect 3-D Reconstruction from Opti-Acoustic Stereo Imaging. In: Third International Symposium on 3D Data Processing, Visualization and Transmission, Chapel Hill, USA, June 14-16 (2006)
Balasuriya, A., Ura, T.: Underwater cable following by Twin-Burger 2. In: Proc. IEEE Robotics Automation (2001)
Belcher, E.O., Gallagher, D.G., Barone, J.R., Honaker, R.E.: Acoustic lens camera and underwater display combine to provide efficient and effective hull and berth inspections. In: Proc. Oceans’03, San Diego, CA (September 2003)
Kim, K., Neretti, N., Intrator, N.: Non-iterative construction of super-resolution image from an acoustic camera video sequence. In: Proc. CIHSPS (2005)
Garcia, R., Nicosevici, T., Ridao, P., Ribas, D.: Towards a real-time vision-based navigation system for a small-class UUV. In: IEEE IROS, Las Vegas (2003)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. Alvey Vision Conference (1988)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge Univ. Press, Cambridge (2001)
Kullback, S.: Information theory and statistics. Dover publications, Mineola (1968)
Marks, R., Wang, H.H., Lee, M., Rock, S.: Automatic visual station keeping of an underwater robot. In: Proc. Oceans’94, Brest, France (September 1994)
Lots, J-F., Lane, D.M., Trucco, E., Chaumette, F.: A 2-D visual servoing for underwater vehicle station keeping. In: Proceedings IEEE Conference Robotics and Automation, Seoul, Korea (2001)
Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proc. Int. Joint Conference on Artificial Intelligence, Vancouver, Canada (August 1981)
Majumder, S., Scheding, S., Durrant-Whyte, H.F.: Multi sensor data fusion for underwater navigation. Robotics and Autonomous Systems 35(1) (2001)
Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. J. Society for Industrial and Applied Mathematics 11(2) (1963)
Ortiz, A., Oliver, G., Frau, J.: A vision system for underwater real-time control tasks. In: Proc. Oceans’97, Halifax, CA, pp. 1425–1430 (1997)
Rosenblum, L.J., Kamgar-Parsi, B., Belcher, E.O., Engelsen, O.: Acoustic imaging: The reconstruction of underwater objects. In: IEEE Visualization (1991)
Gracias, N., Santos-Victor, J.: Underwater video mosaics as visual navigation maps. Computer Vision Image Understanding 79 (2000)
Shi, J., Tomasi, C.: Good features to track. In: Proc. IEEE CVPR, Seattle, WA (June 1994)
Silpa-Anan, C., Brinsmead, T., Abdallah, S., Zelinsky A.: Preliminary experiments in visual servo control for autonomous underwater vehicle. In: Proc. IEEE Int. Conf. Intelligent Robots and Systems, Maui, Hawaii (October/ November 2001)
Longuet-Higgins, H.: A computer algorithm for reconstructing a scene from two projections. Nature 293, 133–135 (1981)
Tsai, R., Huang, T.: Uniqueness and estimation of three-dimensional motion parameters of rigid objects with curved surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 13–27 (1984)
Shi, J., Tomasi, C.: Good features to track. In: Proc. IEEE Conference on Computer Vision & Pattern Recognition, Seattle, WA, June 1994, pp. 593–600 (1994)
Bouguet, J.: Pyramidal implementation of the Lucas Kanade feature tracker. Microprocessor Research Labs, Intel Corporation, Tech. Rep. (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Sekkati, H., Negahdaripour, S. (2007). 3-D Motion Estimation for Positioning from 2-D Acoustic Video Imagery. In: MartÃ, J., BenedÃ, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72849-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-72849-8_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72848-1
Online ISBN: 978-3-540-72849-8
eBook Packages: Computer ScienceComputer Science (R0)