GA-PSO-FASTSLAM: A hybrid optimization approach in improving fastSLAM performance
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 dep...
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| Format: | Conference or Workshop Item |
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2017
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| author | Khairuddin, Alif Ridzuan Talib, Mohamad Shukor Haron, Habibollah Che Abdullah, Muhamad Yazid |
| author_facet | Khairuddin, Alif Ridzuan Talib, Mohamad Shukor Haron, Habibollah Che Abdullah, Muhamad Yazid |
| author_sort | Khairuddin, Alif Ridzuan |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-03-05T21:10:44Z |
| format | Conference or Workshop Item |
| id | utm.eprints-97042 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T21:10:44Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | utm.eprints-970422022-09-15T04:17:15Z http://eprints.utm.my/97042/ GA-PSO-FASTSLAM: A hybrid optimization approach in improving fastSLAM performance Khairuddin, Alif Ridzuan Talib, Mohamad Shukor Haron, Habibollah Che Abdullah, Muhamad Yazid QA75 Electronic computers. Computer science 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. 2017 Conference or Workshop Item PeerReviewed Khairuddin, Alif Ridzuan and Talib, Mohamad Shukor and Haron, Habibollah and Che Abdullah, Muhamad Yazid (2017) GA-PSO-FASTSLAM: A hybrid optimization approach in improving fastSLAM performance. In: 16th International Conference on Intelligent Systems Design and Applications, ISDA 2016, 16 - 18 December 2016, Porto, Portugal. http://dx.doi.org/10.1007/978-3-319-53480-0_6 |
| spellingShingle | QA75 Electronic computers. Computer science Khairuddin, Alif Ridzuan Talib, Mohamad Shukor Haron, Habibollah Che Abdullah, Muhamad Yazid GA-PSO-FASTSLAM: A hybrid optimization approach in improving fastSLAM performance |
| title | GA-PSO-FASTSLAM: A hybrid optimization approach in improving fastSLAM performance |
| title_full | GA-PSO-FASTSLAM: A hybrid optimization approach in improving fastSLAM performance |
| title_fullStr | GA-PSO-FASTSLAM: A hybrid optimization approach in improving fastSLAM performance |
| title_full_unstemmed | GA-PSO-FASTSLAM: A hybrid optimization approach in improving fastSLAM performance |
| title_short | GA-PSO-FASTSLAM: A hybrid optimization approach in improving fastSLAM performance |
| title_sort | ga pso fastslam a hybrid optimization approach in improving fastslam performance |
| topic | QA75 Electronic computers. Computer science |
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