Abstract
An autonomous underwater vehicle (AUV) is achieved that integrates state of the art simultaneous localization and mapping (SLAM) into the decision processes. This autonomy is used to carry out undersea target reacquisition missions that would otherwise be impossible with a low-cost platform. The AUV requires only simple sensors and operates without navigation equipment such as Doppler Velocity Log, inertial navigation or acoustic beacons. Demonstrations of the capability show that the vehicle can carry out the task in an ocean environment. The system includes a forward looking sonar and a set of simple vehicle sensors. The functionality includes feature tracking using a graphical square root smoothing SLAM algorithm, global localization using multiple EKF estimators, and knowledge adaptive mission execution. The global localization incorporates a unique robust matching criteria which utilizes both positive and negative information. Separate match hypotheses are maintained by each EKF estimator allowing all matching decisions to be reversible.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Yoerger, D., Jakuba, M., Bradley, A., Bingham, B.: Techniques for deep sea near bottom survey using an autonomous underwater vehicle. International Journal of Robotics Research 26(1), 41–54 (2007)
Whitcomb, L., Yoerger, D., Singh, H., Howland, J.: Advances in Underwater Robot Vehicles for Deep Ocean Exploration: Navigation, Control and Survery Operations. In: The Ninth International Symposium on Robotics Research. Springer, London (2000) (to appear)
Williams, S., Dissanayake, G., Durrant-Whyte, H.: Towards terrain-aided navigation for underwater robotics. Advanced Robotics-Utrecht 15, 533–550 (2001)
Ribas, D., Ridao, P., Neira, J., Tardos, J.: Slam using an imaging sonar for partially structured underwater environments. In: Proc. of the IEEE International Conference on Intelligent Robots and Systems (IROS 2006). IEEE, Los Alamitos (2006)
Leonard, J.J., Carpenter, R., Feder, H.J.S.: Stochastic mapping using forward look sonar. Robotica 19, 467–480 (2001)
Tena, I., de Raucourt, S., Petillot, Y., Lane, D.: Concurrent mapping and localization using sidescan sonar. IEEE Journal of Ocean Engineering 29(2), 442–456 (2004)
Fairfield, N., Kantor, G.A., Wettergreen, D.: Towards particle filter SLAM with three dimensional evidence grids in a flooded subterranean environment. In: Proceedings of ICRA 2006, May 2006, pp. 3575–3580 (2006)
Dissanayake, M.G., Newman, P., Clark, S., Durrant-Whyte, H., Corba, M.: A solution to the simultaneous localization and map building (SLAM) problem. IEEE Transactions on Robotics and Automation 17(3), 229–241 (2001)
Newman, P., Leonard, J., Rikoski, R.: Towards constant-time SLAM on an autonomous underwater vehicle using synthetic aperture sonar. In: Proc. of the International Symposium on Robotics Research, ISRR 2003 (2003)
Leonard, J., Rikoski, R., Newman, P., Bosse, M.: Mapping partially observable features from multiple uncertain vantage points. IJRR International Journal on Robotics Research 7(3), 943–975 (2002)
Hahnel, D., Burgard, W., Wegbreit, B., Thrun, S.: Towards lazy data association in SLAM. In: The 11th International Symposium of Robotics, vol. 15, pp. 421–431. Springer, Heidelberg (2005)
Dellaert, F.: Square root SAM: Simultaneous location and mapping via square root information smoothing. In: Robotics: Science and Systems (2005)
Dellaert, F., Kaess, M.: Square root SAM: Simultaneous location and mapping via square root information smoothing. International Journal of Robotics Reasearch 25(12), 1181–1203 (2006)
Folkesson, J., Leonard, J., Leederkerken, J., Williams, R.: Feature tracking for underwater navigation using sonar. In: Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007), pp. 3678–3684 (2007)
Folkesson, J., Leederkerken, J., Williams, R., Patrikalakis, A., Leonard, J.: A Feature Based Navigation System for an Autonomous Underwater Robot. In: Field and Service Robots, pp. 105–114. Springer, Heidelberg (2008)
Neira, J., Tardós, J.D.: Data association in stocastic mapping using the joint compatibility test. IEEE Transaction on Robotics and Automation 17(6), 890–897 (2001)
Folkesson, J., Christensen, H.I.: Closing the loop with graphical SLAM. IEEE Transactions on Robotics and Automation, 731–741 (2007)
Montemerlo, M., Thrun, S.: Simultaneous localization and mapping with unknown data association using FastSLAM. In: Proc. of the IEEE International Conference on Robotics and Automation (ICRA 2003), vol. 1, pp. 1985–1991 (2003)
Singh, H., Roman, C., Pizarro, O., Eustice, R.: Advances in high-resolution imaging from underwater vehicles. In: International Symposium of Robotics Research, San Francisco, CA, USA (October 2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Folkesson, J., Leonard, J. (2011). Autonomy through SLAM for an Underwater Robot. In: Pradalier, C., Siegwart, R., Hirzinger, G. (eds) Robotics Research. Springer Tracts in Advanced Robotics, vol 70. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19457-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-19457-3_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19456-6
Online ISBN: 978-3-642-19457-3
eBook Packages: EngineeringEngineering (R0)