69 lines
3.9 KiB
Markdown
69 lines
3.9 KiB
Markdown
[](https://gitlab.com/VladyslavUsenko/basalt/commits/master)
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## Basalt
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For more information see https://vision.in.tum.de/research/vslam/basalt
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This project contains tools for:
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* Camera, IMU and motion capture calibration.
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* Visual-inertial odometry and mapping.
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* Simulated environment to test different components of the system.
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Some reusable components of the system are available as a separate [header-only library](https://gitlab.com/VladyslavUsenko/basalt-headers) ([Documentation](https://vladyslavusenko.gitlab.io/basalt-headers/)).
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There is also a [Github mirror](https://github.com/VladyslavUsenko/basalt-mirror) of this project to enable easy forking.
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## Related Publications
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Visual-Inertial Odometry and Mapping:
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* **Visual-Inertial Mapping with Non-Linear Factor Recovery**, V. Usenko, N. Demmel, D. Schubert, J. Stückler, D. Cremers, In IEEE Robotics and Automation Letters (RA-L) [[DOI:10.1109/LRA.2019.2961227]](https://doi.org/10.1109/LRA.2019.2961227) [[arXiv:1904.06504]](https://arxiv.org/abs/1904.06504).
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Calibration (explains implemented camera models):
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* **The Double Sphere Camera Model**, V. Usenko and N. Demmel and D. Cremers, In 2018 International Conference on 3D Vision (3DV), [[DOI:10.1109/3DV.2018.00069]](https://doi.org/10.1109/3DV.2018.00069), [[arXiv:1807.08957]](https://arxiv.org/abs/1807.08957).
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Calibration (demonstrates how these tools can be used for dataset calibration):
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* **The TUM VI Benchmark for Evaluating Visual-Inertial Odometry**, D. Schubert, T. Goll, N. Demmel, V. Usenko, J. Stückler, D. Cremers, In 2018 International Conference on Intelligent Robots and Systems (IROS), [[DOI:10.1109/IROS.2018.8593419]](https://doi.org/10.1109/IROS.2018.8593419), [[arXiv:1804.06120]](https://arxiv.org/abs/1804.06120).
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Calibration (describes B-spline trajectory representation used in camera-IMU calibration):
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* **Efficient Derivative Computation for Cumulative B-Splines on Lie Groups**, C. Sommer, V. Usenko, D. Schubert, N. Demmel, D. Cremers, In 2020 Conference on Computer Vision and Pattern Recognition (CVPR), [[DOI:10.1109/CVPR42600.2020.01116]](https://doi.org/10.1109/CVPR42600.2020.01116), [[arXiv:1911.08860]](https://arxiv.org/abs/1911.08860).
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## Installation
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### APT installation for Ubuntu 20.04 and 18.04 (Fast)
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Set up keys, add the repository to the sources list, update the Ubuntu package index and install Basalt:
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```
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sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 0D97B6C9
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sudo sh -c 'echo "deb [arch=amd64] http://packages.usenko.eu/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/basalt.list'
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sudo apt-get update
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sudo apt-get install basalt
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```
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### Source installation for Ubuntu >= 18.04 and MacOS >= 10.14 Mojave
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Clone the source code for the project and build it. For MacOS you should have [Homebrew](https://brew.sh/) installed.
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```
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git clone --recursive https://gitlab.com/VladyslavUsenko/basalt.git
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cd basalt
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./scripts/install_deps.sh
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mkdir build
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cd build
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cmake .. -DCMAKE_BUILD_TYPE=RelWithDebInfo
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make -j8
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```
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NOTE: It is possible to compile the code on Ubuntu 16.04, but you need to install cmake-3.10 or higher and gcc-7. See corresponding [Dockerfile](docker/b_image_xenial/Dockerfile) as an example.
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## Usage
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* [Camera, IMU and Mocap calibration. (TUM-VI, Euroc, UZH-FPV and Kalibr datasets)](doc/Calibration.md)
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* [Visual-inertial odometry and mapping. (TUM-VI and Euroc datasets)](doc/VioMapping.md)
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* [Visual odometry (no IMU). (KITTI dataset)](doc/Vo.md)
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* [Simulation tools to test different components of the system.](doc/Simulation.md)
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## Device support
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* [Tutorial on Camera-IMU and Motion capture calibration with Realsense T265.](doc/Realsense.md)
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## Development
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* [Development environment setup.](doc/DevSetup.md)
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## Licence
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The code is provided under a BSD 3-clause license. See the LICENSE file for details.
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Note also the different licenses of thirdparty submodules.
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