by Richard J. Rikoski, John J. Leonard, Paul M. Newman
in Proc. IEEE Int. Conf. Robotics and Automation, 2002
http://albacore.mit.edu/~jleonard/pubs/rikoski_newman_leonard_paper816.pdf
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Abstract:
Abstract — Stochastic mapping is an approach to the concurrent mapping and localization (CML) problem. The approach is powerful because feature and robot states are explicitly correlated. Improving the estimate of any state automatically improves the estimates of correlated states. This paper describes a number of extensions to the stochastic mapping framework, which are made possible by the incorporation of past vehicle states into the state vector to explicitly represent the robot’s trajectory. Having access to past robot states simplifies mapping, navigation, and cooperation. Experimental results using sonar data are presented. I.
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