Bathymetric Particle Filter SLAM With Graph-Based Trajectory Update Method
A graph-based particle filter bathymetric simultaneous localization and mapping (BSLAM) method is proposed to solve the oscillation problem of the trajectories estimated by particles when using a low precise vehicle motion model and obtain accurate navigation results for autonomous underwater vehicl...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9452183/ |
| _version_ | 1818915033644531712 |
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| author | Qianyi Zhang Ye Li Teng Ma Zheng Cong Wenjun Zhang |
| author_facet | Qianyi Zhang Ye Li Teng Ma Zheng Cong Wenjun Zhang |
| author_sort | Qianyi Zhang |
| collection | DOAJ |
| description | A graph-based particle filter bathymetric simultaneous localization and mapping (BSLAM) method is proposed to solve the oscillation problem of the trajectories estimated by particles when using a low precise vehicle motion model and obtain accurate navigation results for autonomous underwater vehicles (AUVs). A graph-based trajectory update method is proposed to update the trajectories stored in particles before particle weighting to weaken the influence of the low precise odometer model on the particle trajectories. A particle weighting method based on submap matching is proposed to improve the robustness of the particle filter. Besides, a graph-based map generation method is proposed to solve the map selection problem of the particle filtering theory. The performance of the proposed method is demonstrated using a simulated dataset and a field dataset collected from a sea trial. The results show that the proposed method is more accurate and effective compared with a state-of-art particle filter BSLAM method. |
| first_indexed | 2024-12-19T23:55:51Z |
| format | Article |
| id | doaj.art-d3001da0dad149a0944fb8aab1fe3819 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T23:55:51Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d3001da0dad149a0944fb8aab1fe38192022-12-21T20:01:01ZengIEEEIEEE Access2169-35362021-01-019854648547510.1109/ACCESS.2021.30885419452183Bathymetric Particle Filter SLAM With Graph-Based Trajectory Update MethodQianyi Zhang0https://orcid.org/0000-0001-8509-7136Ye Li1https://orcid.org/0000-0002-6917-8865Teng Ma2https://orcid.org/0000-0002-5609-7388Zheng Cong3https://orcid.org/0000-0002-0195-4880Wenjun Zhang4Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin, ChinaScience and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin, ChinaScience and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin, ChinaScience and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin, ChinaScience and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin, ChinaA graph-based particle filter bathymetric simultaneous localization and mapping (BSLAM) method is proposed to solve the oscillation problem of the trajectories estimated by particles when using a low precise vehicle motion model and obtain accurate navigation results for autonomous underwater vehicles (AUVs). A graph-based trajectory update method is proposed to update the trajectories stored in particles before particle weighting to weaken the influence of the low precise odometer model on the particle trajectories. A particle weighting method based on submap matching is proposed to improve the robustness of the particle filter. Besides, a graph-based map generation method is proposed to solve the map selection problem of the particle filtering theory. The performance of the proposed method is demonstrated using a simulated dataset and a field dataset collected from a sea trial. The results show that the proposed method is more accurate and effective compared with a state-of-art particle filter BSLAM method.https://ieeexplore.ieee.org/document/9452183/Autonomous underwater vehiclebathymetric simultaneous localization and mappingparticle filterpose graph optimization |
| spellingShingle | Qianyi Zhang Ye Li Teng Ma Zheng Cong Wenjun Zhang Bathymetric Particle Filter SLAM With Graph-Based Trajectory Update Method IEEE Access Autonomous underwater vehicle bathymetric simultaneous localization and mapping particle filter pose graph optimization |
| title | Bathymetric Particle Filter SLAM With Graph-Based Trajectory Update Method |
| title_full | Bathymetric Particle Filter SLAM With Graph-Based Trajectory Update Method |
| title_fullStr | Bathymetric Particle Filter SLAM With Graph-Based Trajectory Update Method |
| title_full_unstemmed | Bathymetric Particle Filter SLAM With Graph-Based Trajectory Update Method |
| title_short | Bathymetric Particle Filter SLAM With Graph-Based Trajectory Update Method |
| title_sort | bathymetric particle filter slam with graph based trajectory update method |
| topic | Autonomous underwater vehicle bathymetric simultaneous localization and mapping particle filter pose graph optimization |
| url | https://ieeexplore.ieee.org/document/9452183/ |
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