Extended Kalman Filter Calibration and Localization: ekf_cal is a package focused on the simulation and development of a multi-sensor online calibration kalman filter. It combines the architecture of a Multi-State Constraint Kalman Filter (MSCKF) with a multi-sensor calibration filter to provide intrinsic and extrinsic estimates for the following sensors:
This guide assumes you have the ekf_cal repository in a colcon workspace.
mkdir ekf_cal_ws/
mkdir ekf_cal_ws/src/
cd ekf_cal_ws/src/
git clone [email protected]:unmannedlab/ekf_cal.git
cd ../
The ekf_cal package has the following hard dependencies that are required for all compilations:
The following dependencies are for building the ROS node and simulation, respectively
The following soft dependencies useful for development and documentation
These can be installed by running rosdep in the base directory of the colcon workspace (e.g. ekf_cal_ws)
rosdep install --from-paths src -y --ignore-src
Building can be done simply with the following command:
colcon build --symlink-install --packages-select ekf_cal --event-handlers console_cohesion+ --cmake-args -DCMAKE_C_FLAGS='--coverage' -DCMAKE_CXX_FLAGS='--coverage'
Alternatively, a Dockerfile is provided, which can be used either inside a VS Code devcontainer, or a standalone container.
This repository offers two main ways to utilize the Kalman filter framework: a simulation and ROS2 node. Both the simulation and ROS node are configurable and runnable using identically formatted YAML files. Further documentation can be found on the Parameters page.
Simulations can be run using a YAML configuration file that extends the base configuration file with additional parameters. See the example example.yaml. Multiple simulations can be run in parallel using the run.py. An example using a single input is given below
python3 eval/run.py config/example.yaml
The results of a run can be plotted using report.py
python3 eval/report.py config/example.yaml
To run and plot in sequence, utilize evaluate.py
python3 eval/evaluate.py config/example.yaml
This will generate and run the requested number of simulation runs for the specified run time and produce plots of the Monte Carlo data. For example, the report generates plots of the body acceleration estimates and the true error in those acceleration estimates.
For an example of a filter node launch file, see example.launch.py
In particular, note the configuration file example.yaml.
The configuration file specifies which sensor topics should to use and the initialization values. Once built, the ROS node can be started by running the following command
ros2 launch example.launch
Once the package has been built, unit tests and static analysis can be run with the following commands
colcon test --packages-select ekf_cal --event-handlers console_direct+
A test code coverage report can be generated using the following commands
colcon build --symlink-install --packages-select ekf_cal \
--event-handlers console_cohesion+ \
--cmake-args -DCMAKE_C_FLAGS='--coverage' -DCMAKE_CXX_FLAGS='--coverage'
colcon test --packages-select ekf_cal --pytest-with-coverage \
--pytest-args --cov-report=term --event-handlers console_direct+
colcon lcov-result --packages-select ekf_cal --filter '*_test.cpp' '*_main.cpp'
The lines of code in the repository can be counted (cloc) using the following command
echo 'Count Lines of Code {#cloc}\n============' > docs/software/cloc.md && \
cloc src eval --md | tail -n +4 >> docs/software/cloc.md && \
sed -i 's/--------|--------|--------|--------|--------/| | | | | |/' docs/software/cloc.md
A performance flamegraph can be generated using the following command
cd docs/flamegraph/ && ./run_perf.sh
Documentation can be generated using the following command:
doxygen .doxyfile
A single pdf can be generated of the documentation using the following command
doxygen .doxyfile && cd docs/doxygen/latex && make
@inproceedings{2023_Multi_IMU,
title = {Online Multi-IMU Calibration Using Visual-Inertial Odometry},
booktitle = {2023 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)},
author = {Jacob Hartzer and Srikanth Saripalli},
year = {2023},
doi = {10.1109/SDF-MFI59545.2023.10361310},
arxiv = {2310.12411},
}@inproceedings{2022_Multi_Cam,
title = {Online Multi Camera-IMU Calibration},
booktitle = {2022 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)},
author = {Hartzer, Jacob and Saripalli, Srikanth},
year = {2022},
pages = {360-365},
doi = {10.1109/SSRR56537.2022.10018692},
arxiv = {2209.13821},
}