XRDSLAM: A Flexible and Modular Framework for Deep Learning based SLAM
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909373404545024 |
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| author | Wang, Xiaomeng Wang, Nan Zhang, Guofeng |
| author_facet | Wang, Xiaomeng Wang, Nan Zhang, Guofeng |
| contents | In this paper, we propose a flexible SLAM framework, XRDSLAM. It adopts a modular code design and a multi-process running mechanism, providing highly reusable foundational modules such as unified dataset management, 3d visualization, algorithm configuration, and metrics evaluation. It can help developers quickly build a complete SLAM system, flexibly combine different algorithm modules, and conduct standardized benchmarking for accuracy and efficiency comparison. Within this framework, we integrate several state-of-the-art SLAM algorithms with different types, including NeRF and 3DGS based SLAM, and even odometry or reconstruction algorithms, which demonstrates the flexibility and extensibility. We also conduct a comprehensive comparison and evaluation of these integrated algorithms, analyzing the characteristics of each. Finally, we contribute all the code, configuration and data to the open-source community, which aims to promote the widespread research and development of SLAM technology within the open-source ecosystem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_23690 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | XRDSLAM: A Flexible and Modular Framework for Deep Learning based SLAM Wang, Xiaomeng Wang, Nan Zhang, Guofeng Computer Vision and Pattern Recognition Robotics In this paper, we propose a flexible SLAM framework, XRDSLAM. It adopts a modular code design and a multi-process running mechanism, providing highly reusable foundational modules such as unified dataset management, 3d visualization, algorithm configuration, and metrics evaluation. It can help developers quickly build a complete SLAM system, flexibly combine different algorithm modules, and conduct standardized benchmarking for accuracy and efficiency comparison. Within this framework, we integrate several state-of-the-art SLAM algorithms with different types, including NeRF and 3DGS based SLAM, and even odometry or reconstruction algorithms, which demonstrates the flexibility and extensibility. We also conduct a comprehensive comparison and evaluation of these integrated algorithms, analyzing the characteristics of each. Finally, we contribute all the code, configuration and data to the open-source community, which aims to promote the widespread research and development of SLAM technology within the open-source ecosystem. |
| title | XRDSLAM: A Flexible and Modular Framework for Deep Learning based SLAM |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2410.23690 |