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.

翻译：本文提出一种灵活的SLAM框架XRDSLAM。该框架采用模块化代码设计与多进程运行机制，提供了高度可复用的基础模块，包括统一数据集管理、三维可视化、算法配置与指标评估等。它能够帮助开发者快速构建完整的SLAM系统，灵活组合不同算法模块，并进行标准化的精度与效率对比测试。在本框架中，我们集成了多种类型的先进SLAM算法，包括基于NeRF与3DGS的SLAM方法，甚至里程计或重建算法，充分体现了其灵活性与可扩展性。我们对这些集成算法进行了全面的比较与评估，分析了各自的特点。最后，我们将全部代码、配置与数据开源贡献给社区，旨在推动SLAM技术在开源生态中的广泛研究与发展。