libRSF - A Robust Sensor Fusion Library

The libRSF is an open source C++ library that provides several components that are required for robust sensor fusion. It can be used to describe the estimation problem as factor graph and solve it using the Ceres Solver. More information can be found under libRSF - A Robust Sensor Fusion Library.

Main features are:

A set of predefined error functions for localization problems.
Several robust error models for non-Gaussian problems.
A sliding window filter for online applications.

License

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

Citation

This library is the implementation of [1] and [2]. 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

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

BibTeX:

    @InProceedings{Pfeifer2019,
    author    = {Tim Pfeifer and Peter Protzel},
    title     = {Expectation-Maximization for Adaptive Mixture Models in Graph Optimization},
    booktitle = {Proc. of Intl. Conf. on Robotics and Automation (ICRA)},
    year      = {2019},
    }

    @InProceedings{Pfeifer2019a,
    author    = {Tim Pfeifer and Peter Protzel},
    title     = {Incrementally learned Mixture Models for GNSS Localization},
    booktitle = {Proc. of Intelligent Vehicles Symposium (IV)},
    year      = {2019},
    }

Installation

The libRSF is a CMake project that requires the installation of the following dependencies:

CMake (>= 2.8)
```
sudo apt-get install cmake
```
Eigen (>= 3.3)
```
sudo apt-get install libeigen3-dev
```

Ceres (>= 2.0) and its dependencies

sudo apt-get install libgoogle-glog-dev
sudo apt-get install libatlas-base-dev
sudo apt-get install libsuitesparse-dev

git clone https://ceres-solver.googlesource.com/ceres-solver
cd ceres-solver
mkdir build && cd build
cmake ..
make all -j8
make install

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

  git clone https://github.com/tipf/libRSF.git
  cd libRSF
  mkdir build && cd build
  cmake ..
  make all -j8

Applications

After building the library, some applications are provided. Usually they correspond directly to a publication.

ICRA 2019

These two applications are made for the ICRA 2019 conference, the corresponding paper is [1]. One can be used for GNSS datasets and calculates a 3D position in the ECEF frame, while the other one is for 2D ranging datasets. To run them, the following syntax have to be used:

  libRSF/build/applications/ICRA19_GNSS     <input file> <output file> error: <error model>
  libRSF/build/applications/ICRA19_Ranging  <input file> <output file> error: <error model>

<input file> is the dataset you want to process, the format is explained by readme files in the datasets folder.

<output file> is the estimated Trajectory. The output file contains several columns that represent timestamps and estimated positions:

For 3D estimation:
Column 1    - Timestamp [s]
Column 2    - X coordinate in the ECEF frame [m]
Column 3    - Y coordinate in the ECEF frame [m]
Column 4    - Z coordinate in the ECEF frame [m]
Column 5-13 - Covariance matrix of the estimated position in row-major format (Currently not used!)

For 2D estimation:
Column 1    - Timestamp [s]
Column 2    - X coordinate in a local frame [m]
Column 3    - Y coordinate in a local frame [m]
Column 4-7  - Covariance matrix of the estimated position in row-major format (Currently not used!)

<error model> is one of the following error models:

gauss -     A Gaussian distribution
dcs   -     Dynamic Covariance Scaling
cdce  -     Closed form Dynamic Covariance Estimation
mm    -     Max-Mixture (an approximation of a Gaussian mixture)
sm    -     Sum-Mixture (an exact Gaussian mixture)
stmm  -     Adaptive Max-Mixture using the EM Algorithm
stsm  -     Adaptive Sum-Mixture using the EM Algorithm

A full example could be:

  libRSF/build/applications/ICRA19_GNSS libRSF/datasets/smartLoc/Data_Berlin_Potsdamer_Platz_Web.txt Result_Berlin_Potsdamer_Platz_Web.txt error: gauss

IV 2019

These application is made for the IV 2019 conference, the corresponding paper is [2]. It can be used for GNSS datasets and calculates a 3D position in the ECEF frame. To run them, the following syntax have to be used:

  libRSF/build/applications/IV19_GNSS     <input file> <output file> error: <error model>

<input file> is the dataset you want to process, the format is explained by readme files in the datasets folder.