Abstract
In order to mend the problem of particle filter’s sample depletion, the paper introduces the adaptive algorithms into FastSLAM. Besides using selection, crossover and mutation operation of genetic algorithm to improve the diversity of samples, this algorithm imports adaptive controlling parameters to overcome the premature convergence at the same time protect the excellent individual. Theoretical analysis and simulation experiments show that the algorithm can effectively improve the accuracy of simultaneous localization and localization.
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Xia, Ym., Yang, Ym. (2011). An Improved FastSLAM Algorithm Based on Genetic Algorithms. In: Qi, L. (eds) Information and Automation. ISIA 2010. Communications in Computer and Information Science, vol 86. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19853-3_43
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DOI: https://doi.org/10.1007/978-3-642-19853-3_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19852-6
Online ISBN: 978-3-642-19853-3
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