VSLAM-LAB/VSLAM-LAB

A Comprehensive Framework for Visual SLAM Systems and Datasets

What it solves

VSLAM-LAB simplifies the complex process of developing, testing, and comparing Visual SLAM (Simultaneous Localization and Mapping) systems. It eliminates the manual effort required to compile multiple different SLAM baselines, manage various dataset formats, and standardize the evaluation of results across different algorithms.

How it works

The framework provides a unified command-line interface (CLI) and a configuration-driven pipeline. Users can use a single command to download specific dataset sequences, install baseline algorithms (such as ORB-SLAM2 or DROID-SLAM), and run experiments. Experiments are defined in YAML files, allowing users to specify which sequences to run, how many times to repeat them, and parameters for frame selection (such as slicing, stepping, or using Visual Place Recognition (VPR) to downsample frames based on dissimilarity).

Who it’s for

It is designed for researchers and developers working on robot vision, autonomous navigation, and spatial AI who need a reproducible way to benchmark new VSLAM algorithms against existing state-of-the-art baselines.

Highlights

  • Unified Interface: Run diverse VSLAM baselines and datasets from a single command line.
  • Broad Compatibility: Supports a wide array of systems (e.g., VGGT-SLAM, DPVO, COLMAP) and datasets (indoor, synthetic, vehicle, and UAV).
  • Reproducible Benchmarking: Standardized methods for evaluating and analyzing results.
  • Flexible Frame Selection: Advanced controls to filter RGB frames via indexing, stepping, or VPR-based downsampling.
  • Dependency Management: Uses pixi to ensure reproducible environment installation.

Related

  • Project
    luigifreda/pyslam

    A hybrid Python/C++ modular framework for Visual SLAM that integrates modern AI depth prediction, semantic segmentation, and volumetric reconstruction for research and prototyping.

  • Project
    LAION-AI/CLIP_benchmark

    A standardized evaluation framework for CLIP-like models to measure performance on zero-shot classification, retrieval, captioning, and linear probing across diverse datasets.

  • Project
    open-compass/opencompass

    A comprehensive evaluation platform for large language and vision-language models that provides a unified framework for benchmarking across diverse datasets and paradigms.

  • Project
    open-compass/VLMEvalKit

    An open-source evaluation toolkit for large vision-language models that enables one-command evaluation across 70+ benchmarks and 200+ models.

  • Project
    stella-cv/stella_vslam

    stella_vslam is an open‑source visual SLAM library (monocular, stereo, RGB‑D) that supports many camera models, lets you save/load maps, and offers modular APIs plus ROS integration. It’s a community fork of OpenVSLAM, actively maintained under a BSD‑2 license.