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inekf

This repository contains a C++ library that implements an invariant extended Kalman filter (InEKF) for 3D aided inertial navigation.

This filter can be used to estimate a robot's 3D pose and velocity using an IMU motion model for propagation. The following measurements are currently supported:

• Prior landmark position measurements (localization)
• Estiamted landmark position measurements (SLAM)
• Kinematic and contact measurements

The core theory was developed by Barrau and Bonnabel and is presented in: "The Invariant Extended Kalman filter as a Stable Observer".

Inclusion of kinematic and contact measurements is presented in: "Contact-aided Invariant Extended Kalman Filtering for Legged Robot State Estimation".

A ROS wrapper for the filter is available at https://github.com/RossHartley/invariant-ekf-ros.

Setup

Requirements

• CMake 2.8.3 or later
• g++ 5.4.0 or later
• Eigen3 C++ Library

Installation Using CMake

mkdir build
cd build 
cmake .. 
make

invariant-ekf can be easily included in your cmake project by adding the following to your CMakeLists.txt:

find_package(inekf) 
include_directories(${inekf_INCLUDE_DIRS})

Examples

1. A landmark-aided inertial navigation example is provided at src/examples/landmarks.cpp
2. A contact-aided inertial navigation example is provided at src/examples/kinematics.cpp

Citations

The contact-aided invariant extended Kalman filter is described in:

• R. Hartley, M. G. Jadidi, J. Grizzle, and R. M. Eustice, “Contact-aided invariant extended kalman filtering for legged robot state estimation,” in Proceedings of Robotics: Science and Systems, Pittsburgh, Pennsylvania, June 2018.

@INPROCEEDINGS{Hartley-RSS-18, 
    AUTHOR    = {Ross Hartley AND Maani Ghaffari Jadidi AND Jessy Grizzle AND Ryan M Eustice}, 
    TITLE     = {Contact-Aided Invariant Extended Kalman Filtering for Legged Robot State Estimation}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2018}, 
    ADDRESS   = {Pittsburgh, Pennsylvania}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2018.XIV.050} 
} 

The core theory of invariant extended Kalman filtering is presented in:

• Barrau, Axel, and Silvère Bonnabel. "The invariant extended Kalman filter as a stable observer." IEEE Transactions on Automatic Control 62.4 (2017): 1797-1812.

@article{barrau2017invariant,
  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={2017},
  publisher={IEEE}
}