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Multiple Mobile Robot SLAM for collaborative mapping and exploration
Over the past five decades, Autonomous Mobile Robots (AMRs) have been an active research field. Maps of high accuracy are required for AMRs to operate successfully. In addition to this, AMRs needs to localise themselves reliably relative to the map. Simultaneous Localisation and Mapping (SLAM) addre...
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| Format: | Thesis |
| Language: | English |
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Department of Electrical Engineering
2022
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| _version_ | 1867613219445538816 |
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| access_status_str | Open Access |
| author | Dikoko, Boitumelo |
| author2 | Verrinder, Robyn |
| author_browse | Dikoko, Boitumelo Verrinder, Robyn |
| author_facet | Verrinder, Robyn Dikoko, Boitumelo |
| author_sort | Dikoko, Boitumelo |
| collection | Thesis |
| description | Over the past five decades, Autonomous Mobile Robots (AMRs) have been an active research field. Maps of high accuracy are required for AMRs to operate successfully. In addition to this, AMRs needs to localise themselves reliably relative to the map. Simultaneous Localisation and Mapping (SLAM) address the problem of both map building and robot localisation. When exploring large areas, Multi-Robot SLAM (MRSLAM) has the potential to be far more efficient and robust, while sharing the computational burden across robots. However, MRSLAM encounters issues such as difficulty in map fusion of multi-resolution maps, and unknown relative positions of the robots. This thesis describes a distributed multi-resolution map merging algorithm for MRSLAM. HectorSLAM, which is one of many single robot SLAM implementations, has demonstrated exceptional results and was selected as the basis for the MRSLAM implementation in this project. We consider the environment to be three-dimensional with the maps being constrained to a two-dimensional plane. Each robot is equipped with a laser range sensor for perception and has no information regarding the relative positioning of the other robots. The experiments were conducted both in simulation and a real-world environment. Up-to three robots were placed in the same environment with Hector-SLAM running, the local maps and localisation were then sent to a central node, which attempted to find map overlaps and merge the resulting maps. When evaluating the success of the map merging algorithm, the quality of the map from each robot was interrogated. Experiments conducted on up to three AMRs show the effectiveness of the proposed algorithms in an indoor environment. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/35587 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:39.476Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/35587 Multiple Mobile Robot SLAM for collaborative mapping and exploration Dikoko, Boitumelo Verrinder, Robyn Boje, Edward Multiple Robot SLAM Map Merging Multi-Session Mapping Over the past five decades, Autonomous Mobile Robots (AMRs) have been an active research field. Maps of high accuracy are required for AMRs to operate successfully. In addition to this, AMRs needs to localise themselves reliably relative to the map. Simultaneous Localisation and Mapping (SLAM) address the problem of both map building and robot localisation. When exploring large areas, Multi-Robot SLAM (MRSLAM) has the potential to be far more efficient and robust, while sharing the computational burden across robots. However, MRSLAM encounters issues such as difficulty in map fusion of multi-resolution maps, and unknown relative positions of the robots. This thesis describes a distributed multi-resolution map merging algorithm for MRSLAM. HectorSLAM, which is one of many single robot SLAM implementations, has demonstrated exceptional results and was selected as the basis for the MRSLAM implementation in this project. We consider the environment to be three-dimensional with the maps being constrained to a two-dimensional plane. Each robot is equipped with a laser range sensor for perception and has no information regarding the relative positioning of the other robots. The experiments were conducted both in simulation and a real-world environment. Up-to three robots were placed in the same environment with Hector-SLAM running, the local maps and localisation were then sent to a central node, which attempted to find map overlaps and merge the resulting maps. When evaluating the success of the map merging algorithm, the quality of the map from each robot was interrogated. Experiments conducted on up to three AMRs show the effectiveness of the proposed algorithms in an indoor environment. 2022-01-26T11:31:30Z 2022-01-26T11:31:30Z 2021 2022-01-26T09:20:51Z Master Thesis Masters MSc http://hdl.handle.net/11427/35587 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment |
| spellingShingle | Multiple Robot SLAM Map Merging Multi-Session Mapping Dikoko, Boitumelo Multiple Mobile Robot SLAM for collaborative mapping and exploration |
| thesis_degree_str | Master's |
| title | Multiple Mobile Robot SLAM for collaborative mapping and exploration |
| title_full | Multiple Mobile Robot SLAM for collaborative mapping and exploration |
| title_fullStr | Multiple Mobile Robot SLAM for collaborative mapping and exploration |
| title_full_unstemmed | Multiple Mobile Robot SLAM for collaborative mapping and exploration |
| title_short | Multiple Mobile Robot SLAM for collaborative mapping and exploration |
| title_sort | multiple mobile robot slam for collaborative mapping and exploration |
| topic | Multiple Robot SLAM Map Merging Multi-Session Mapping |
| url | http://hdl.handle.net/11427/35587 |
| work_keys_str_mv | AT dikokoboitumelo multiplemobilerobotslamforcollaborativemappingandexploration |