Surfel-based lightweight LiDAR-inertial SLAM system for UAV pose estimation
The easily occluded nature of satellite signals poses significant challenges to the pose estimation of small unmanned aerial vehicles. In this paper, a lightweight LiDAR (Light Detection and Ranging) -inertial SLAM (Simultaneous Localization and Mapping) system specifically tailored for UAV pose est...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Advances in Aeronautical Science and Engineering
2025-06-01
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| Series: | Hangkong gongcheng jinzhan |
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| Online Access: | http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2023224 |
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| author | LIU Chang ZHAO Zixu SHANG Yuanfeng QIU Dawei SHI Jinglin LIU Jie JIANG Ji |
| author_facet | LIU Chang ZHAO Zixu SHANG Yuanfeng QIU Dawei SHI Jinglin LIU Jie JIANG Ji |
| author_sort | LIU Chang |
| collection | DOAJ |
| description | The easily occluded nature of satellite signals poses significant challenges to the pose estimation of small unmanned aerial vehicles. In this paper, a lightweight LiDAR (Light Detection and Ranging) -inertial SLAM (Simultaneous Localization and Mapping) system specifically tailored for UAV pose estimation is proposed. The proposed surfel-based LiDAR point cloud registration algorithm achieves point cloud registration and pose estimation by minimizing the distance between points and surfels, while reducing the algorithm′s computational complexity and ensuring lightweight operation by discarding un-stable surface elements. The framework of integrating this algorithm into the error-state Kalman filter based LiDAR-inertial SLAM system is also designed at the same time. The proposed SLAM system is evaluated through experiments conducted on experimental datasets. The results demonstrate superior pose estimation accuracy compared to existing LiDAR-Inertial navigation systems. Furthermore,while maintaining the performance in terms of runtime, the proposed technique reduces the average position deviation by 37.63% and the average attitude deviation by 33.94% in outdoor satellite signal denied environments. |
| format | Article |
| id | doaj-art-6b62bb40552d47e9ae5da5878f191af0 |
| institution | DOAJ |
| issn | 1674-8190 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Editorial Department of Advances in Aeronautical Science and Engineering |
| record_format | Article |
| series | Hangkong gongcheng jinzhan |
| spelling | doaj-art-6b62bb40552d47e9ae5da5878f191af02025-08-20T03:11:51ZzhoEditorial Department of Advances in Aeronautical Science and EngineeringHangkong gongcheng jinzhan1674-81902025-06-0116312413110.16615/j.cnki.1674-8190.2025.03.13hkgcjz-16-3-124Surfel-based lightweight LiDAR-inertial SLAM system for UAV pose estimationLIU Chang0ZHAO Zixu1SHANG Yuanfeng2QIU Dawei3SHI Jinglin4LIU Jie5JIANG Ji6Wireless Communication Technology Research Center, Institute of Computing Technology, CAS, Beijing 100190, ChinaWireless Communication Technology Research Center, Institute of Computing Technology, CAS, Beijing 100190, ChinaWireless Communication Technology Research Center, Institute of Computing Technology, CAS, Beijing 100190, ChinaWireless Communication Technology Research Center, Institute of Computing Technology, CAS, Beijing 100190, ChinaWireless Communication Technology Research Center, Institute of Computing Technology, CAS, Beijing 100190, ChinaCollaborative Innovation Center for Management and Application Technology of UAV, Yunnan Police College, Kunming 650223, ChinaCollaborative Innovation Center for Management and Application Technology of UAV, Yunnan Police College, Kunming 650223, ChinaThe easily occluded nature of satellite signals poses significant challenges to the pose estimation of small unmanned aerial vehicles. In this paper, a lightweight LiDAR (Light Detection and Ranging) -inertial SLAM (Simultaneous Localization and Mapping) system specifically tailored for UAV pose estimation is proposed. The proposed surfel-based LiDAR point cloud registration algorithm achieves point cloud registration and pose estimation by minimizing the distance between points and surfels, while reducing the algorithm′s computational complexity and ensuring lightweight operation by discarding un-stable surface elements. The framework of integrating this algorithm into the error-state Kalman filter based LiDAR-inertial SLAM system is also designed at the same time. The proposed SLAM system is evaluated through experiments conducted on experimental datasets. The results demonstrate superior pose estimation accuracy compared to existing LiDAR-Inertial navigation systems. Furthermore,while maintaining the performance in terms of runtime, the proposed technique reduces the average position deviation by 37.63% and the average attitude deviation by 33.94% in outdoor satellite signal denied environments.http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2023224uav pose estimationlightweight lidar-inertial slammulti-sensor fusionpoint cloud registration |
| spellingShingle | LIU Chang ZHAO Zixu SHANG Yuanfeng QIU Dawei SHI Jinglin LIU Jie JIANG Ji Surfel-based lightweight LiDAR-inertial SLAM system for UAV pose estimation Hangkong gongcheng jinzhan uav pose estimation lightweight lidar-inertial slam multi-sensor fusion point cloud registration |
| title | Surfel-based lightweight LiDAR-inertial SLAM system for UAV pose estimation |
| title_full | Surfel-based lightweight LiDAR-inertial SLAM system for UAV pose estimation |
| title_fullStr | Surfel-based lightweight LiDAR-inertial SLAM system for UAV pose estimation |
| title_full_unstemmed | Surfel-based lightweight LiDAR-inertial SLAM system for UAV pose estimation |
| title_short | Surfel-based lightweight LiDAR-inertial SLAM system for UAV pose estimation |
| title_sort | surfel based lightweight lidar inertial slam system for uav pose estimation |
| topic | uav pose estimation lightweight lidar-inertial slam multi-sensor fusion point cloud registration |
| url | http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2023224 |
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