VSLAM-LAB: A Comprehensive Framework for Visual SLAM Methods and Datasets

Fontan, Alejandro ; Fischer, Tobias ; Civera, Javier (Universidad de Zaragoza) ; Milford, Michael

Resumen: 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 of these
features are 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.

Idioma: Inglés

DOI: 10.1109/IROS60139.2025.11247218

Año: 2025

Publicado en: Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems (2025), [8 pp.]
ISSN: 2153-0858

Tipo y forma: Article (Published version)

Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)