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.
Platform | Build Status |
---|---|
Ubuntu 20.04 | |
Ubuntu 18.04 |
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)
sudo apt-get install cmake
-
Eigen (>= 3.3.5)
Only for Ubuntu 18.04, 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
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:
-
How to use the robust GNSS localization from our ICRA 2019 or IV 2019 paper?
-
How to use the robust Gaussian mixture models from our RA-L 2020 Paper?
-
How to build your own application on top of the libRSF? (under construction)
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), 2020 (coming soon)
This work is released under the GNU General Public License version 3.