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

Alejandro Fontan, Tobias Fischer, Javier Civera, Michael Milford

TL;DR

VSLAM benchmarking is hampered by fragmented toolchains, inconsistent datasets, and non-reproducible evaluation. The authors present VSLAM-LAB, a unified, CLI-driven framework that automates dependency management, dataset handling, experiment configuration, and standardized trajectory evaluation across multiple VSLAM methods and datasets. It enforces reproducibility via per-method environments, configuration-driven workflows, and common evaluation metrics such as Absolute Trajectory Error (ATE), complemented by visualization aids. Experiments demonstrate cross-method benchmarking across diverse datasets, revealing that traditional methods like ORB-SLAM2 can outperform deep methods on some outdoor sequences, while newer methods like MASt3R-SLAM excel on extremely challenging sequences, highlighting VSLAM-LAB’s ability to integrate novel methods with ease.

Abstract

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.

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

TL;DR

VSLAM benchmarking is hampered by fragmented toolchains, inconsistent datasets, and non-reproducible evaluation. The authors present VSLAM-LAB, a unified, CLI-driven framework that automates dependency management, dataset handling, experiment configuration, and standardized trajectory evaluation across multiple VSLAM methods and datasets. It enforces reproducibility via per-method environments, configuration-driven workflows, and common evaluation metrics such as Absolute Trajectory Error (ATE), complemented by visualization aids. Experiments demonstrate cross-method benchmarking across diverse datasets, revealing that traditional methods like ORB-SLAM2 can outperform deep methods on some outdoor sequences, while newer methods like MASt3R-SLAM excel on extremely challenging sequences, highlighting VSLAM-LAB’s ability to integrate novel methods with ease.

Abstract

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.

Paper Structure

This paper contains 14 sections, 2 figures, 2 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. Visual SLAM Datasets
  4. Visual SLAM Benchmarking Suites
  5. Localization Frameworks
  6. VSLAM-LAB
  7. Dependency management
  8. Visual SLAM Methods
  9. Visual SLAM Datasets
  10. Experiment Customization
  11. Evaluation
  12. Other Tools
  13. EXPERIMENTS
  14. CONCLUSIONS

Figures (2)

  • Figure 1: Comparison of 4 state-of-the-art Visual SLAM methods across 20 sequences from 12 diverse datasets. Our evaluation highlights the strengths and weaknesses of each system, providing insights to guide future VSLAM research. Notably, the VSLAM-LAB framework ensures these experiments can be reproduced seamlessly with negligible time overhead and modified with minimal implementation effort. Smaller area is better.
  • Figure 2: Benchmarking Configurations for VSLAM-LAB. The figure presents four evaluation categories—Easy 2025, Medium 2025, Difficult 2025, and Extreme 2025—grouping sequences based on increasing levels of environmental complexity and motion challenges. Each configuration includes representative sequences (top row), camera trajectories (middle row), and ATE boxplots (bottom row) to facilitate performance comparisons across VSLAM methods. We note that these categories are examples only, and new categories can be easily added as described in Section \ref{['subset:experimentcustomization']}.