Real-Time LiDAR–Inertial Simultaneous Localization and Mesh Reconstruction
In this paper, a novel LiDAR–inertial-based Simultaneous Localization and Mesh Reconstruction (LI-SLAMesh) system is proposed, which can achieve fast and robust pose tracking and online mesh reconstruction in an outdoor environment. The LI-SLAMesh system consists of two components, including LiDAR–i...
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| Language: | English |
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MDPI AG
2024-10-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/15/11/495 |
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| author | Yunqi Cheng Meng Xu Kezhi Wang Zonghai Chen Jikai Wang |
| author_facet | Yunqi Cheng Meng Xu Kezhi Wang Zonghai Chen Jikai Wang |
| author_sort | Yunqi Cheng |
| collection | DOAJ |
| description | In this paper, a novel LiDAR–inertial-based Simultaneous Localization and Mesh Reconstruction (LI-SLAMesh) system is proposed, which can achieve fast and robust pose tracking and online mesh reconstruction in an outdoor environment. The LI-SLAMesh system consists of two components, including LiDAR–inertial odometry and a Truncated Signed Distance Field (TSDF) free online reconstruction module. Firstly, to reduce the odometry drift errors we use scan-to-map matching, and inter-frame inertial information is used to generate prior relative pose estimation for later LiDAR-dominated optimization. Then, based on the motivation that the unevenly distributed residual terms tend to degrade the nonlinear optimizer, a novel residual density-driven Gauss–Newton method is proposed to obtain the optimal pose estimation. Secondly, to achieve fast and accurate 3D reconstruction, compared with TSDF-based mapping mechanism, a more compact map representation is proposed, which only maintains the occupied voxels and computes the vertices’ SDF values of each occupied voxels using an iterative Implicit Moving Least Squares (IMLS) algorithm. Then, marching cube is performed on the voxels and a dense mesh map is generated online. Extensive experiments are conducted on public datasets. The experimental results demonstrate that our method can achieve significant localization and online reconstruction performance improvements. The source code will be made public for the benefit of the robotic community. |
| format | Article |
| id | doaj-art-2bb9c59dcd934e64bf92f1bfa6ac4b3d |
| institution | OA Journals |
| issn | 2032-6653 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-2bb9c59dcd934e64bf92f1bfa6ac4b3d2025-08-20T01:53:56ZengMDPI AGWorld Electric Vehicle Journal2032-66532024-10-01151149510.3390/wevj15110495Real-Time LiDAR–Inertial Simultaneous Localization and Mesh ReconstructionYunqi Cheng0Meng Xu1Kezhi Wang2Zonghai Chen3Jikai Wang4Department of Automation, University of Science and Technology of China, Hefei 230027, ChinaDepartment of Automation, University of Science and Technology of China, Hefei 230027, ChinaDepartment of Automation, University of Science and Technology of China, Hefei 230027, ChinaDepartment of Automation, University of Science and Technology of China, Hefei 230027, ChinaDepartment of Automation, University of Science and Technology of China, Hefei 230027, ChinaIn this paper, a novel LiDAR–inertial-based Simultaneous Localization and Mesh Reconstruction (LI-SLAMesh) system is proposed, which can achieve fast and robust pose tracking and online mesh reconstruction in an outdoor environment. The LI-SLAMesh system consists of two components, including LiDAR–inertial odometry and a Truncated Signed Distance Field (TSDF) free online reconstruction module. Firstly, to reduce the odometry drift errors we use scan-to-map matching, and inter-frame inertial information is used to generate prior relative pose estimation for later LiDAR-dominated optimization. Then, based on the motivation that the unevenly distributed residual terms tend to degrade the nonlinear optimizer, a novel residual density-driven Gauss–Newton method is proposed to obtain the optimal pose estimation. Secondly, to achieve fast and accurate 3D reconstruction, compared with TSDF-based mapping mechanism, a more compact map representation is proposed, which only maintains the occupied voxels and computes the vertices’ SDF values of each occupied voxels using an iterative Implicit Moving Least Squares (IMLS) algorithm. Then, marching cube is performed on the voxels and a dense mesh map is generated online. Extensive experiments are conducted on public datasets. The experimental results demonstrate that our method can achieve significant localization and online reconstruction performance improvements. The source code will be made public for the benefit of the robotic community.https://www.mdpi.com/2032-6653/15/11/4953D LiDARdense mappingTSDFGaussian ProcessIMLS |
| spellingShingle | Yunqi Cheng Meng Xu Kezhi Wang Zonghai Chen Jikai Wang Real-Time LiDAR–Inertial Simultaneous Localization and Mesh Reconstruction World Electric Vehicle Journal 3D LiDAR dense mapping TSDF Gaussian Process IMLS |
| title | Real-Time LiDAR–Inertial Simultaneous Localization and Mesh Reconstruction |
| title_full | Real-Time LiDAR–Inertial Simultaneous Localization and Mesh Reconstruction |
| title_fullStr | Real-Time LiDAR–Inertial Simultaneous Localization and Mesh Reconstruction |
| title_full_unstemmed | Real-Time LiDAR–Inertial Simultaneous Localization and Mesh Reconstruction |
| title_short | Real-Time LiDAR–Inertial Simultaneous Localization and Mesh Reconstruction |
| title_sort | real time lidar inertial simultaneous localization and mesh reconstruction |
| topic | 3D LiDAR dense mapping TSDF Gaussian Process IMLS |
| url | https://www.mdpi.com/2032-6653/15/11/495 |
| work_keys_str_mv | AT yunqicheng realtimelidarinertialsimultaneouslocalizationandmeshreconstruction AT mengxu realtimelidarinertialsimultaneouslocalizationandmeshreconstruction AT kezhiwang realtimelidarinertialsimultaneouslocalizationandmeshreconstruction AT zonghaichen realtimelidarinertialsimultaneouslocalizationandmeshreconstruction AT jikaiwang realtimelidarinertialsimultaneouslocalizationandmeshreconstruction |