Laser SLAM Matching Localization Method for Subway Tunnel Point Clouds
When facing geometrically similar environments such as subway tunnels, Scan-Map registration is highly dependent on the correct initial value of the pose, otherwise mismatching is prone to occur, which limits the application of SLAM (Simultaneous Localization and Mapping) in tunnels. We propose a no...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/12/3681 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849425640854388736 |
|---|---|
| author | Yi Zhang Feiyang Dong Qihao Sun Weiwei Song |
| author_facet | Yi Zhang Feiyang Dong Qihao Sun Weiwei Song |
| author_sort | Yi Zhang |
| collection | DOAJ |
| description | When facing geometrically similar environments such as subway tunnels, Scan-Map registration is highly dependent on the correct initial value of the pose, otherwise mismatching is prone to occur, which limits the application of SLAM (Simultaneous Localization and Mapping) in tunnels. We propose a novel coarse-to-fine registration strategy that includes geometric feature extraction and a keyframe-based pose optimization model. The method involves initial feature point set acquisition through point distance calculations, followed by the extraction of line and plane features, and convex hull features based on the normal vector’s change rate. Coarse registration is achieved through rotation and translation using three types of feature sets, with the resulting pose serving as the initial value for fine registration via Point-Plane ICP. The algorithm’s accuracy and efficiency are validated using Innovusion lidar scans of a subway tunnel, achieving a single-frame point cloud registration accuracy of 3 cm within 0.7 s, significantly improving upon traditional registration algorithms. The study concludes that the proposed method effectively enhances SLAM’s applicability in challenging tunnel environments, ensuring high registration accuracy and efficiency. |
| format | Article |
| id | doaj-art-e8d8b9f9d61347a1b613f4d842584cb8 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e8d8b9f9d61347a1b613f4d842584cb82025-08-20T03:29:43ZengMDPI AGSensors1424-82202025-06-012512368110.3390/s25123681Laser SLAM Matching Localization Method for Subway Tunnel Point CloudsYi Zhang0Feiyang Dong1Qihao Sun2Weiwei Song3School of Geodesy and Geomatics, Wuhan University, Wuhan 430072, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430072, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430072, ChinaGNSS Research Center, Key Laboratory of Luojia of Hubei Province, Wuhan University, Wuhan 430072, ChinaWhen facing geometrically similar environments such as subway tunnels, Scan-Map registration is highly dependent on the correct initial value of the pose, otherwise mismatching is prone to occur, which limits the application of SLAM (Simultaneous Localization and Mapping) in tunnels. We propose a novel coarse-to-fine registration strategy that includes geometric feature extraction and a keyframe-based pose optimization model. The method involves initial feature point set acquisition through point distance calculations, followed by the extraction of line and plane features, and convex hull features based on the normal vector’s change rate. Coarse registration is achieved through rotation and translation using three types of feature sets, with the resulting pose serving as the initial value for fine registration via Point-Plane ICP. The algorithm’s accuracy and efficiency are validated using Innovusion lidar scans of a subway tunnel, achieving a single-frame point cloud registration accuracy of 3 cm within 0.7 s, significantly improving upon traditional registration algorithms. The study concludes that the proposed method effectively enhances SLAM’s applicability in challenging tunnel environments, ensuring high registration accuracy and efficiency.https://www.mdpi.com/1424-8220/25/12/3681SLAMsubway tunnelsfeature extractionregistration |
| spellingShingle | Yi Zhang Feiyang Dong Qihao Sun Weiwei Song Laser SLAM Matching Localization Method for Subway Tunnel Point Clouds Sensors SLAM subway tunnels feature extraction registration |
| title | Laser SLAM Matching Localization Method for Subway Tunnel Point Clouds |
| title_full | Laser SLAM Matching Localization Method for Subway Tunnel Point Clouds |
| title_fullStr | Laser SLAM Matching Localization Method for Subway Tunnel Point Clouds |
| title_full_unstemmed | Laser SLAM Matching Localization Method for Subway Tunnel Point Clouds |
| title_short | Laser SLAM Matching Localization Method for Subway Tunnel Point Clouds |
| title_sort | laser slam matching localization method for subway tunnel point clouds |
| topic | SLAM subway tunnels feature extraction registration |
| url | https://www.mdpi.com/1424-8220/25/12/3681 |
| work_keys_str_mv | AT yizhang laserslammatchinglocalizationmethodforsubwaytunnelpointclouds AT feiyangdong laserslammatchinglocalizationmethodforsubwaytunnelpointclouds AT qihaosun laserslammatchinglocalizationmethodforsubwaytunnelpointclouds AT weiweisong laserslammatchinglocalizationmethodforsubwaytunnelpointclouds |