XRDSLAM: A Flexible and Modular Framework for Deep Learning based SLAM

• URL: http://arxiv.org/abs/2410.23690v1
• Date: Thu, 31 Oct 2024 07:25:39 GMT
• Title: XRDSLAM: A Flexible and Modular Framework for Deep Learning based SLAM
• Authors: Xiaomeng Wang, Nan Wang, Guofeng Zhang,
• Abstract summary: XRDSLAM is a flexible SLAM framework that adopts a modular code design and a multi-process running mechanism. 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. 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.
• Score: 5.092026311165656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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