A Review of 2D Lidar SLAM Research
Two-dimensional (2D) simultaneous localization and mapping (SLAM) is a key technology for intelligent indoor robots. By using a map generated via SLAM, the robot can navigate and perform specific tasks. This paper reviews the progress of 2D Lidar SLAM algorithms based on four principles: filter-base...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/7/1214 |
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| author | Yingying Ran Xiaobin Xu Zhiying Tan Minzhou Luo |
| author_facet | Yingying Ran Xiaobin Xu Zhiying Tan Minzhou Luo |
| author_sort | Yingying Ran |
| collection | DOAJ |
| description | Two-dimensional (2D) simultaneous localization and mapping (SLAM) is a key technology for intelligent indoor robots. By using a map generated via SLAM, the robot can navigate and perform specific tasks. This paper reviews the progress of 2D Lidar SLAM algorithms based on four principles: filter-based SLAM, matching-based SLAM, graph optimization-based SLAM, and deep learning-based SLAM, highlighting their advantages, disadvantages, and applicability. Additionally, two key research topics in 2D Lidar SLAM are presented: solutions for dynamic objects during mapping and the fusion of 2D Lidar and vision data. Finally, the development trends of 2D SLAM are discussed. |
| format | Article |
| id | doaj-art-b5d6b00bdcca41b4bc71ca2d00dbef41 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-b5d6b00bdcca41b4bc71ca2d00dbef412025-08-20T03:08:59ZengMDPI AGRemote Sensing2072-42922025-03-01177121410.3390/rs17071214A Review of 2D Lidar SLAM ResearchYingying Ran0Xiaobin Xu1Zhiying Tan2Minzhou Luo3College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213200, ChinaCollege of Mechanical and Electrical Engineering, Hohai University, Changzhou 213200, ChinaCollege of Mechanical and Electrical Engineering, Hohai University, Changzhou 213200, ChinaCollege of Mechanical and Electrical Engineering, Hohai University, Changzhou 213200, ChinaTwo-dimensional (2D) simultaneous localization and mapping (SLAM) is a key technology for intelligent indoor robots. By using a map generated via SLAM, the robot can navigate and perform specific tasks. This paper reviews the progress of 2D Lidar SLAM algorithms based on four principles: filter-based SLAM, matching-based SLAM, graph optimization-based SLAM, and deep learning-based SLAM, highlighting their advantages, disadvantages, and applicability. Additionally, two key research topics in 2D Lidar SLAM are presented: solutions for dynamic objects during mapping and the fusion of 2D Lidar and vision data. Finally, the development trends of 2D SLAM are discussed.https://www.mdpi.com/2072-4292/17/7/1214lidar SLAMgraph optimizationdeep learningdynamic objectschanging environment |
| spellingShingle | Yingying Ran Xiaobin Xu Zhiying Tan Minzhou Luo A Review of 2D Lidar SLAM Research Remote Sensing lidar SLAM graph optimization deep learning dynamic objects changing environment |
| title | A Review of 2D Lidar SLAM Research |
| title_full | A Review of 2D Lidar SLAM Research |
| title_fullStr | A Review of 2D Lidar SLAM Research |
| title_full_unstemmed | A Review of 2D Lidar SLAM Research |
| title_short | A Review of 2D Lidar SLAM Research |
| title_sort | review of 2d lidar slam research |
| topic | lidar SLAM graph optimization deep learning dynamic objects changing environment |
| url | https://www.mdpi.com/2072-4292/17/7/1214 |
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