Information-Fusion Methods Based Simultaneous Localization and Mapping for Robot Adapting to Search and Rescue Postdisaster Environments
The first application of utilizing unique information-fusion SLAM (IF-SLAM) methods is developed for mobile robots performing simultaneous localization and mapping (SLAM) adapting to search and rescue (SAR) environments in this paper. Several fusion approaches, parallel measurements filtering, explo...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2018/4218324 |
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| _version_ | 1832560939921047552 |
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| author | Hongling Wang Chengjin Zhang Yong Song Bao Pang |
| author_facet | Hongling Wang Chengjin Zhang Yong Song Bao Pang |
| author_sort | Hongling Wang |
| collection | DOAJ |
| description | The first application of utilizing unique information-fusion SLAM (IF-SLAM) methods is developed for mobile robots performing simultaneous localization and mapping (SLAM) adapting to search and rescue (SAR) environments in this paper. Several fusion approaches, parallel measurements filtering, exploration trajectories fusing, and combination sensors’ measurements and mobile robots’ trajectories, are proposed. The novel integration particle filter (IPF) and optimal improved EKF (IEKF) algorithms are derived for information-fusion systems to perform SLAM task in SAR scenarios. The information-fusion architecture consists of multirobots and multisensors (MAM); multiple robots mount on-board laser range finder (LRF) sensors, localization sonars, gyro odometry, Kinect-sensor, RGB-D camera, and other proprioceptive sensors. This information-fusion SLAM (IF-SLAM) is compared with conventional methods, which indicates that fusion trajectory is more consistent with estimated trajectories and real observation trajectories. The simulations and experiments of SLAM process are conducted in both cluttered indoor environment and outdoor collapsed unstructured scenario, and experimental results validate the effectiveness of the proposed information-fusion methods in improving SLAM performances adapting to SAR scenarios. |
| format | Article |
| id | doaj-art-42deb3e0558c4b368e0874c73e0ea0c1 |
| institution | Kabale University |
| issn | 1687-9600 1687-9619 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-42deb3e0558c4b368e0874c73e0ea0c12025-02-03T01:26:22ZengWileyJournal of Robotics1687-96001687-96192018-01-01201810.1155/2018/42183244218324Information-Fusion Methods Based Simultaneous Localization and Mapping for Robot Adapting to Search and Rescue Postdisaster EnvironmentsHongling Wang0Chengjin Zhang1Yong Song2Bao Pang3School of Control Science and Engineering, Shandong University, Jinan 250101, ChinaSchool of Control Science and Engineering, Shandong University, Jinan 250101, ChinaSchool of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, Weihai 264209, ChinaSchool of Control Science and Engineering, Shandong University, Jinan 250101, ChinaThe first application of utilizing unique information-fusion SLAM (IF-SLAM) methods is developed for mobile robots performing simultaneous localization and mapping (SLAM) adapting to search and rescue (SAR) environments in this paper. Several fusion approaches, parallel measurements filtering, exploration trajectories fusing, and combination sensors’ measurements and mobile robots’ trajectories, are proposed. The novel integration particle filter (IPF) and optimal improved EKF (IEKF) algorithms are derived for information-fusion systems to perform SLAM task in SAR scenarios. The information-fusion architecture consists of multirobots and multisensors (MAM); multiple robots mount on-board laser range finder (LRF) sensors, localization sonars, gyro odometry, Kinect-sensor, RGB-D camera, and other proprioceptive sensors. This information-fusion SLAM (IF-SLAM) is compared with conventional methods, which indicates that fusion trajectory is more consistent with estimated trajectories and real observation trajectories. The simulations and experiments of SLAM process are conducted in both cluttered indoor environment and outdoor collapsed unstructured scenario, and experimental results validate the effectiveness of the proposed information-fusion methods in improving SLAM performances adapting to SAR scenarios.http://dx.doi.org/10.1155/2018/4218324 |
| spellingShingle | Hongling Wang Chengjin Zhang Yong Song Bao Pang Information-Fusion Methods Based Simultaneous Localization and Mapping for Robot Adapting to Search and Rescue Postdisaster Environments Journal of Robotics |
| title | Information-Fusion Methods Based Simultaneous Localization and Mapping for Robot Adapting to Search and Rescue Postdisaster Environments |
| title_full | Information-Fusion Methods Based Simultaneous Localization and Mapping for Robot Adapting to Search and Rescue Postdisaster Environments |
| title_fullStr | Information-Fusion Methods Based Simultaneous Localization and Mapping for Robot Adapting to Search and Rescue Postdisaster Environments |
| title_full_unstemmed | Information-Fusion Methods Based Simultaneous Localization and Mapping for Robot Adapting to Search and Rescue Postdisaster Environments |
| title_short | Information-Fusion Methods Based Simultaneous Localization and Mapping for Robot Adapting to Search and Rescue Postdisaster Environments |
| title_sort | information fusion methods based simultaneous localization and mapping for robot adapting to search and rescue postdisaster environments |
| url | http://dx.doi.org/10.1155/2018/4218324 |
| work_keys_str_mv | AT honglingwang informationfusionmethodsbasedsimultaneouslocalizationandmappingforrobotadaptingtosearchandrescuepostdisasterenvironments AT chengjinzhang informationfusionmethodsbasedsimultaneouslocalizationandmappingforrobotadaptingtosearchandrescuepostdisasterenvironments AT yongsong informationfusionmethodsbasedsimultaneouslocalizationandmappingforrobotadaptingtosearchandrescuepostdisasterenvironments AT baopang informationfusionmethodsbasedsimultaneouslocalizationandmappingforrobotadaptingtosearchandrescuepostdisasterenvironments |