Multi-Robot SLAM Using Fast LiDAR Odometry and Mapping
This paper presents an approach to implem enting centralized multirobot simultaneous localization and mapping (MR-SLAM) in an unknown environment based on LiDAR sensors. The suggested implementation addresses two main challenges faced in MR-SLAM, particularly in real-time applications: computing com...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
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| Series: | Designs |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2411-9660/7/5/110 |
| _version_ | 1797574127432761344 |
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| author | Basma Ahmed Jalil Ibraheem Kasim Ibraheem |
| author_facet | Basma Ahmed Jalil Ibraheem Kasim Ibraheem |
| author_sort | Basma Ahmed Jalil |
| collection | DOAJ |
| description | This paper presents an approach to implem enting centralized multirobot simultaneous localization and mapping (MR-SLAM) in an unknown environment based on LiDAR sensors. The suggested implementation addresses two main challenges faced in MR-SLAM, particularly in real-time applications: computing complexity (solving the problem with minimum time and resources) and map merging (finding the alignment between the maps and merging maps by integrating information from the aligned maps into one map). The proposed approach integrates Fast LiDAR and Odometry Mapping (FLOAM), which reduces the computational complexity of localization and mapping for individual robots by adopting a non-iterative two-stage distortion compensation method. This, in turn, accelerates inputs for the map merging algorithm and expedites the creation of a comprehensive map. The map merging algorithm utilizes feature matching techniques, Singular Value Decomposition (SVD), and the Iterative Closest Point (ICP) algorithm to estimate the transformation between the maps. Subsequently, the algorithm employs a map-merging graph to estimate the global transformation. Our system has been designed to utilize two robots and has been evaluated on datasets and in a simulated environment using ROS and Gazebo. The system required less computing time to build the global map and achieved good estimation accuracy. |
| first_indexed | 2024-03-10T21:19:29Z |
| format | Article |
| id | doaj.art-4129621e3b75403383e78550d928b3dc |
| institution | Directory Open Access Journal |
| issn | 2411-9660 |
| language | English |
| last_indexed | 2024-03-10T21:19:29Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Designs |
| spelling | doaj.art-4129621e3b75403383e78550d928b3dc2023-11-19T16:11:58ZengMDPI AGDesigns2411-96602023-09-017511010.3390/designs7050110Multi-Robot SLAM Using Fast LiDAR Odometry and MappingBasma Ahmed Jalil0Ibraheem Kasim Ibraheem1Department of Computer Engineering, Faculty of Engineering, Mosul University, Mosul 41002, IraqDepartment of Electrical Engineering, College of Engineering, University of Baghdad, Baghdad 10071, IraqThis paper presents an approach to implem enting centralized multirobot simultaneous localization and mapping (MR-SLAM) in an unknown environment based on LiDAR sensors. The suggested implementation addresses two main challenges faced in MR-SLAM, particularly in real-time applications: computing complexity (solving the problem with minimum time and resources) and map merging (finding the alignment between the maps and merging maps by integrating information from the aligned maps into one map). The proposed approach integrates Fast LiDAR and Odometry Mapping (FLOAM), which reduces the computational complexity of localization and mapping for individual robots by adopting a non-iterative two-stage distortion compensation method. This, in turn, accelerates inputs for the map merging algorithm and expedites the creation of a comprehensive map. The map merging algorithm utilizes feature matching techniques, Singular Value Decomposition (SVD), and the Iterative Closest Point (ICP) algorithm to estimate the transformation between the maps. Subsequently, the algorithm employs a map-merging graph to estimate the global transformation. Our system has been designed to utilize two robots and has been evaluated on datasets and in a simulated environment using ROS and Gazebo. The system required less computing time to build the global map and achieved good estimation accuracy.https://www.mdpi.com/2411-9660/7/5/110MR-SLAMROSfeature matchingliDAR SLAMMap MergeGazebo |
| spellingShingle | Basma Ahmed Jalil Ibraheem Kasim Ibraheem Multi-Robot SLAM Using Fast LiDAR Odometry and Mapping Designs MR-SLAM ROS feature matching liDAR SLAM Map Merge Gazebo |
| title | Multi-Robot SLAM Using Fast LiDAR Odometry and Mapping |
| title_full | Multi-Robot SLAM Using Fast LiDAR Odometry and Mapping |
| title_fullStr | Multi-Robot SLAM Using Fast LiDAR Odometry and Mapping |
| title_full_unstemmed | Multi-Robot SLAM Using Fast LiDAR Odometry and Mapping |
| title_short | Multi-Robot SLAM Using Fast LiDAR Odometry and Mapping |
| title_sort | multi robot slam using fast lidar odometry and mapping |
| topic | MR-SLAM ROS feature matching liDAR SLAM Map Merge Gazebo |
| url | https://www.mdpi.com/2411-9660/7/5/110 |
| work_keys_str_mv | AT basmaahmedjalil multirobotslamusingfastlidarodometryandmapping AT ibraheemkasimibraheem multirobotslamusingfastlidarodometryandmapping |