Abstract
FastSLAM algorithm is one of the introduced Simultaneous Localization and Mapping (SLAM) algorithms for autonomous mobile robot. It decomposes the SLAM problem into one distinct localization problem and a collection of landmarks estimation problems. In recent discovery, FastSLAM suffers particle depletion problem which causes it to degenerate over time in terms of accuracy. In this work, a new hybrid approach is proposed by integrating two soft computing techniques that are genetic algorithm (GA) and particle swarm optimization (PSO) into FastSLAM. It is developed to overcome the particle depletion problem occur by improving the FastSLAM accuracy in terms of robot and landmark set position estimation. The experiment is conducted in simulation where the result is evaluated using root mean square error (RMSE) analysis. The experiment result shows that the proposed hybrid approach able to minimize the FastSLAM problem by reducing the degree of error occurs (RMSE value) during robot and landmark set position estimation.
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Acknowledgment
This work was supported by Fundamental Research Grant Scheme (FRGS) under grant number R.J130000.7828.4F860 funded by Ministry of Education (MOHE) under the Malaysian government for Faculty of Computing, Universiti Teknologi Malaysia (UTM).
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Khairuddin, A.R., Talib, M.S., Haron, H., Abdullah, M.Y.C. (2017). GA-PSO-FASTSLAM: A Hybrid Optimization Approach in Improving FastSLAM Performance. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_6
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DOI: https://doi.org/10.1007/978-3-319-53480-0_6
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