Abstract
The rationale of adopting multiple particle filters to solve the Simultaneous Localisation and Map-building (SLAM) problem is discussed in this paper. SLAM can be considered as a combined state and parameter estimation problem. The particle filtering based solution is not only more flexible than the established extended Kalman filtering method, but also offers computational advantages. Experimental results based on a standard SLAM data set verify the feasibility of the method.
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Yuen, D.C.K., MacDonald, B.A. (2004). Theoretical Considerations of Multiple Particle Filters for Simultaneous Localisation and Map-Building. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30132-5_32
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DOI: https://doi.org/10.1007/978-3-540-30132-5_32
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