Grey Wolf Resampling-Based Rao-Blackwellized Particle Filter for Mobile Robot Simultaneous Localization and Mapping
An artificial intelligent grey wolf optimizer (GWO)-assisted resampling scheme is applied to the Rao-Blackwellized particle filter (RBPF) in the simultaneous localization and mapping (SLAM). By doing this, we can make the diversity of the particles resampling and then obtain a better localization ac...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2021/4978984 |
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| _version_ | 1849304359963197440 |
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| author | Yong Dai Ming Zhao |
| author_facet | Yong Dai Ming Zhao |
| author_sort | Yong Dai |
| collection | DOAJ |
| description | An artificial intelligent grey wolf optimizer (GWO)-assisted resampling scheme is applied to the Rao-Blackwellized particle filter (RBPF) in the simultaneous localization and mapping (SLAM). By doing this, we can make the diversity of the particles resampling and then obtain a better localization accuracy and fast convergence to realize indoor mobile robot SLAM. In addition, we propose an adaptive local data association (Range-SLAM) scheme to improve the computational efficiency for the algorithm of the nearest neighbor (NN) data association in the iteration of the RBPF prediction. Through the experiment and simulations, the proposed SLAM schemes have fast convergence, accuracy, and heuristics. Therefore, the improved RBPF and new data association schemes presented in this paper can provide a feasible method for the indoor mobile robot SLAM. |
| format | Article |
| id | doaj-art-112e8fb2dfc740b592ba6a39dc9d4d54 |
| institution | Kabale University |
| issn | 1687-9619 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-112e8fb2dfc740b592ba6a39dc9d4d542025-08-20T03:55:45ZengWileyJournal of Robotics1687-96192021-01-01202110.1155/2021/49789844978984Grey Wolf Resampling-Based Rao-Blackwellized Particle Filter for Mobile Robot Simultaneous Localization and MappingYong Dai0Ming Zhao1School of Automation and Electrical EngineeringSchool of Applied TechnologyAn artificial intelligent grey wolf optimizer (GWO)-assisted resampling scheme is applied to the Rao-Blackwellized particle filter (RBPF) in the simultaneous localization and mapping (SLAM). By doing this, we can make the diversity of the particles resampling and then obtain a better localization accuracy and fast convergence to realize indoor mobile robot SLAM. In addition, we propose an adaptive local data association (Range-SLAM) scheme to improve the computational efficiency for the algorithm of the nearest neighbor (NN) data association in the iteration of the RBPF prediction. Through the experiment and simulations, the proposed SLAM schemes have fast convergence, accuracy, and heuristics. Therefore, the improved RBPF and new data association schemes presented in this paper can provide a feasible method for the indoor mobile robot SLAM.http://dx.doi.org/10.1155/2021/4978984 |
| spellingShingle | Yong Dai Ming Zhao Grey Wolf Resampling-Based Rao-Blackwellized Particle Filter for Mobile Robot Simultaneous Localization and Mapping Journal of Robotics |
| title | Grey Wolf Resampling-Based Rao-Blackwellized Particle Filter for Mobile Robot Simultaneous Localization and Mapping |
| title_full | Grey Wolf Resampling-Based Rao-Blackwellized Particle Filter for Mobile Robot Simultaneous Localization and Mapping |
| title_fullStr | Grey Wolf Resampling-Based Rao-Blackwellized Particle Filter for Mobile Robot Simultaneous Localization and Mapping |
| title_full_unstemmed | Grey Wolf Resampling-Based Rao-Blackwellized Particle Filter for Mobile Robot Simultaneous Localization and Mapping |
| title_short | Grey Wolf Resampling-Based Rao-Blackwellized Particle Filter for Mobile Robot Simultaneous Localization and Mapping |
| title_sort | grey wolf resampling based rao blackwellized particle filter for mobile robot simultaneous localization and mapping |
| url | http://dx.doi.org/10.1155/2021/4978984 |
| work_keys_str_mv | AT yongdai greywolfresamplingbasedraoblackwellizedparticlefilterformobilerobotsimultaneouslocalizationandmapping AT mingzhao greywolfresamplingbasedraoblackwellizedparticlefilterformobilerobotsimultaneouslocalizationandmapping |