MS-Mapping: Multi-session LiDAR Mapping with Wasserstein-based Keyframe Selection
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911957728100352 |
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| author | Hu, Xiangcheng Wu, Jin Jiao, Jianhao Zhang, Wei Tan, Ping |
| author_facet | Hu, Xiangcheng Wu, Jin Jiao, Jianhao Zhang, Wei Tan, Ping |
| contents | Large-scale multi-session LiDAR mapping is crucial for various applications but still faces significant challenges in data redundancy, memory consumption, and efficiency. This paper presents MS-Mapping, a novel multi-session LiDAR mapping system that incorporates an incremental mapping scheme to enable efficient map assembly in large-scale environments. To address the data redundancy and improve graph optimization efficiency caused by the vast amount of point cloud data, we introduce a real-time keyframe selection method based on the Wasserstein distance. Our approach formulates the LiDAR point cloud keyframe selection problem using a similarity method based on Gaussian mixture models (GMM) and addresses the real-time challenge by employing an incremental voxel update method. To facilitate further research and development in the community, we make our code\footnote{https://github.com/JokerJohn/MS-Mapping} and datasets publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02096 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MS-Mapping: Multi-session LiDAR Mapping with Wasserstein-based Keyframe Selection Hu, Xiangcheng Wu, Jin Jiao, Jianhao Zhang, Wei Tan, Ping Robotics Large-scale multi-session LiDAR mapping is crucial for various applications but still faces significant challenges in data redundancy, memory consumption, and efficiency. This paper presents MS-Mapping, a novel multi-session LiDAR mapping system that incorporates an incremental mapping scheme to enable efficient map assembly in large-scale environments. To address the data redundancy and improve graph optimization efficiency caused by the vast amount of point cloud data, we introduce a real-time keyframe selection method based on the Wasserstein distance. Our approach formulates the LiDAR point cloud keyframe selection problem using a similarity method based on Gaussian mixture models (GMM) and addresses the real-time challenge by employing an incremental voxel update method. To facilitate further research and development in the community, we make our code\footnote{https://github.com/JokerJohn/MS-Mapping} and datasets publicly available. |
| title | MS-Mapping: Multi-session LiDAR Mapping with Wasserstein-based Keyframe Selection |
| topic | Robotics |
| url | https://arxiv.org/abs/2406.02096 |