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

2 min read

VSLAM-LAB is a benchmarking framework for visual SLAM: one command-line tool that installs a baseline, downloads and preprocesses a dataset, runs a YAML-defined experiment for a number of repeats, and evaluates and compares the trajectories with consistent metrics. Dependencies are pinned with the pixi package manager so builds are reproducible. Experiments can subsample frames by index range, stride, a maximum count, or visual-place-recognition dissimilarity. The paper also shows how to define custom benchmarks, using difficulty levels as the example.

Findings

  • The arXiv abstract reports no numerical results; this is a tooling paper.
  • The README (August 2026 commit) lists 12 baselines, ten visual SLAM systems (including ORB-SLAM2 and 3, DROID-SLAM, DPVO, MASt3R-SLAM, VGGT-SLAM, MonoGS, OKVIS2, PyCuVSLAM) and two SfM tools (COLMAP, VGGT), and 43 dataset entries.
  • The dataset list spans indoor, synthetic, vehicle, UAV, ground robot, construction-site, planetary-analogue, endoscopy and nine underwater sets.

Limits

  • Seedling note from the abstract and README only.
  • Trajectory accuracy is the evaluation target. Semantic maps and scene graphs are outside its scope, so it does not help evaluate the upper layers of a 3D scene graph.
  • Single-agent: there is no multi-robot or map-merging protocol.

Relevance

The underwater and planetary entries make this one of the few places where the same SLAM baselines can be run, under one protocol, on land, air and sea data. A cross-domain comparison in multi-domain perception needs that before anyone claims a method transfers, and repeated runs per sequence are a cheap way to see run-to-run variance for robust robotics.