KalmanCPP

Kalman filter library written in C++17 with an emphasis on templates. This library provides an easily extensible framework for implementing your own Kalman filters based on the provided base implementations.

Currently, only an extended Kalman filter (EKF) base implementation is available. The user implements their own prediction and update steps and passes to the filter. The library also allows the user to choose between using numerical and analytical methods for calculating Jacobians, further simplifying implementation. For further details, see the example folder.

More filters such as the unscented Kalman filter will be added in the future when time allows. In the meantime, contributions are welcome (please refer to CONTRIBUTING.md).

Getting started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

• CMake v3.15+ - Found at https://cmake.org/

• C++ Compiler - Needs to support at least the C++17 standard. This project has so far only been tested using GCC (v9.3) so it is not guaranteed to work on other compilers such as Clang or MSVC. Additional compiler support might be implemented in the future and pull requests are always welcome.

Note: You also need to be able to provide CMake a supported generator. This project has mostly been tested with Ninja so it is recommended to use that one unless you have a good reason to use something else.

• Conan (package manager) - Used for managing dependancies such as the Eigen library. More information here.

Building the library (as a standalone project)

If you are only interested in using the KalmanCpp library inside your own project, skip to the next section. Building the library as a standalone-project should only be needed if you plan on contributing to the project or if you want to run the examples.

First clone the repository and run git submodule update --init to update the submodules used in the example.

We recommend building your library through an editor such as CLion, Visual Studio or Visual Studio Code. If you want to build from the terminal however, you can run the following commands from the library root directory:

mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release -GNinja .. && ninja

Including KalmanCpp in your own project

To include KalmanCpp in your own project, you can clone the library as a submodule and use the add_subdirectory(kalman_cpp) command in your CMakeLists.txt file. You will also need to link the library to your project using the target_link_libraries command in CMake.

Note the option for building the examples if you do not with to build them, see in cmake/StandardSettings.cmake.

Running the tests

This library makes use of Google Test for unit testing. Unit testing can be disabled in the options, by setting the ENABLE_UNIT_TESTING (from cmake/StandardSettings.cmake) to be false. To run the tests, simply use CTest, from the build directory, passing the desire configuration for which to run tests for. An example of this procedure is:

cd build/Release
ctest -C Release  # or `ctest -C Debug` or any other configuration you wish to test

# you can also run tests with the `-VV` flag for a more verbose output (i.e.
#GoogleTest output as well)

Contributing

Please read CONTRIBUTING.md for details on our how you can become a contributor and the process for submitting pull requests to us.

Versioning

This project makes use of SemVer for versioning. A list of existing versions can be found in the project's releases.

Authors

• Thorsteinn Jonsson - @ThorsteinnJonsson

Acknowledgments

We would like to thank the following:

filipdutescu's C++ project template for providing an excellent project template that helped get this project started.

alandefreitas' Matplot++ library, a C++ Graphics Library for Data Visualization used in out examples.

License

This project is licensed under the MIT license - see the LICENSE file for details