Loosely Coupled PPP/Inertial/LiDAR Simultaneous Localization and Mapping (SLAM) Based on Graph Optimization
Navigation services and high-precision positioning play a significant role in emerging fields such as self-driving and mobile robots. The performance of precise point positioning (PPP) may be seriously affected by signal interference and struggles to achieve continuous and accurate positioning in co...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/5/812 |
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| author | Baoxiang Zhang Cheng Yang Guorui Xiao Peigong Li Zhengyang Xiao Haopeng Wei Jialin Liu |
| author_facet | Baoxiang Zhang Cheng Yang Guorui Xiao Peigong Li Zhengyang Xiao Haopeng Wei Jialin Liu |
| author_sort | Baoxiang Zhang |
| collection | DOAJ |
| description | Navigation services and high-precision positioning play a significant role in emerging fields such as self-driving and mobile robots. The performance of precise point positioning (PPP) may be seriously affected by signal interference and struggles to achieve continuous and accurate positioning in complex environments. LiDAR/inertial navigation can use spatial structure information to realize pose estimation but cannot solve the problem of cumulative error. This study proposes a PPP/inertial/LiDAR combined localization algorithm based on factor graph optimization. Firstly, the algorithm performed the spatial alignment by adding the initial yaw factor. Then, the PPP factor and anchor factor were constructed using PPP information. Finally, the global localization is estimated accurately and robustly based on the factor graph. The vehicle experiment shows that the proposed algorithm in this study can achieve meter-level accuracy in complex environments and can greatly enhance the accuracy, continuity, and reliability of attitude estimation. |
| format | Article |
| id | doaj-art-04ffaaf9ee894d1ba696015eb504112e |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-04ffaaf9ee894d1ba696015eb504112e2025-08-20T02:52:35ZengMDPI AGRemote Sensing2072-42922025-02-0117581210.3390/rs17050812Loosely Coupled PPP/Inertial/LiDAR Simultaneous Localization and Mapping (SLAM) Based on Graph OptimizationBaoxiang Zhang0Cheng Yang1Guorui Xiao2Peigong Li3Zhengyang Xiao4Haopeng Wei5Jialin Liu6School of Land Science and Technology, China University of Geosciences, Beijing 100083, ChinaSchool of Land Science and Technology, China University of Geosciences, Beijing 100083, ChinaSchool of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, ChinaSchool of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, ChinaSchool of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, ChinaSchool of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, ChinaSchool of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, ChinaNavigation services and high-precision positioning play a significant role in emerging fields such as self-driving and mobile robots. The performance of precise point positioning (PPP) may be seriously affected by signal interference and struggles to achieve continuous and accurate positioning in complex environments. LiDAR/inertial navigation can use spatial structure information to realize pose estimation but cannot solve the problem of cumulative error. This study proposes a PPP/inertial/LiDAR combined localization algorithm based on factor graph optimization. Firstly, the algorithm performed the spatial alignment by adding the initial yaw factor. Then, the PPP factor and anchor factor were constructed using PPP information. Finally, the global localization is estimated accurately and robustly based on the factor graph. The vehicle experiment shows that the proposed algorithm in this study can achieve meter-level accuracy in complex environments and can greatly enhance the accuracy, continuity, and reliability of attitude estimation.https://www.mdpi.com/2072-4292/17/5/812PPPgraph optimizationLiDAR inertial odometrymulti-sensor fusion |
| spellingShingle | Baoxiang Zhang Cheng Yang Guorui Xiao Peigong Li Zhengyang Xiao Haopeng Wei Jialin Liu Loosely Coupled PPP/Inertial/LiDAR Simultaneous Localization and Mapping (SLAM) Based on Graph Optimization Remote Sensing PPP graph optimization LiDAR inertial odometry multi-sensor fusion |
| title | Loosely Coupled PPP/Inertial/LiDAR Simultaneous Localization and Mapping (SLAM) Based on Graph Optimization |
| title_full | Loosely Coupled PPP/Inertial/LiDAR Simultaneous Localization and Mapping (SLAM) Based on Graph Optimization |
| title_fullStr | Loosely Coupled PPP/Inertial/LiDAR Simultaneous Localization and Mapping (SLAM) Based on Graph Optimization |
| title_full_unstemmed | Loosely Coupled PPP/Inertial/LiDAR Simultaneous Localization and Mapping (SLAM) Based on Graph Optimization |
| title_short | Loosely Coupled PPP/Inertial/LiDAR Simultaneous Localization and Mapping (SLAM) Based on Graph Optimization |
| title_sort | loosely coupled ppp inertial lidar simultaneous localization and mapping slam based on graph optimization |
| topic | PPP graph optimization LiDAR inertial odometry multi-sensor fusion |
| url | https://www.mdpi.com/2072-4292/17/5/812 |
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