开源可扩展的深度学习SLAM框架，支持多种算法快速集成与对比
XRDSLAM: A Flexible and Modular Framework for Deep Learning based SLAM
- 模块化设计，支持数据管理、可视化、配置等基础功能复用
- 集成多种主流SLAM算法，涵盖NeRF与3DGS等类型，便于性能对比
- 适合开发者快速搭建系统，推动开源SLAM生态发展
本文提出一种灵活的SLAM框架XRDSLAM，采用模块化代码设计与多进程运行机制，提供统一数据集管理、3D可视化、算法配置与评估指标等高复用性基础模块。该框架可帮助开发者快速构建完整SLAM系统，灵活组合不同算法模块，并实现精度与效率的标准化对比。在框架内，我们集成了多种主流SLAM算法，包括基于NeRF与3DGS的SLAM，以及里程计与重建算法，验证了其灵活性与可扩展性。我们还对集成算法进行了全面对比与评估，分析各类算法特性。最后，所有代码、配置与数据均开源，旨在促进开源生态中SLAM技术的广泛研究与发展。
原文摘要 · Abstract (English)
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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