A Novel Combined SLAM Based on RBPF-SLAM and EIF-SLAM for Mobile System Sensing in a Large Scale Environment
Mobile autonomous systems are very important for marine scientific investigation and military applications. Many algorithms have been studied to deal with the computational efficiency problem required for large scale Simultaneous Localization and Mapping (SLAM) and its related accuracy and consisten...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2011-10-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/11/11/10197/ |
| _version_ | 1798039694023327744 |
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| author | Hongjin Zhang Yan Liang Tianhong Yan Tao Zhang Shujing Zhang Bo He |
| author_facet | Hongjin Zhang Yan Liang Tianhong Yan Tao Zhang Shujing Zhang Bo He |
| author_sort | Hongjin Zhang |
| collection | DOAJ |
| description | Mobile autonomous systems are very important for marine scientific investigation and military applications. Many algorithms have been studied to deal with the computational efficiency problem required for large scale Simultaneous Localization and Mapping (SLAM) and its related accuracy and consistency. Among these methods, submap-based SLAM is a more effective one. By combining the strength of two popular mapping algorithms, the Rao-Blackwellised particle filter (RBPF) and extended information filter (EIF), this paper presents a Combined SLAM—an efficient submap-based solution to the SLAM problem in a large scale environment. RBPF-SLAM is used to produce local maps, which are periodically fused into an EIF-SLAM algorithm. RBPF-SLAM can avoid linearization of the robot model during operating and provide a robust data association, while EIF-SLAM can improve the whole computational speed, and avoid the tendency of RBPF-SLAM to be over-confident. In order to further improve the computational speed in a real time environment, a binary-tree-based decision-making strategy is introduced. Simulation experiments show that the proposed Combined SLAM algorithm significantly outperforms currently existing algorithms in terms of accuracy and consistency, as well as the computing efficiency. Finally, the Combined SLAM algorithm is experimentally validated in a real environment by using the Victoria Park dataset. |
| first_indexed | 2024-04-11T21:57:17Z |
| format | Article |
| id | doaj.art-054272d368074d83a6f87424dd37b079 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:57:17Z |
| publishDate | 2011-10-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-054272d368074d83a6f87424dd37b0792022-12-22T04:01:03ZengMDPI AGSensors1424-82202011-10-011111101971021910.3390/s111110197A Novel Combined SLAM Based on RBPF-SLAM and EIF-SLAM for Mobile System Sensing in a Large Scale EnvironmentHongjin ZhangYan LiangTianhong YanTao ZhangShujing ZhangBo HeMobile autonomous systems are very important for marine scientific investigation and military applications. Many algorithms have been studied to deal with the computational efficiency problem required for large scale Simultaneous Localization and Mapping (SLAM) and its related accuracy and consistency. Among these methods, submap-based SLAM is a more effective one. By combining the strength of two popular mapping algorithms, the Rao-Blackwellised particle filter (RBPF) and extended information filter (EIF), this paper presents a Combined SLAM—an efficient submap-based solution to the SLAM problem in a large scale environment. RBPF-SLAM is used to produce local maps, which are periodically fused into an EIF-SLAM algorithm. RBPF-SLAM can avoid linearization of the robot model during operating and provide a robust data association, while EIF-SLAM can improve the whole computational speed, and avoid the tendency of RBPF-SLAM to be over-confident. In order to further improve the computational speed in a real time environment, a binary-tree-based decision-making strategy is introduced. Simulation experiments show that the proposed Combined SLAM algorithm significantly outperforms currently existing algorithms in terms of accuracy and consistency, as well as the computing efficiency. Finally, the Combined SLAM algorithm is experimentally validated in a real environment by using the Victoria Park dataset.http://www.mdpi.com/1424-8220/11/11/10197/RBPF-SLAMEIF-SLAMsubmapconsistencycomputational efficiency |
| spellingShingle | Hongjin Zhang Yan Liang Tianhong Yan Tao Zhang Shujing Zhang Bo He A Novel Combined SLAM Based on RBPF-SLAM and EIF-SLAM for Mobile System Sensing in a Large Scale Environment Sensors RBPF-SLAM EIF-SLAM submap consistency computational efficiency |
| title | A Novel Combined SLAM Based on RBPF-SLAM and EIF-SLAM for Mobile System Sensing in a Large Scale Environment |
| title_full | A Novel Combined SLAM Based on RBPF-SLAM and EIF-SLAM for Mobile System Sensing in a Large Scale Environment |
| title_fullStr | A Novel Combined SLAM Based on RBPF-SLAM and EIF-SLAM for Mobile System Sensing in a Large Scale Environment |
| title_full_unstemmed | A Novel Combined SLAM Based on RBPF-SLAM and EIF-SLAM for Mobile System Sensing in a Large Scale Environment |
| title_short | A Novel Combined SLAM Based on RBPF-SLAM and EIF-SLAM for Mobile System Sensing in a Large Scale Environment |
| title_sort | novel combined slam based on rbpf slam and eif slam for mobile system sensing in a large scale environment |
| topic | RBPF-SLAM EIF-SLAM submap consistency computational efficiency |
| url | http://www.mdpi.com/1424-8220/11/11/10197/ |
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