XRDSLAM: A Flexible and Modular Framework for Deep Learning based SLAM
This provides a tool for SLAM researchers and developers to streamline development and comparison, though it is incremental as it builds on existing algorithms.
The authors introduced XRDSLAM, a flexible and modular framework for deep learning-based SLAM, enabling developers to quickly build systems and benchmark algorithms, with integration of state-of-the-art methods like NeRF and 3DGS.
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.