Abstract
This paper describes a multiple hypothesis approach to concurrent mapping and localization (CML) for autonomous underwater vehicles (AUVs). The objective of CML is to enable a mobile robot to build a map of an unknown environment, while simultaneously using that map to navigate with bounded position error. Multiple hypothesis concurrent mapping and localization (MHCML) has potential to provide a theoretically consistent framework that incorporates navigation error, sensor noise, data association uncertainty, and physically-based sensor models. MHCML is fundamentally different from conventional multiple hypothesis tracking because multiple hypotheses are considered for both the location of the vehicle and the locations of features. New techniques for evaluation of decision dependencies and calculation of likelihoods for vehicle and feature tracks are introduced. Simulation results are presented to illustrate the viability of the approach for an AUV equipped with a forward-look sonar.
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References
Y. Bar-Shalom and T. E. Fortmann. Tracking and Data Association. Academic Press, 1988.
J. G. Bellingham, C. Chryssostomidis, M. Deffenbaugh, J. J. Leonard, and H. Schmidt. Arctic under-ice survey operations. In Proc. Int. Symp. on Unmanned Untethered Submersible Technology, pages 50–59, 1993.
K. Chang, S. Mori, and C. Chong. Performance evaluation of a multiple hypothesis multitarget tracking algorithm. In IEEE Int. Conference on Decision and Control (CDC), pages 2258–2263, 1990.
K. S. Chong and L. Kleeman. Sonar feature map building for a mobile robot. In Proc. IEEE Int. Conf. Robotics and Automation, 1997.
I. J. Cox and J. J. Leonard. Modeling a dynamic environment using a Bayesian multiple hypothesis approach. Artificial Intelligence, 66(2):311–344, April 1994.
I. J. Cox and M. L. Miller. On finding ranked assignments with application to multi-target tracking and motion correspondence. Technical report, NEC Research Institute, July 1993.
A. Elfes. Sonar-based real-world mapping and navigation. IEEE Journal of Robotics and Automation, RA-3(3):249–265, June 1987.
E. Geyer, P. Creamer, J. D’Appolito, and R. Gains. Characteristics and capabilities of navigation systems for unmanned untethered submersibles. In Proc. Int. Symp. on Unmanned Untethered Submersible Technology, pages 320–347, 1987.
T. Kurien. Issues in the design of practical multitarget tracking algorithms. In Y. Bar-Shalom, editor, Multitarget-Multisensor Tracking: Advanced Applications, pages 43–83. Boston: Artech House, 1990.
J. J. Leonard and H. F. Durrant-Whyte. Simultaneous map building and localization for an autonomous mobile robot. In Proc. IEEE Int. Workshop on Intelligent Robots and Systems, Osaka, Japan, 1991.
E. Levine, D. Connors, R. Shell, T. Gagliardi, and R. Hanson. Oceanographic mapping with the Navy’s large diameter UUV. Sea Technology, pages 49–57, 1995.
M. Medeiros and R. Carpenter. High resolution array signal processing for AUVs. In AUV 96, pages 10–15, 1996.
H. Moravec. Sensor fusion in certainty grids for mobile robots. In Sensor Devices and Systems for Robotics, pages 253–276. Springer-Verlag, 1989. Nato ASI Series.
S. Mori, C. Chong, E. Tse, and R. Wishner. Tracking and classifying multiple targets without a priori identification. IEEE Transactions on Automatic Control, AC-31(5), May 1986.
P. Moutarlier and R. Chatila. Stochastic multisensory data fusion for mobile robot location and environment modeling. In 5th Int. Symposium on Robotics Research, Tokyo, 1989.
F. Nussbaum, G. Stevens, and J. Kelly. Sensors for a forward-looking high resolution auv sonar. In AUV 96, pages 141–145, 1996.
D. B. Reid. An algorithm for tracking multiple targets. IEEE Transactions on Automatic Control, AC- 24(6), December 1979.
W. D. Rencken. Concurrent localisation and map building for mobile robots using ultrasonic sensors. In Proc. IEEE Int. Workshop on Intelligent Robots and Systems, pages 2192–2197, Yokohama, Japan, 1993.
H. Schmidt, J. Bellingham, M. Johnson, D. Herold, D. Farmer, and R. Pawlowcisz. Real-time frontal mapping with AUVs in a coastal environment. In IEEE Oceans, pages 1094–1098, 1996.
R. Smith, M. Self, and P. Cheeseman. Estimating uncertain spatial relationships in robotics. In I. Cox and G. Wilfong, editors, Autonomous Robot Vehicles. Springer-Verlag, 1990.
C. S. Smith, J. J. Leonard, A. A. Bennett, and C. Shaw. Concurrent mapping and localization for autonomous underwater vehicles. In Undersea Defence Technology, pages 338–342, 1997.
C. S. Smith, J. J. Leonard, A. A. Bennett, and C. Shaw. Feature-based concurrent mapping and localization for autonomous underwater vehicles. In IEEE Oceans, 1997.
W. K. Stewart. Multisensor Modeling Underwater with Uncertain Information. PhD thesis, Massachusetts Institute of Technology, 1988.
S. T. Tuohy, J. J. Leonard, J. G. Bellingham, N. M. Patrikalakis, and C. Chryssostomidis. Map based navigation for autonomous underwater vehicles. International Journal of Offshore and Polar Engineering, 6(1):9–18, March 1996.
J. Uhlmann. Dynamic Map Building and Localization: New Theoretical Foundations. PhD thesis, University of Oxford, 1995.
J. Vaganay, J. G. Bellingham, and J. J. Leonard. Outlier rejection for autonomous acoustic navigation. In Proc. IEEE Int. Conf. Robotics and Automation, pages 2174–2181, April 1996.
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Smith, C.M., Leonard, J.J. (1998). A Multiple-Hypothesis Approach to Concurrent Mapping and Localization for Autonomous Underwater Vehicles. In: Zelinsky, A. (eds) Field and Service Robotics. Springer, London. https://doi.org/10.1007/978-1-4471-1273-0_37
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DOI: https://doi.org/10.1007/978-1-4471-1273-0_37
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