Need help with Kimera?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

MIT-SPARK
855 Stars 130 Forks BSD 2-Clause "Simplified" License 45 Commits 0 Opened issues

Description

Index repo for Kimera code

Services available

!
?

Need anything else?

Contributors list

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

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.