Abstract
Mobile robot localization is the problem of tracking a moving robot through an environment given inaccurate sensor data and knowledge of the robot’s motion. Monte Carlo Localization (MCL) is a popular probabilistic method of solving the localization problem. By using a Bayesian formulation of the problem, the robot’s belief is represented by a set of weighted samples and updated according to motion and sensor information. One problem with MCL is that it requires a static map of the environment. While it is robust to errors in the map, they necessarily make the results less accurate. This article presents a method for updating the map dynamically during the process of localization, without requiring a severe increase in running time. Ordinarily, if the environment changes, the map must be recreated with user input. With the approach described here, it is possible for the robot to dynamically update the map without requiring user intervention or a significant amount of processing.
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© 2005 Springer-Verlag Berlin Heidelberg
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Milstein, A. (2005). Dynamic Maps in Monte Carlo Localization. In: Kégl, B., Lapalme, G. (eds) Advances in Artificial Intelligence. Canadian AI 2005. Lecture Notes in Computer Science(), vol 3501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424918_1
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DOI: https://doi.org/10.1007/11424918_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25864-3
Online ISBN: 978-3-540-31952-8
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