Mapping Complex Marine Environments with Autonomous Surface Craft

  • Jacques C. LeedekerkenEmail author
  • Maurice F. Fallon
  • John J. Leonard
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 79)


This paper presents a novel marine mapping system using an Autonomous Surface Craft (ASC). The platform includes an extensive sensor suite for mapping environments both above and below the water surface. A relatively small hull size and shallow draft permits operation in cluttered and shallow environments. We address the Simultaneous Mapping and Localization (SLAM) problem for concurrent mapping above and below the water in large scale marine environments. Our key algorithmic contributions include: (1) methods to account for degradation of GPS in close proximity to bridges or foliage canopies and (2) scalable systems for management of large volumes of sensor data to allow for consistent online mapping under limited physical memory. Experimental results are presented to demonstrate the approach for mapping selected structures along the Charles River in Boston.


Iterative Close Point Dead Reckoning Iterative Close Point Occupancy Grid Lidar Return 
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  1. 1.
    Harvard bridge, spanning charles river at massachusetts avenue, boston, suffolk county, ma,
  2. 2.
    Abbeel, P., Coates, A., Montemerlo, M., Ng, A.Y., Thrun, S.: Discriminative training of kalman filters. In: Proceedings of Robotics: Science and Systems, Cambridge, USA (2005)Google Scholar
  3. 3.
    Bachrach, A., He, R., Roy, N.: Autonomous flight in unstructured and unknown indoor environments. In: European Micro Aerial Vehicle Conference (2009)Google Scholar
  4. 4.
    Besl, P., McKay, N.: A method for registration of 3-d shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)CrossRefGoogle Scholar
  5. 5.
    Bosse, M., Newman, P., Leonard, J., Teller, S.: Simultaneous localization and map building in large-scale cyclic environments using the Atlas framework. Intl. J. of Robotics Research 23(12), 1113–1139 (2004)CrossRefGoogle Scholar
  6. 6.
    Bosse, M., Zlot, R.: Continuous 3D scan-matching with a spinning 2D laser, Kobe, Japan (2009)Google Scholar
  7. 7.
    Canny, J.: A computational approach to edge detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986), doi:10.1109/TPAMI.1986.4767851CrossRefGoogle Scholar
  8. 8.
    Curcio, J., Leonard, J., Patrikalakis, A.: SCOUT - a low cost autonomous surface platform for research in cooperative autonomy. In: Proceedings of the IEEE/MTS OCEANS Conference and Exhibition, Washington, DC (2005)Google Scholar
  9. 9.
    Dellaert, F., Kaess, M.: Square Root SAM: Simultaneous localization and mapping via square root information smoothing. Intl. J. of Robotics Research 25(12), 1181–1203 (2006)zbMATHCrossRefGoogle Scholar
  10. 10.
    Elfes, A.: Integration of sonar and stereo range data using a grid-based representation. In: Proc. IEEE Int. Conf. Robotics and Automation (1988)Google Scholar
  11. 11.
    Fairfield, N., Kantor, G.A., Wettergreen, D.: Real-time SLAM with octree evidence grids for exploration in underwater tunnels. J. of Field Robotics (2007)Google Scholar
  12. 12.
    Fairfield, N., Wettergreen, D.: Active localization on the ocean floor with multibeam sonar. In: Proceedings of the IEEE/MTS OCEANS Conference and Exhibition, pp. 1–10 (2008), doi:10.1109/OCEANS.2008.5151853Google Scholar
  13. 13.
    Folkesson, J., Leedekerken, J., Williams, R., Leonard, J.: Feature tracking for underwater navigation using sonar. In: IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), San Diego, CA (2007)Google Scholar
  14. 14.
    Folkesson, J., Leederkerken, J., Williams, R., Patrikalakis, A., Leonard, J.: A feature based navigation system for an autonomous underwater robot. In: Field and Service Robotics (FSR), vol. 42, pp. 105–114 (2008)Google Scholar
  15. 15.
    Horn, B.K.P.: Closed-form solution of absolute orientation using unit quaternions. Journal of the Optical Society of America 4, 629–642 (1987)Google Scholar
  16. 16.
    Kaess, M., Ranganathan, A., Dellaert, F.: iSAM: Incremental smoothing and mapping. IEEE Trans. Robotics 24(6), 1365–1378 (2008)CrossRefGoogle Scholar
  17. 17.
    Moravec, H.: Sensor fusion in certainty grids for mobile robots. In: Sensor Devices and Systems for Robotics. Nato ASI Series, pp. 253–276. Springer (1989)Google Scholar
  18. 18.
    Newman, P., Cole, D., Ho, K.: Outdoor SLAM using visual appearance and laser ranging. In: Proceedings 2006 IEEE International Conference on Robotics and Automation. ICRA 2006, pp. 1180–1187. IEEE (2006)Google Scholar
  19. 19.
    Ni, K., Steedly, D., Dellaert, F.: Tectonic SAM: Exact, out-of-core, submap-based SLAM. In: IEEE Intl. Conf. on Robotics and Automation (ICRA), pp. 1678–1685 (2007)Google Scholar
  20. 20.
    Nüchter, A., Lingemann, K., Hertzberg, J., Surmann, H.: 6D SLAM-3D mapping outdoor environments. Journal of Field Robotics 24(8-9), 699–722 (2007)zbMATHCrossRefGoogle Scholar
  21. 21.
    Pauly, M., Gross, M., Kobbelt, L.: Efficient simplification of point-sampled surfaces. In: Visualization, VIS 2002, pp. 163–170. IEEE (2002), doi:10.1109/VISUAL.2002.1183771Google Scholar
  22. 22.
    Ribas, D., Ridao, P., Neira, J., Tardós, J.: SLAM using an imaging sonar for partially structured underwater environments. In: IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS) (2006)Google Scholar
  23. 23.
    Ribas, D., Ridao, P., Tardós, J., Neira, J.: Underwater SLAM in man-made structured environments. Journal of Field Robotics 25(11-12), 898–921 (2008)zbMATHCrossRefGoogle Scholar
  24. 24.
    Roman, C., Singh, H.: Improved vehicle based multibeam bathymetry using sub-maps and SLAM. In: IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), pp. 3662–3669 (2005)Google Scholar
  25. 25.
    Roman, C., Singh, H.: Consistency based error evaluation for deep sea bathymetric mapping with robotic vehicles, Orlando, FL (2006)Google Scholar
  26. 26.
    Rusu, R.B., Blodow, N., Marton, Z.C., Beetz, M.: Aligning point cloud views using persistent feature histograms, Nice, France (2008)Google Scholar
  27. 27.
    Ryde, J., Hu, H.: 3D mapping with multi-resolution occupied voxel lists. Autonomous Robots 28(2) (2010)Google Scholar
  28. 28.
    Thrun, S.: Learning occupancy grids with forward sensor models. In: Proceedings of the Conference on Intelligent Robots and Systems (IROS 2001), Hawaii (2001)Google Scholar
  29. 29.
    Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., Fong, P., Gale, J., Halpenny, M., Hoffmann, G., Lau, K., Oakley, C., Palatucci, M., Pratt, V., Stang, P., Strohband, S., Dupont, C., Jendrossek, L.E., Koelen, C., Markey, C., Rummel, C., van Niekerk, J., Jensen, E., Alessandrini, P., Bradski, G.: Stanley: The robot that won the DARPA grand challenge. Journal of Field Robotics: Special Issue on the DARPA Grand Challenge 23, 661–692 (2006)CrossRefGoogle Scholar
  30. 30.
    Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. Intl. Journal of Computer Vision 13(2), 119–152 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2014

Authors and Affiliations

  • Jacques C. Leedekerken
    • 1
    Email author
  • Maurice F. Fallon
    • 1
  • John J. Leonard
    • 1
  1. 1.Massachusetts Institute of TechnologyCambridgeUSA

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