A Comprehensive Framework for Visual SLAM Baselines and Datasets

Alejandro Fontan · Tobias Fischer · Nicolas Marticorena

Somayeh Hussaini · Ted Vanderfeen · Beverley Gorry · Javier Civera · Michael Milford

Introduction

VSLAM-LAB is designed to simplify the development, evaluation, and application of Visual SLAM (VSLAM) systems. This framework enables users to compile and configure VSLAM systems, download and process datasets, and design, run, and evaluate experiments — all from a single command line!

Why Use VSLAM-LAB?

• Unified Framework: Streamlines the management of VSLAM systems and datasets.
• Ease of Use: Run experiments with minimal configuration and single command executions.
• Broad Compatibility: Supports a wide range of VSLAM systems and datasets.
• Reproducible Results: Standardized methods for evaluating and analyzing results.

Getting Started

To ensure all dependencies are installed in a reproducible manner, we use the package management tool pixi. If you haven't installed pixi yet, please run the following command in your terminal:

curl -fsSL https://pixi.sh/install.sh | bash

After installation, restart your terminal or source your shell for the changes to take effect. For more details, refer to the pixi documentation.

If you already have pixi remember to update: pixi self-update

Clone the repository and navigate to the project directory:

git clone https://github.com/VSLAM-LAB/VSLAM-LAB.git && cd VSLAM-LAB

Quick Demo

You can now execute any baseline on any sequence from any dataset within VSLAM-LAB using the following command:

pixi run demo <baseline> <dataset> <sequence> <mode>

For a full list of available systems and datasets, see the VSLAM-LAB Supported Baselines and Datasets. Example commands:

pixi run demo mast3rslam eth table_3 mono
pixi run demo droidslam rgbdtum rgbd_dataset_freiburg1_xyz rgbd
pixi run demo orbslam2 kitti 04 stereo
pixi run demo pycuvslam euroc MH_01_easy stereo-vi

To change the paths where VSLAM-LAB-Benchmark or/and VSLAM-LAB-Evaluation data are stored (for example, to /media/${USER}/data), use the following commands:

pixi run set-benchmark-path /media/${USER}/data
pixi run set-evaluation-path /media/${USER}/data

Configure your own experiments

With VSLAM-LAB, you can easily design and configure experiments using a YAML file and run them with a single command. To run the experiment demo, execute the following command:

pixi run vslamlab configs/exp_vslamlab.yaml (--overwrite)

Experiments in VSLAM-LAB are sequences of entries in a YAML file (see example ~/VSLAM-LAB/configs/exp_vslamlab.yaml):

exp_vslamlab:
  Config: config_vslamlab.yaml  # YAML file containing the sequences to be run
  NumRuns: 1                    # Maximum number of executions per sequence
  Parameters: {verbose: 1}      # Vector with parameters that will be input to the baseline executable
  Module: droidslam             # droidslam/monogs/orbslam2/mast3rslam/dpvo/...

Config files are YAML files containing the list of sequences to be executed in the experiment (see example ~/VSLAM-LAB/configs/config_vslamlab.yaml):

rgbdtum:
  - 'rgbd_dataset_freiburg1_xyz'
hamlyn:
  - 'rectified01'
7scenes:
  - 'chess_seq-01'
eth:
  - 'table_3'
euroc:
  - 'MH_01_easy'
monotum:
  - 'sequence_01'

For a full list of available VSLAM systems and datasets, refer to the section VSLAM-LAB Supported Baselines and Datasets.

VSLAM-LAB Pipeline Commands

In addition to running the full automated pipeline, VSLAM-LAB provides modular commands to interact directly with datasets and baselines. For a comprehensive list of all available commands consult Wiki: Comand‐line Interface

pixi run install-baseline <baseline>                     # Example: pixi run install-baseline droidslam
pixi run download-sequence <dataset> <sequence>          # Example: pixi run download-sequence eth table_3
pixi run run-exp <exp_yaml>                              # Example: pixi run run-exp configs/exp_vslamlab.yaml
pixi run evaluate-exp <exp_yaml>                         # Example: pixi run evaluate-exp configs/exp_vslamlab.yaml
pixi run compare-exp <exp_yaml>                          # Example: pixi run compare-exp configs/exp_vslamlab.yaml

Add a new VSLAM Dataset

Expand the evaluation suite by integrating custom datasets. Follow the instructions in Wiki: Integrate a new VSLAM Dataset.

Add a new VSLAM Baseline

Incorporate new algorithms into the framework. Follow the guide in Wiki: Integrate a new VSLAM Baseline. Benchmark your method against state-of-the-art baselines across all supported datasets.

For a reference implementation, see the VGGT-SLAM integration in commit 259f7ae.

License

VSLAM-LAB is released under a LICENSE.txt. For a list of code dependencies which are not property of the authors of VSLAM-LAB, please check docs/Dependencies.md.

Citation

If you're using VSLAM-LAB in your research, please cite:

@INPROCEEDINGS{fontan2025vslam,
  author={Fontan, Alejandro and Fischer, Tobias and Civera, Javier and Milford, Michael},
  booktitle={2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  title={VSLAM-LAB: A Comprehensive Framework for Visual SLAM Methods and Datasets},
  year={2025},
  volume={},
  number={},
  pages={9013-9020},
  keywords={Visualization;Simultaneous localization and mapping;Robot vision systems;Standardization;Benchmark testing;Programming;Reproducibility of results;Intelligent robots;Faces;Software development management},
  doi={10.1109/IROS60139.2025.11247218}
}

VSLAM-LAB Supported Baselines and Datasets

Baselines	System	Modes	License	Label	Conda Pkg	Camera Models
VGGT-SLAM	VSLAM	mono	BSD-2	vggtslam	✅	pinhole
MASt3R-SLAM	VSLAM	mono	CC BY-NC-SA 4.0	mast3rslam	✅	radtan5 unknown
DPVO	VSLAM	mono	License	dpvo	✅	radtan5
DROID-SLAM	VSLAM	mono rgbd stereo	BSD-3	droidslam	✅	radtan5
ORB-SLAM2	VSLAM	mono rgbd stereo	GPLv3	orbslam2	✅	radtan5
MonoGS	VSLAM	mono rgbd	License	monogs	✅	radtan5
AnyFeature-VSLAM	VSLAM	mono	GPLv3	anyfeature	✅	radtan5
----------	-------	-------	----------	--------	---	----------
PyCuVSLAM	VSLAM	mono rgbd stereo(-vi)	NVIDIA	pycuvslam	➖	radtan5 equid4
ORB-SLAM3	VSLAM	mono(-vi) rgbd(-vi) stereo(-vi)	GPLv3	orbslam3	✅	radtan5 equid4
OKVIS2	VSLAM	mono-vi	BSD-3	okvis2	✅	radtan5 equid4
----------	-------	-------	----------	--------	---	----------
COLMAP	SfM	mono	BSD	colmap	✅	radtan5 equid4 unknown
VGGT	SfM	mono	VGGT	vggt	➖	pinhole

Datasets	Features	Label	Modes	Camera Models
ETH3D SLAM Benchmarks	📸🏠🤳	eth	mono rgbd	pinhole
RGB-D SLAM Dataset and Benchmark	📸🏠🤳	rgbdtum	mono rgbd	radtan5
The KITTI Vision Benchmark Suite	📸🏞️🚗	kitti	mono stereo	pinhole
The EuRoC MAV Dataset	📸🏞️🚁	euroc	mono(-vi) stereo(-vi)	radtan4
The Replica Dataset - iMAP	💻🏠🤳	replica	mono rgbd	pinhole
TartanAir: A Dataset to Push the Limits of Visual SLAM	💻🏞️🤳	tartanair	mono	pinhole
ICL-NUIM RGB-D Benchmark Dataset	💻🏠🤳	nuim	mono rgbd	pinhole
RGB-D Dataset 7-Scenes	📸🏠🤳	7scenes	mono rgbd	pinhole
OpenLORIS-Scene Dataset	📸🏠🤳	openloris-d400/t265	mono(-vi) rgbd(-vi) stereo(-vi)	pinhole equid4
Monado SLAM Dataset - Valve Index	📸🏠🥽	msd	mono(-vi) stereo(-vi)	equid4
ROVER: A Multiseason Dataset for Visual SLAM	📸🏞️🚗	rover-picam/d435i/t265	mono(-vi) rgbd stereo(-vi)	radtan5 equid4
The UT Campus Object Dataset	📸🏞️🤖	ut-coda	mono stereo	radtan5
Sesoko campaign	📸🏞️🌊	sesoko	mono	pinhole
The MADMAX data set for visual-inertial rover navigation on Mars	📸🏞️🤳	madmax	mono(-vi) stereo(-vi)	pinhole
Soneva Corals	📸🏞️🌊🤳	soneva	mono	pinhole
Sweet Corals	📸🏞️🌊🤳	sweetcorals	mono	pinhole unknown
Eiffel Tower: A Deep-Sea Underwater Dataset for Long-Term Visual Localization	📸🏞️🌊🤖	eiffel-tower	mono	radtan4

Tools	Features	Label	Modes	Camera Models
Stray Scanner App	📸🤳	strayscanner	mono rgbd	pinhole

Real / Synthetic : 📸 / 💻

Indoor / Outdoor / Underwater / Intracorporeal : 🏠 / 🏞️ / 🌊 / 🫀

Handheld / Headmounted / Vehicle / UAV / Robot : 🤳 / 🥽 / 🚗 / 🚁 / 🤖

VSLAM-LAB Roadmap

Baselines

• Extend orbslam3 and orbslam3-dev to rgbd-vi
• Extend okvis2 and okvis2-dev to rgbd-vi and stereo-vi

Datasets

• Implement monotum