Awesome

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 22.04
Ubuntu 20.04

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 20.04/22.04:

  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)

  sudo apt-get install cmake
  

• Eigen (>= 3.3.5)

  Only for Ubuntu < 20.04, you have to install a current version of Eigen locally.

  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.

  sudo apt-get install libeigen3-dev
  

• Ceres (>= 2.0) and its dependencies

  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

  sudo apt-get install libyaml-cpp-dev
  

• GeographicLib

  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.