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.
Abstract (translated)
在这篇论文中，我们提出了一种灵活的SLAM框架——XRDSLAM。该框架采用了模块化代码设计和多进程运行机制，提供了高度可复用的基础模块，如统一的数据集管理、3D可视化、算法配置以及指标评估等。它可以帮助开发者快速构建完整的SLAM系统，灵活组合不同的算法模块，并进行标准化的基准测试以比较准确性和效率。在这一框架内，我们整合了几种不同类型的前沿SLAM算法，包括基于NeRF和3DGS的SLAM，甚至里程计或重建算法，这体现了该框架的灵活性和可扩展性。我们也对这些集成的算法进行了全面的对比与评估，分析了每种算法的特点。最后，我们将所有代码、配置和数据贡献给了开源社区，旨在促进SLAM技术在开源生态系统中的广泛研究与发展。
URL
https://arxiv.org/abs/2410.23690
