统一视觉SLAM开发与评估流程，一键完成配置、数据下载与测试。
VSLAM-LAB: A Comprehensive Framework for Visual SLAM Methods and Datasets
- 提供统一命令行接口，自动化编译、配置和实验流程。
- 支持多种SLAM算法与数据集，实现标准化评估与结果对比。
- 降低开发门槛，适合快速搭建基准测试，加速研究迭代。
视觉同步定位与地图构建（VSLAM）研究面临工具链分散、系统配置复杂及评估方法不一致等挑战。为此，我们提出VSLAM-LAB，一个统一框架，旨在简化VSLAM系统的开发、评估与部署。该框架通过单一命令行接口，实现VSLAM算法的无缝编译与配置、数据集的自动下载与预处理，以及标准化的实验设计、执行与评估。支持多种VSLAM系统与数据集，具备广泛兼容性与可扩展性，通过一致的评估指标与分析工具促进可复现性。框架显著降低实现复杂度与配置开销，使研究者能聚焦于方法创新，加速迈向可扩展的真实世界应用。我们展示了用户可便捷创建基准测试：此处引入基于难度级别的分类，亦可设想环境或条件特定的分类方式。
原文摘要 · Abstract (English)
Visual Simultaneous Localization and Mapping (VSLAM) research faces significant challenges due to fragmented toolchains, complex system configurations, and inconsistent evaluation methodologies. To address these issues, we present VSLAM-LAB, a unified framework designed to streamline the development, evaluation, and deployment of VSLAM systems. VSLAM-LAB simplifies the entire workflow by enabling seamless compilation and configuration of VSLAM algorithms, automated dataset downloading and preprocessing, and standardized experiment design, execution, and evaluation--all accessible through a single command-line interface. The framework supports a wide range of VSLAM systems and datasets, offering broad compatibility and extendability while promoting reproducibility through consistent evaluation metrics and analysis tools. By reducing implementation complexity and minimizing configuration overhead, VSLAM-LAB empowers researchers to focus on advancing VSLAM methodologies and accelerates progress toward scalable, real-world solutions. We demonstrate the ease with which user-relevant benchmarks can be created: here, we introduce difficulty-level-based categories, but one could envision environment-specific or condition-specific categories.
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