Real-Time Lidar Odometry and Mapping with Loop Closure
Real-time performance and global consistency are extremely important in Simultaneous Localization and Mapping (SLAM) problems. Classic lidar-based SLAM systems often consist of front-end odometry and back-end pose optimization. However, due to expensive computation, it is often difficult to achieve...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/12/4373 |
| _version_ | 1797482482367463424 |
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| author | Yonghui Liu Weimin Zhang Fangxing Li Zhengqing Zuo Qiang Huang |
| author_facet | Yonghui Liu Weimin Zhang Fangxing Li Zhengqing Zuo Qiang Huang |
| author_sort | Yonghui Liu |
| collection | DOAJ |
| description | Real-time performance and global consistency are extremely important in Simultaneous Localization and Mapping (SLAM) problems. Classic lidar-based SLAM systems often consist of front-end odometry and back-end pose optimization. However, due to expensive computation, it is often difficult to achieve loop-closure detection without compromising the real-time performance of the odometry. We propose a SLAM system where scan-to-submap-based local lidar odometry and global pose optimization based on submap construction as well as loop-closure detection are designed as separated from each other. In our work, extracted edge and surface feature points are inserted into two consecutive feature submaps and added to the pose graph prepared for loop-closure detection and global pose optimization. In addition, a submap is added to the pose graph for global data association when it is marked as in a finished state. In particular, a method to filter out false loops is proposed to accelerate the construction of constraints in the pose graph. The proposed method is evaluated on public datasets and achieves competitive performance with pose estimation frequency over 15 Hz in local lidar odometry and low drift in global consistency. |
| first_indexed | 2024-03-09T22:33:57Z |
| format | Article |
| id | doaj.art-8ac25be6f40e4c939315ef764ea9b8d5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T22:33:57Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8ac25be6f40e4c939315ef764ea9b8d52023-11-23T18:52:25ZengMDPI AGSensors1424-82202022-06-012212437310.3390/s22124373Real-Time Lidar Odometry and Mapping with Loop ClosureYonghui Liu0Weimin Zhang1Fangxing Li2Zhengqing Zuo3Qiang Huang4School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaReal-time performance and global consistency are extremely important in Simultaneous Localization and Mapping (SLAM) problems. Classic lidar-based SLAM systems often consist of front-end odometry and back-end pose optimization. However, due to expensive computation, it is often difficult to achieve loop-closure detection without compromising the real-time performance of the odometry. We propose a SLAM system where scan-to-submap-based local lidar odometry and global pose optimization based on submap construction as well as loop-closure detection are designed as separated from each other. In our work, extracted edge and surface feature points are inserted into two consecutive feature submaps and added to the pose graph prepared for loop-closure detection and global pose optimization. In addition, a submap is added to the pose graph for global data association when it is marked as in a finished state. In particular, a method to filter out false loops is proposed to accelerate the construction of constraints in the pose graph. The proposed method is evaluated on public datasets and achieves competitive performance with pose estimation frequency over 15 Hz in local lidar odometry and low drift in global consistency.https://www.mdpi.com/1424-8220/22/12/4373real-time lidar odometrysubmap-based loop-closure detectionpose graph optimizationsimultaneous localization and mapping (SLAM) |
| spellingShingle | Yonghui Liu Weimin Zhang Fangxing Li Zhengqing Zuo Qiang Huang Real-Time Lidar Odometry and Mapping with Loop Closure Sensors real-time lidar odometry submap-based loop-closure detection pose graph optimization simultaneous localization and mapping (SLAM) |
| title | Real-Time Lidar Odometry and Mapping with Loop Closure |
| title_full | Real-Time Lidar Odometry and Mapping with Loop Closure |
| title_fullStr | Real-Time Lidar Odometry and Mapping with Loop Closure |
| title_full_unstemmed | Real-Time Lidar Odometry and Mapping with Loop Closure |
| title_short | Real-Time Lidar Odometry and Mapping with Loop Closure |
| title_sort | real time lidar odometry and mapping with loop closure |
| topic | real-time lidar odometry submap-based loop-closure detection pose graph optimization simultaneous localization and mapping (SLAM) |
| url | https://www.mdpi.com/1424-8220/22/12/4373 |
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