VOX-LIO: An Effective and Robust LiDAR-Inertial Odometry System Based on Surfel Voxels
Accurate and robust pose estimation is critical for simultaneous localization and mapping (SLAM), and multi-sensor fusion has demonstrated efficacy with significant potential for robotic applications. This study presents VOX-LIO, an effective LiDAR-inertial odometry system. To improve both robustnes...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2214 |
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| author | Meijun Guo Yonghui Liu Yuhang Yang Xiaohai He Weimin Zhang |
| author_facet | Meijun Guo Yonghui Liu Yuhang Yang Xiaohai He Weimin Zhang |
| author_sort | Meijun Guo |
| collection | DOAJ |
| description | Accurate and robust pose estimation is critical for simultaneous localization and mapping (SLAM), and multi-sensor fusion has demonstrated efficacy with significant potential for robotic applications. This study presents VOX-LIO, an effective LiDAR-inertial odometry system. To improve both robustness and accuracy, we propose an adaptive hash voxel-based point cloud map management method that incorporates surfel features and planarity. This method enhances the efficiency of point-to-surfel association by leveraging long-term observed surfel. It facilitates the incremental refinement of surfel features within classified surfel voxels, thereby enabling precise and efficient map updates. Furthermore, we develop a weighted fusion approach that integrates LiDAR and IMU data measurements on the manifold, effectively compensating for motion distortion, particularly under high-speed LiDAR motion. We validate our system through experiments conducted on both public datasets and our mobile robot platforms. The results demonstrate that VOX-LIO outperforms the existing methods, effectively handling challenging environments while minimizing computational cost. |
| format | Article |
| id | doaj-art-bcc1713cd67349d6a3cdf6b51e3eeee5 |
| institution | Matheson Library |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-bcc1713cd67349d6a3cdf6b51e3eeee52025-07-11T14:42:25ZengMDPI AGRemote Sensing2072-42922025-06-011713221410.3390/rs17132214VOX-LIO: An Effective and Robust LiDAR-Inertial Odometry System Based on Surfel VoxelsMeijun Guo0Yonghui Liu1Yuhang Yang2Xiaohai He3Weimin Zhang4School 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, ChinaAccurate and robust pose estimation is critical for simultaneous localization and mapping (SLAM), and multi-sensor fusion has demonstrated efficacy with significant potential for robotic applications. This study presents VOX-LIO, an effective LiDAR-inertial odometry system. To improve both robustness and accuracy, we propose an adaptive hash voxel-based point cloud map management method that incorporates surfel features and planarity. This method enhances the efficiency of point-to-surfel association by leveraging long-term observed surfel. It facilitates the incremental refinement of surfel features within classified surfel voxels, thereby enabling precise and efficient map updates. Furthermore, we develop a weighted fusion approach that integrates LiDAR and IMU data measurements on the manifold, effectively compensating for motion distortion, particularly under high-speed LiDAR motion. We validate our system through experiments conducted on both public datasets and our mobile robot platforms. The results demonstrate that VOX-LIO outperforms the existing methods, effectively handling challenging environments while minimizing computational cost.https://www.mdpi.com/2072-4292/17/13/2214LiDAR-IMUmanifold optimizationmobile robotmulti-sensor fusionsimultaneous localization and mapping (SLAM)surfel feature |
| spellingShingle | Meijun Guo Yonghui Liu Yuhang Yang Xiaohai He Weimin Zhang VOX-LIO: An Effective and Robust LiDAR-Inertial Odometry System Based on Surfel Voxels Remote Sensing LiDAR-IMU manifold optimization mobile robot multi-sensor fusion simultaneous localization and mapping (SLAM) surfel feature |
| title | VOX-LIO: An Effective and Robust LiDAR-Inertial Odometry System Based on Surfel Voxels |
| title_full | VOX-LIO: An Effective and Robust LiDAR-Inertial Odometry System Based on Surfel Voxels |
| title_fullStr | VOX-LIO: An Effective and Robust LiDAR-Inertial Odometry System Based on Surfel Voxels |
| title_full_unstemmed | VOX-LIO: An Effective and Robust LiDAR-Inertial Odometry System Based on Surfel Voxels |
| title_short | VOX-LIO: An Effective and Robust LiDAR-Inertial Odometry System Based on Surfel Voxels |
| title_sort | vox lio an effective and robust lidar inertial odometry system based on surfel voxels |
| topic | LiDAR-IMU manifold optimization mobile robot multi-sensor fusion simultaneous localization and mapping (SLAM) surfel feature |
| url | https://www.mdpi.com/2072-4292/17/13/2214 |
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