Skip to main content
Log in

Seafloor map generation for autonomous underwater vehicle navigation

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

Elevation map generation is an essential component of any autonomous underwater vehicle designed to navigate close to the seafloor because elevation maps are used for obstacle avoidance, path planning and self localization. We present an algorithm for the reconstruction of elevation maps of the seafloor from side-scan sonar backscatter images and sparse bathymetric points co-registered within the image. Given the trajectory for the underwater vehicle, the reconstruction is corrected for the attitude of the side-scan sonar during the image generation process. To perform reconstruction, an arbitrary but computable scattering model is assumed for the seafloor backscatter. The algorithm uses the sparse bathymetric data to generate an initial estimate for the elevation map which is then iteratively refined to fit the backscatter image by minimizing a global error functional. Concurrently, the parameters of the scattering model are determined on a coarse grid in the image by fitting the assumed scattering model to the backscatter data. The reconstruction is corrected for the movement of the sensor by initially doing local reconstructions in sensor coordinates and then transforming the local reconstructions to a global coordinate system using vehicle attitude and performing the reconstruction again. We demonstrate the effectiveness of our algorithm on synthetic and real data sets. Our algorithm is shown to decrease the average elevation error when compared to real bathymetry from 4.6 meters for the initial surface estimate to 1.6 meters for the final surface estimate from a survey taken of the Juan de Fuca Ridge.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Aleksandrov, A.D., Kolmogorov, A.N., and Lavrent'ev, M.A. 1964. Mathematics: Its Content, Methods and Meaning, MIT Press: Cambridge, MA.

    Google Scholar 

  • Baeck, T. and Schwefel, H.P. 1993. An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1(1):1–10.

    Google Scholar 

  • Blake, A. and Zisserman, A. 1987. Visual Reconstruction, MIT Press: Cambridge, MA.

    Google Scholar 

  • Caruthers, J.W. and Novarini, J.C. 1993. Modeling bistatic bottom scattering strength including a forward scatter lobe. IEEE J. Oceanic Engineering, 18(2):100–107.

    Google Scholar 

  • Cervenka, P. and de Moustier, C. 1993. Sidescan sonar image processing techniques. IEEE J. Oceanic Engineering, 18(2):108–122.

    Google Scholar 

  • Clarke, J.H. 1994. Toward remote seafloor classification using the angular response of acoustic backscatter: A case study from multiple overlapping GLORIA data. IEEE J. Oceanic Engineering, 19(1):112–127.

    Google Scholar 

  • Cobra, D.T., Oppenheim, A.V., and Jaffe, J.S. 1992. Geometric distortions in side-scan sonar images: A procedure for their estimation and correction. IEEE J. Oceanic Engineering, 17(3):252–268.

    Google Scholar 

  • Cuschieri, J.M. and Hebert, M. 1990. Three-dimensional map generation from side-scan sonar images. L. Energy Resources Technology, 112:96–102.

    Google Scholar 

  • Denbigh, P.N. 1989. Swath bathymetry: Principles of operation and analysis of errors. IEEE J. Oceanic Engineering, 14(4):289–298.

    Google Scholar 

  • de Moustier, C. and Alexandrou, D. 1991. Angular dependence of 12-kHz seafloor acoustic backscatter. J. Acoustical Society of America, 90:522–531.

    Google Scholar 

  • Elfes, A. 1987. Sonar-based real world mapping and navigation. IEEE J. Robotics and Automation, RA-3(3):249–265.

    Google Scholar 

  • Gensane, M. 1989. A statistical study of acoustic signals backscattered from the sea bottom. IEEE J. Oceanic Engineering, 14(1):84–93.

    Google Scholar 

  • Hebert, M. 1989. Terrain modeling for autonomous underwater navigation. In Proc. Unmanned Untethered Submersible Technology Conf., pp. 502–511.

  • Horn, B.K.P. 1986. Robot Vision. MIT Press: Cambridge, MA.

    Google Scholar 

  • Horn, B.K.P. and Brooks, M.J. 1988. The variational approach to shape from shading. Computer Vision, Graphics and Image Processing, 33(2):174–208.

    Google Scholar 

  • Jackson, D.R., Winebrenner, D.P., and Ishimaru, A. 1986. Application of the composite roughness model to high-frequency bottom backscattering. J. Acoustical Society of America, 79:1410–1422.

    Google Scholar 

  • Johnson, A.E. 1993. Incorporating different reflection models into surface reconstruction. In Proc. Unmanned Untethered Submersible Technology Conf., pp. 446–459.

  • Langer, D. and Hebert, M. 1991. Building qualitative elevation maps from underwater sonar data for autonomous underwater navigation. In Proc. IEEE Int. Conf. Robotics and Automation, pp. 2478–2483.

  • Leonard, J.J. and Durrant-Whyte, H.F. 1992. Directed Sonar Sensing for Mobile Robot Navigation, Kluwer Academic: Norwell, MA.

    Google Scholar 

  • Malik, S. 1991. Quantitative seafloor backscatter characterization using an interferometric sidescan sonar. Master's Thesis, U. Virginia.

  • Matsumoto, H., Dziak, R.P., and Fox, C.G. 1993. Estimation of seafloor microtopographic roughness through modeling of acoustic backscatter data recorded by multibeam sonar systems. J. Acoustical Society of America, 94:2776–2787.

    Google Scholar 

  • Mazel, C. 1985. Side Scan Sonar Record Interpretation, Klein Associates: Salem, NH.

    Google Scholar 

  • Michalopoulou, Z., Alexandrou, D., and de Moustier, C. 1994. Application of a maximum likelihood processor to acoustic backscatter for the estimation of seafloor roughness parameters. J. Acoustical Society of America, 95:2467–2477.

    Google Scholar 

  • Mitchell, N.C. and Somers, M.L. 1989. Quantitative backscatter measurements with a long-range side-scan sonar. IEEE J. Oceanic Engineering, 14(4):368–374.

    Google Scholar 

  • Mourad, P.D. and Jackson, D.R. High frequency sonar equation models for bottom backscatter and forward loss. In Proc. IEEE Oceans 89 Conf., pp. 1163–1175.

  • Oren, M. and Nayar, S.K. 1992. Diffuse scattering model for rough surfaces. Dept. of Computer Science Technical Report 057–92, Columbia University, New York, NY.

    Google Scholar 

  • Press, W.H., Flannery, B.P., Teukolsky, S.A., and Vetterling, W.T. 1988. Numerical Recipes in C, Cambridge University Press: New York, NY.

    Google Scholar 

  • Rigaud, V. and Marcé, L. 1990. Absolute location of underwater robotic vehicles by acoustic data fusion. In Proc. IEEE Int. Conf. Robotics and Automation, pp. 1310–1315.

  • Stanton, T.K. 1984. Sonar estimates of seafloor microroughness. J. Acoustical Society of America, 74:809–818.

    Google Scholar 

  • Stewart, W.K., Marra, M., and Jiang, M. 1992. A hierarchical approach to seafloor classification using neural networks. IEEE Oceans 92 Conf., pp. 109–113.

  • Stewart, W.K. 1989. Three-dimensional modeling of seafloor backscatter from sidescan sonar for autonomous classification and navigation. In Proc. Unmanned Untethered Submersible Technology Conf., pp. 372–392.

  • Stewart, W.K., Chu, D., Malik, S., Lerner, S., and Singh, H. 1994. Quantitative seafloor characterization using a bathymetric sidescan sonar. IEEE J. Oceanic Engineering, 19(4):599–610.

    Google Scholar 

  • Torrance, K.E. and Sparrow, E.M. Theory for off-specular scattering omroughened surfaces, J. Optical Society of America, 57:1105–1114.

  • Urick, R.J. 1983. Principles of Underwater Sound, McGraw-Hill: New York, NY.

    Google Scholar 

  • von Alt, C. 1989. A 200kHz deep sea interferometric side scan sonar system. In Proc. IEEE Oceans 89 Conf., pp. 1136–1141.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Johnson, A.E., Hebert, M. Seafloor map generation for autonomous underwater vehicle navigation. Auton Robot 3, 145–168 (1996). https://doi.org/10.1007/BF00141152

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00141152

Keywords

Navigation