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Kimera

Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.

Kimera comprises four modules:

  • A fast and accurate Visual Inertial Odometry (VIO) pipeline (Kimera-VIO)
  • A full SLAM implementation based on Robust Pose Graph Optimization (Kimera-RPGO)
  • A per-frame and multi-frame 3D mesh generator (Kimera-Mesher)
  • And a generator of semantically annotated 3D meshes (Kimera-Semantics)

Kimera

Click on the following links to install Kimera's modules and get started! It is very easy to install!

Kimera-VIO & Kimera-Mesher

Kimera-RPGO

Kimera-Semantics

Chart

overall_chart

Citation

If you found any of the above modules useful, we would really appreciate if you could cite our work:

@InProceedings{Rosinol19icra-incremental,
  title = {Incremental visual-inertial 3d mesh generation with structural regularities},
  author = {Rosinol, Antoni and Sattler, Torsten and Pollefeys, Marc and Carlone, Luca},
  year = {2019},
  booktitle = {2019 International Conference on Robotics and Automation (ICRA)},
  pdf = {https://arxiv.org/pdf/1903.01067.pdf}
}
@InProceedings{Rosinol20icra-Kimera,
  title = {Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping},
  author = {Rosinol, Antoni and Abate, Marcus and Chang, Yun and Carlone, Luca},
  year = {2020},
  booktitle = {IEEE Intl. Conf. on Robotics and Automation (ICRA)},
  url = {https://github.com/MIT-SPARK/Kimera},
  pdf = {https://arxiv.org/pdf/1910.02490.pdf}
}
@InProceedings{Rosinol20rss-dynamicSceneGraphs,
  title = {{3D} Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans},
  author = {A. Rosinol and A. Gupta and M. Abate and J. Shi and L. Carlone},
  year = {2020},
  booktitle = {Robotics: Science and Systems (RSS)},
  pdf = {https://arxiv.org/pdf/2002.06289.pdf}
}
@InProceedings{Rosinol21arxiv-Kimera,
  title = {{K}imera: from {SLAM} to Spatial Perception with {3D} Dynamic Scene Graphs},
  author = {A. Rosinol, A. Violette, M. Abate, N. Hughes, Y. Chang, J. Shi, A. Gupta, L. Carlone},
  year = {2021},
  booktitle = {arxiv},
  pdf = {https://arxiv.org/pdf/2101.06894.pdf}
}

Open-Source Datasets

In addition to the real-life tests on the Euroc dataset, we use a photo-realistic Unity-based simulator to test Kimera. The simulator provides:

  • RGB Stereo camera
  • Depth camera
  • Ground-truth 2D Semantic Segmentation
  • IMU data
  • Ground-Truth Odometry
  • 2D Lidar
  • TF (ground-truth odometry of robots, and agents)
  • Static TF (ground-truth poses of static objects)

Using this simulator, we created several large visual-inertial datasets which feature scenes with and without dynamic agents (humans), as well as a large variety of environments (indoors and outdoors, small and large). These are ideal to test your Metric-Semantic SLAM and/or other Spatial-AI systems!

Acknowledgments

Kimera is partially funded by ARL DCIST, ONR RAIDER, MIT Lincoln Laboratory, and “la Caixa” Foundation (ID 100010434), LCF/BQ/AA18/11680088 (A. Rosinol).

License

BSD License