Fork of https://gitlab.com/VladyslavUsenko/basalt.git
For more information see https://vision.in.tum.de/research/vslam/basalt
This project contains tools for:
- Camera, IMU and motion capture calibration.
- Visual-inertial odometry and mapping.
- Simulated environment to test different components of the system.
Some reusable components of the system are available as a separate header-only library (Fork of https://gitlab.com/VladyslavUsenko/basalt-headers).
Visual-Inertial Odometry and Mapping:
- 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] [arXiv:1904.06504].
Calibration (explains implemented camera models):
- 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], [arXiv:1807.08957].
Calibration (demonstrates how these tools can be used for dataset calibration):
- 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], [arXiv:1804.06120].
Calibration (describes B-spline trajectory representation used in camera-IMU calibration):
- 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], [arXiv:1911.08860].
Optimization (describes square-root optimization and marginalization used in VIO/VO):
- Square Root Marginalization for Sliding-Window Bundle Adjustment, N. Demmel, D. Schubert, C. Sommer, D. Cremers, V. Usenko, In 2021 International Conference on Computer Vision (ICCV), [arXiv:2109.02182]
Clone the project's source code:
git clone --recursive https://github.com/RoblabWh/basalt
cd basalt
Build the Docker image and run a container:
docker build -t basalt .
xhost +local:
docker run -it --rm --env=DISPLAY --env=QT_X11_NO_MITSHM=1 --volume=/tmp/.X11-unix:/tmp/.X11-unix:rw --volume="$HOME":/home/ubuntu basalt
The default build target is the runnable runtime image. To generate a .deb package instead,
build the deb target and export it to a local out/ directory:
docker build --target deb --output type=local,dest=. .
This produces out/basalt_*.deb (dependencies are resolved automatically via dpkg-shlibdeps).
Alternatively, docker build --target package -t basalt-pkg . builds a full image containing the
.deb at /basalt/build/.
Build and install the project. For macOS, you should have Homebrew installed.
./scripts/install_deps.sh
cmake -B build -G Ninja -D CMAKE_BUILD_TYPE=Release
cmake --build build --parallel $(nproc)
sudo cmake --install build
- Camera, IMU and Mocap calibration. (TUM-VI, Euroc, UZH-FPV and Kalibr datasets)
- Visual-inertial odometry and mapping. (TUM-VI and Euroc datasets)
- Visual odometry (no IMU). (KITTI dataset)
- Simulation tools to test different components of the system.
- Batch evaluation tutorial (ICCV'21 experiments)
The code is provided under a BSD 3-clause license. See the LICENSE file for details. Note also the different licenses of thirdparty submodules.
Some improvements are ported back from the fork granite (MIT license).