Distributed SLAM Using Improved Particle Filter for Mobile Robot Localization
The distributed SLAM system has a similar estimation performance and requires only one-fifth of the computation time compared with centralized particle filter. However, particle impoverishment is inevitably because of the random particles prediction and resampling applied in generic particle filter,...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/239531 |
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| author | Fujun Pei Mei Wu Simin Zhang |
| author_facet | Fujun Pei Mei Wu Simin Zhang |
| author_sort | Fujun Pei |
| collection | DOAJ |
| description | The distributed SLAM system has a similar estimation performance and requires only one-fifth of the computation time compared with centralized particle filter. However, particle impoverishment is inevitably because of the random particles prediction and resampling applied in generic particle filter, especially in SLAM problem that involves a large number of dimensions. In this paper, particle filter use in distributed SLAM was improved in two aspects. First, we improved the important function of the local filters in particle filter. The adaptive values were used to replace a set of constants in the computational process of importance function, which improved the robustness of the particle filter. Second, an information fusion method was proposed by mixing the innovation method and the number of effective particles method, which combined the advantages of these two methods. And this paper extends the previously known convergence results for particle filter to prove that improved particle filter converges to the optimal filter in mean square as the number of particles goes to infinity. The experiment results show that the proposed algorithm improved the virtue of the DPF-SLAM system in isolate faults and enabled the system to have a better tolerance and robustness. |
| format | Article |
| id | doaj-art-14e81cb3ece04fc5b500d035d30991d2 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-14e81cb3ece04fc5b500d035d30991d22025-02-03T01:29:23ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/239531239531Distributed SLAM Using Improved Particle Filter for Mobile Robot LocalizationFujun Pei0Mei Wu1Simin Zhang2School of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, ChinaSchool of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, ChinaSchool of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, ChinaThe distributed SLAM system has a similar estimation performance and requires only one-fifth of the computation time compared with centralized particle filter. However, particle impoverishment is inevitably because of the random particles prediction and resampling applied in generic particle filter, especially in SLAM problem that involves a large number of dimensions. In this paper, particle filter use in distributed SLAM was improved in two aspects. First, we improved the important function of the local filters in particle filter. The adaptive values were used to replace a set of constants in the computational process of importance function, which improved the robustness of the particle filter. Second, an information fusion method was proposed by mixing the innovation method and the number of effective particles method, which combined the advantages of these two methods. And this paper extends the previously known convergence results for particle filter to prove that improved particle filter converges to the optimal filter in mean square as the number of particles goes to infinity. The experiment results show that the proposed algorithm improved the virtue of the DPF-SLAM system in isolate faults and enabled the system to have a better tolerance and robustness.http://dx.doi.org/10.1155/2014/239531 |
| spellingShingle | Fujun Pei Mei Wu Simin Zhang Distributed SLAM Using Improved Particle Filter for Mobile Robot Localization The Scientific World Journal |
| title | Distributed SLAM Using Improved Particle Filter for Mobile Robot Localization |
| title_full | Distributed SLAM Using Improved Particle Filter for Mobile Robot Localization |
| title_fullStr | Distributed SLAM Using Improved Particle Filter for Mobile Robot Localization |
| title_full_unstemmed | Distributed SLAM Using Improved Particle Filter for Mobile Robot Localization |
| title_short | Distributed SLAM Using Improved Particle Filter for Mobile Robot Localization |
| title_sort | distributed slam using improved particle filter for mobile robot localization |
| url | http://dx.doi.org/10.1155/2014/239531 |
| work_keys_str_mv | AT fujunpei distributedslamusingimprovedparticlefilterformobilerobotlocalization AT meiwu distributedslamusingimprovedparticlefilterformobilerobotlocalization AT siminzhang distributedslamusingimprovedparticlefilterformobilerobotlocalization |