h-k8888 / maplab

An open visual-inertial mapping framework.

Ubuntu 18.04+ROS melodic:

News

• May 2018: maplab was presented at ICRA in Brisbane. Paper / Initial Release
• July 2018: Check out our release candidate with improved localization and lots of new features! Release 1.3
• November 2022: maplab 2.0 initial release with new features and sensors

Description

This repository contains maplab 2.0, an open research-oriented mapping framework, written in C++, for multi-session and multi-robot mapping. For the original maplab release from 2018 the source code and documentation is available here.

For documentation, tutorials and datasets, please visit the wiki.

Features

Robust visual-inertial odometry with localization

Large-scale multisession mapping and optimization

Dense reconstruction

A research platform extensively tested on real robots

Installation and getting started

The following articles help you with getting started with maplab and ROVIOLI:

• Installation on Ubuntu 18.04 or 20.04
• Introduction to the maplab framework
• Structure of the framework
• Running ROVIOLI in VIO mode
• Basic console usage
• Console map management

More detailed information can be found in the wiki pages.

Research Results

The maplab framework has been used as an experimental platform for numerous scientific publications. For a complete list of publications please refer to Research based on maplab.

Citing

Please cite the following paper when using maplab for your research:

@article{schneider2018maplab,
  title={maplab: An Open Framework for Research in Visual-inertial Mapping and Localization},
  author={T. Schneider and M. T. Dymczyk and M. Fehr and K. Egger and S. Lynen and I. Gilitschenski and R. Siegwart},
  journal={IEEE Robotics and Automation Letters},
  year={2018},
  doi={10.1109/LRA.2018.2800113}
}

Additional Citations

Certain components of maplab are directly using the code of the following publications:

• Localization:

  @inproceedings{lynen2015get,
    title={Get Out of My Lab: Large-scale, Real-Time Visual-Inertial Localization.},
    author={Lynen, Simon and Sattler, Torsten and Bosse, Michael and Hesch, Joel A and Pollefeys, Marc and Siegwart, Roland},
    booktitle={Robotics: Science and Systems},
    year={2015}
  }

• ROVIOLI which is composed of ROVIO + maplab for map building and localization:

  @article{bloesch2017iterated,
    title={Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback},
    author={Bloesch, Michael and Burri, Michael and Omari, Sammy and Hutter, Marco and Siegwart, Roland},
    journal={The International Journal of Robotics Research},
    volume={36},
    number={10},
    pages={1053--1072},
    year={2017},
    publisher={SAGE Publications Sage UK: London, England}
  }

Credits

• Thomas Schneider
• Marcin Dymczyk
• Marius Fehr
• Kevin Egger
• Simon Lynen
• Mathias Bürki
• Titus Cieslewski
• Timo Hinzmann
• Mathias Gehrig
• Florian Tschopp
• Andrei Cramariuc
• Lukas Bernreiter

For a complete list of contributors, have a look at CONTRIBUTORS.md