InEKF

InEKF is a light-weight library for Invariant Extended Kalman Filter on matrix Lie groups. InEKF is implemented for systems whose states evolve on the abstract $\mathrm{N}$-dimensional $\mathrm{K}$ direct isometry matrix Lie groups $\mathrm{SE_K(N)}$ (including the 3-dimensional special orthogonal group $\mathrm{SO(3)}$, the 3-dimensional $\mathrm{K}$ direct isometry special Euclidean group $\mathrm{SE_K(3)}$ and $\mathrm{N}$-dimensional translation matrix group $\mathrm{T(N)}$) and Euclidean vector spaces. Except for InEKF, the library can be extended to other Kalman filters, e.g. the conventional Extended Kalman Filter (EKF) or the Iterated Error-State Extended Kalman Filter (IESEKF) for multiple robot platforms, such as legged robots and Unmanned Aerial Vehicles (UAVs).

Build

Prerequisites

• GCC and CMake

• Eigen3: Linear algebra

  On Ubuntu 18.04, we can install CMake and Eigen3 following:

  # gcc and cmake 
  sudo apt-get install gcc cmake 
  # Eigen3 
  sudo apt-get install libeigen3-dev

Build the Library

• We can build the InEKF library following:

  git clone https://github.com/zha0ming1e/InEKF 
  cd ./InEKF/ 
  mkdir build && cd build/ 
  cmake .. && make -j8 
  # or: cmake .. && make -j$(( $( cat /proc/cpuinfo | grep "processor" | sort -u | wc -l ) - 1 ))

Include InEKF in Your Projects

• InEKF can be included in your cmake project by adding the InEKF directory to your CMakeLists.txt:

  find_package(inekf) 
  include_directories(${inekf_INCLUDE_DIRS}) 

References

• Barrau, A. and Bonnabel, S., 2016. The invariant extended Kalman filter as a stable observer. IEEE Transactions on Automatic Control, 62(4), pp.1797-1812.

  @article{barrau2016invariant,
    title={The invariant extended Kalman filter as a stable observer},
    author={Barrau, Axel and Bonnabel, Silv{\`e}re},
    journal={IEEE Transactions on Automatic Control},
    volume={62},
    number={4},
    pages={1797--1812},
    year={2016},
    publisher={IEEE}
  }
  

• Hartley, R., Ghaffari, M., Eustice, R.M. and Grizzle, J.W., 2020. Contact-aided invariant extended Kalman filtering for robot state estimation. The International Journal of Robotics Research, 39(4), pp.402-430.

  @article{hartley2020contact,
    title={Contact-aided invariant extended Kalman filtering for robot state estimation},
    author={Hartley, Ross and Ghaffari, Maani and Eustice, Ryan M and Grizzle, Jessy W},
    journal={The International Journal of Robotics Research},
    volume={39},
    number={4},
    pages={402--430},
    year={2020},
    publisher={SAGE Publications Sage UK: London, England}
  }
  

• Bloesch, M., Hutter, M., Hoepflinger, M.A., Leutenegger, S., Gehring, C., Remy, C.D. and Siegwart, R., 2013. State estimation for legged robots-consistent fusion of leg kinematics and IMU. Robotics, 17, pp.17-24.

  @article{bloesch2013state,
    title={State estimation for legged robots-consistent fusion of leg kinematics and IMU},
    author={Bloesch, Michael and Hutter, Marco and Hoepflinger, Mark A and Leutenegger, Stefan and Gehring, Christian and Remy, C David and Siegwart, Roland},
    journal={Robotics},
    volume={17},
    pages={17--24},
    year={2013},
    publisher={MIT Press}
  }
  

• Ramadoss, P., Romualdi, G., Dafarra, S., Chavez, F.J.A., Traversaro, S. and Pucci, D., 2021, May. Diligent-kio: A proprioceptive base estimator for humanoid robots using extended kalman filtering on matrix lie groups. In 2021 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2904-2910). IEEE.

  @inproceedings{ramadoss2021diligent,
    title={Diligent-kio: A proprioceptive base estimator for humanoid robots using extended kalman filtering on matrix lie groups},
    author={Ramadoss, Prashanth and Romualdi, Giulio and Dafarra, Stefano and Chavez, Francisco Javier Andrade and Traversaro, Silvio and Pucci, Daniele},
    booktitle={2021 IEEE International Conference on Robotics and Automation (ICRA)},
    pages={2904--2910},
    year={2021},
    organization={IEEE}
  }
  

• Ramadoss, P., Romualdi, G., Dafarra, S., Traversaro, S. and Pucci, D., 2022, October. Comparison of EKF-Based Floating Base Estimators for Humanoid Robots with Flat Feet. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 6780-6787). IEEE.

  @inproceedings{ramadoss2022comparison,
    title={Comparison of EKF-Based Floating Base Estimators for Humanoid Robots with Flat Feet},
    author={Ramadoss, Prashanth and Romualdi, Giulio and Dafarra, Stefano and Traversaro, Silvio and Pucci, Daniele},
    booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
    pages={6780--6787},
    year={2022},
    organization={IEEE}
  }