A Fast Algorithm for Multi-Robot SLAM


Article Preview

Multi-robot SLAM has become a research hotspot in robot simultaneous localization and mapping (SLAM) in recent years. In this paper, we propose multi-robot exactly sparse extended information filters algorithm (MRESEIF) to solve Multi-robot SLAM problem. MRESEIF algorithm sets the threshold to divide the features which are observed by robots into two parts. One part of features are used on observation update, while the other one are used on robots relocalization. The simulation data shows that MRESEIF algorithm can solve multi-robot SLAM problem effectively, the oberservation update time and motion update time are kept in constant range, and the accuracy of robot poses is perfect.



Advanced Materials Research (Volumes 562-564)

Edited by:

Liu Feng




X. L. Xu et al., "A Fast Algorithm for Multi-Robot SLAM", Advanced Materials Research, Vols. 562-564, pp. 941-944, 2012

Online since:

August 2012




[1] Smith R, Self M, Cheeseman P. Estimating uncertain spatial relationships in robotics . Uncertainty in Artificial Intelligence, 1988, 2: 435-461J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, p.68.

[2] Walter M, Eustice R, Leonard J. Exactly sparse extended information filter for feature-based SLAM. The International Journal of Robotics Research. 2007, 4(26): 335-359K. Elissa, Title of paper if known, unpublished.

DOI: https://doi.org/10.1177/0278364906075026

[3] R. Eustice, M. Walter, and J. Leonard, Sparse extended information filters: Insights into sparsification, in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Edmonton, Alberta, Canada, August 2005, p.641.

DOI: https://doi.org/10.1109/iros.2005.1545053

[4] Walter M. Sparse Bayesian Information Filters for Localization and Mapping. United States: The Woods Hole Oceanographic Institution, 2008.M. Young, The Technical Writer's Handbook. Mill Valley, CA: University Science, (1989).