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XRDSLAM: A Flexible and Modular Framework for Deep Learning based SLAM (2410.23690v1)

Published 31 Oct 2024 in cs.CV and cs.RO

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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Summary

  • The paper introduces a modular, flexible deep learning SLAM framework that decouples tracking, mapping, and visualization.
  • It provides a unified pipeline for standardized evaluation by integrating state-of-art algorithms like NeRF-SLAM and 3DGS-based approaches.
  • The framework's open-source release accelerates research by promoting reproducibility and collaborative development.

Overview of XRDSLAM: A Modular Framework for Deep Learning-Based SLAM

The paper introduces XRDSLAM, a versatile and modular framework tailored for deep learning-based Simultaneous Localization and Mapping (SLAM) technologies. This framework is meticulously designed to enhance the development and integration of cutting-edge SLAM algorithms, offering a unified pipeline that promotes configurability and efficiency within the SLAM community.

Key Contributions

The authors highlight multiple contributions through XRDSLAM's design:

  1. Modular Architecture: XRDSLAM uses a multi-process mechanism and modular code architecture, enabling the decoupling of processes like tracking, mapping, and visualization. It enriches the user experience by allowing for the flexible interchange and combination of algorithmic components.
  2. Unified Pipeline: By offering a consistent SLAM development process, XRDSLAM simplifies the creation of SLAM algorithms. It achieves this through reusable components, which facilitate fair and standardized evaluations of different SLAM approaches.
  3. Integration of State-of-the-Art Algorithms: The framework incorporates contemporary SLAM algorithms such as NeRF-based SLAM, 3DGS-based SLAM, and other relevant odometry or reconstruction algorithms. This integration demonstrates XRDSLAM's flexibility and extensibility.
  4. Open-Source Contribution: With all code, configurations, and data available to the public, XRDSLAM supports open-source developments, potentially accelerating research and application of SLAM technologies.

Evaluation and Results

The evaluation showcases XRDSLAM's capability by assessing state-of-the-art SLAM algorithms across various metrics: trajectory accuracy, rendering quality, and reconstruction fidelity. The framework promotes a systematic approach to evaluating these metrics, providing insights into the algorithmic trade-offs between computational efficiency and accuracy. For example, Co-SLAM displayed commendable computational efficiency using hash grids, while Point-SLAM excelled in rendering quality metrics.

Implications and Future Directions

XRDSLAM serves as a development and integration facilitator for the SLAM community, minimizing redundant development efforts and promoting standardized benchmarks. The implications of this framework are vast, providing both practical and theoretical support for the dynamic SLAM field. By fostering an open-source environment, XRDSLAM has the potential to unify contributions from disparate research efforts under a single platform.

Looking forward, XRDSLAM is poised to continue evolving with the incorporation of more sophisticated SLAM algorithms, thereby enhancing the depth and breadth of SLAM research and innovation. This ongoing effort is expected to engage more researchers and practitioners in contributing to the ecosystem, further enriching the capabilities and applications of SLAM systems.

In conclusion, XRDSLAM emerges as a significant tool for the advancement of deep learning-based SLAM systems, offering a platform that modernizes algorithm development, integration, and benchmarking. Through its modular, open-source nature, it promises to be a pivotal resource for the research community striving towards enhancing SLAM technologies.

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