libRSF - A Robust Sensor Fusion Library

The libRSF is an open source C++ library that provides the basic components for robust sensor fusion. It can be used to describe an estimation problem as a factor graph and solves it with least squares, powered by the Ceres Solver. More information can be found under libRSF - A Robust Sensor Fusion Library.

Main features are: - A sliding window filter for online applications, including marginalization. - A set of predefined cost functions for various localization problems. - Several robust error models for non-Gaussian problems, including self-tuning Gaussian mixtures.

Build and Test Status

Platform	Status
Ubuntu 20.04
Ubuntu 18.04

Installation

The libRSF is a CMake project that requires the installation of several dependencies. For convenience, we provide a simple bash script that installs required packages. It is tested only for Ubuntu 18.04/20.04:

  git clone https://github.com/TUC-ProAut/libRSF.git
  cd libRSF
  bash InstallDependencies.bash

Alternatively, you can install them by your own:

• CMake (>= 3.5)

bash sudo apt-get install cmake

• Eigen (>= 3.3.5)

Only for Ubuntu 18.04, you have to install a current version of Eigen locally.

```bash mkdir -p externals/install git submodule update --init externals/eigen

cd externals/eigen mkdir -p build && cd build cmake -DCMAKE_INSTALL_PREFIX=../../install/ .. make install cd ../../.. ```

For Ubuntu >= 20.04 you can install Eigen straight-forward.

bash sudo apt-get install libeigen3-dev

• Ceres (>= 2.0) and its dependencies

```bash sudo apt-get install libgoogle-glog-dev sudo apt-get libgflags-dev sudo apt-get install libatlas-base-dev sudo apt-get install libsuitesparse-dev

mkdir -p externals/install
git submodule update --init externals/ceres-solver

cd externals/ceres-solver mkdir build && cd build cmake -DEigen3_DIR=../install/share/eigen3/cmake -DCMAKE_INSTALL_PREFIX=../../install/ .. make all -j$(getconf _NPROCESSORS_ONLN) make install cd ../.. ```

• yaml-cpp

bash sudo apt-get install libyaml-cpp-dev

• GeographicLib

bash sudo apt-get install libgeographic-dev

The library and its applications can be build following this instructions:

  git clone https://github.com/TUC-ProAut/libRSF.git
  cd libRSF
  mkdir build && cd build
  cmake ..
  make all -j$(getconf _NPROCESSORS_ONLN)

You can install the libRSF using:

  make install

And remove it using:

  make uninstall

Usage

After building the library, some applications are provided which correspond directly to a publication. The following pages give you an overview, how to use them or how to build a custom application using the libRSF:

1. How to use the robust GNSS localization from our ICRA 2019 or IV 2019 paper?

2. How to use the robust Gaussian mixture models from our RA-L 2021 Paper?

3. How to build your own application on top of the libRSF?

Additional Information

Citation

If you use this library for academic work, please cite it using the following BibTeX reference:

  @Misc{libRSF,
   author       = {Tim Pfeifer and Others},
   title        = {libRSF},
   howpublished = {\url{https://github.com/TUC-ProAut/libRSF}}
  }

This library also contains the implementation of [1-3]. Further references will be added with additional content.

[1] Tim Pfeifer and Peter Protzel, Expectation-Maximization for Adaptive Mixture Models in Graph Optimization, Proc. of Intl. Conf. on Robotics and Automation (ICRA), 2019, DOI: 10.1109/ICRA.2019.8793601

[2] Tim Pfeifer and Peter Protzel, Incrementally learned Mixture Models for GNSS Localization, Proc. of Intelligent Vehicles Symposium (IV), 2019, DOI: 10.1109/IVS.2019.8813847

[3] Tim Pfeifer and Sven Lange and Peter Protzel, Advancing Mixture Models for Least Squares Optimization, Robotics and Automation Letters (RA-L), 2021, DOI: 10.1109/LRA.2021.3067307

License

This work is released under the GNU General Public License version 3.