Abstract
Decoupled stochastic mapping (DSM) is a computationally efficient approach to large-scale concurrent mapping and localization. DSM reduces the computational burden of conventional stochastic mapping by dividing the environment into multiple overlapping submap regions, each with its own stochastic map. Two new approximation techniques are utilized for transferring vehicle state information from one submap to another, yielding a constant-time algorithm whose memory requirements scale linearly with the size of the operating area. The performance of two different variations of the algorithm is demonstrated through simulations of environments with 110 and 1200 features. Experimental results are presented for an environment with 93 features using sonar data obtained in a 3 by 9 by 1 meter testing tank.
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References
R. A Brooks. Aspects of mobile robot visual map making. In Second Int. Symp. Robotics Research, Tokyo, Japan, 1984. MIT Press.
R. Chatila and J.P. Laumond. Position referencing and consistent world modeling for mobile robots. In IEEE International Conference on Robotics and Automation. IEEE, 1985.
M. W. M. G. Dissanayake, P. Newman, H. F. Durrant-Whyte, S. Clark, and M. Csorba. A solution to the simultaneous localization and map building (SLAM) problem. In Sixth International Symposium on Experimental Robotics, March 1999.
R. Smith, M. Self, and P. Cheeseman. A stochastic map for uncertain spatial relationships. In 4th International Symposium on Robotics Research. MIT Press, 1987.
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.
J. J. Leonard and H. E Durrant-Whyte. Simultaneous map building and localization for an autonomous mobile robot. In Proc. IEEE Int. Workshop on Intelligent Robots and Systems, pages 1442–1447, Osaka, Japan, 1991.
J. K. Uhlmann, S. J. Julier and M. Csorba. Nondivergent Simultaneous Map Building and Localisation using Covariance Intersection. Navigation and Control Technologies for Unmanned Systems II, 1997.
J. A. Castellanos, J. D. Tards, and G. Schmidt. Building a global map of the environment of a mobile robot: The importance of correlations. In Proc. IEEE Int. Conf Robotics and Automation, pages 1053–1059, 1997.
R. N. Carpenter. Concurrent mapping and localization using forward look sonar. In IEEE AUV, Cambridge, MA, August 1998.
H. J. S. Feder, C. M. Smith, and J. J. Leonard. Incorporating environmental measurements in navigation. In IEEE AUV, Cambridge, MA, August 1998.
H. J. S. Feder, J. J. Leonard, and C. M. Smith. Adaptive mobile robot navigation and mapping. Int. J. Robotics Research, 18 (7): 650–668, July 1999.
J. J. Leonard and H. J. S. Feder. Decoupled stochastic mapping. Marine Robotics Laboratory Technical Report 99–1, Massachusetts Institute of Technology, 1999.
Y. Bar-Shalom and X. R. Li. Estimation and Tracking: Principles, Techniques, and Software. Artech House, 1993.
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© 2000 Springer-Verlag London
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Leonard, J.J., Feder, H.J.S. (2000). A Computationally Efficient Method for Large-Scale Concurrent Mapping and Localization. In: Hollerbach, J.M., Koditschek, D.E. (eds) Robotics Research. Springer, London. https://doi.org/10.1007/978-1-4471-0765-1_21
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DOI: https://doi.org/10.1007/978-1-4471-0765-1_21
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