Quick start

manif has been designed for an easy integration to larger projects:

  • A single dependency on Eigen,
  • header-only for easy integration,
  • templated on the underlying scalar type so that one can use its own,
  • and C++11, since not everyone gets to enjoy the latest C++ features, especially in industry.

All Lie group classes defined in manif have in common that they inherit from a templated base class (CRTP). It allows one to write generic code abstracting the Lie group details. Please find more information in the related [documentation page](Writing-generic-code).

The library supports template scalar types. In particular, it can work with the ceres::Jet type, allowing for automatic Jacobian computation – see related paragraph on Jacobians below.

Installation

Dependencies

  • Eigen 3 :

    • Linux ( Ubuntu and similar )

      ``` apt-get install libeigen3-dev ```

    • OS X

      ``` brew install eigen ```

  • lt::optional : included in the external folder

From source

git clone https://github.com/artivis/manif.git
cd manif && mkdir build && cd build
cmake ..
make install

Use manif in your project

In your project CMakeLists.txt :

project(foo)
# Find the Eigen library
find_package(Eigen3 REQUIRED)
target_include_directories(${PROJECT_NAME} SYSTEM PUBLIC ${EIGEN3_INCLUDE_DIRS})
# Find the manif library
find_package(manif REQUIRED)
add_executable(${PROJECT_NAME} src/foo.cpp)
# Add manif include directories to the target
target_include_directories(${PROJECT_NAME} SYSTEM PUBLIC ${manif_INCLUDE_DIRS})

In your code:

#include <manif/manif.h>

...

auto state = manif::SE3d::Identity();

...

Tutorials and application demos

We provide some self-contained and self-explained executables implementing some real problems. Their source code is located in manif/examples/. These demos are:

  • se2_localization.cpp: 2D robot localization based on fixed landmarks using SE2 as robot poses. This implements the example V.A in the paper.
  • se2_localization_ukfm.cpp: 2D robot localization based on fixed landmarks using SE2 as robot poses. This implements the filter described in ['A Code for Unscented Kalman Filtering on Manifolds (UKF-M)'](brossard-ukfm), M. Brossard, A. Barrau and S. Bonnabel.
  • se3_localization.cpp: 3D robot localization based on fixed landmarks using SE3 as robot poses. This re-implements the example above but in 3D.
  • se2_sam.cpp: 2D smoothing and mapping (SAM) with simultaneous estimation of robot poses and landmark locations, based on SE2 robot poses. This implements a the example V.B in the paper.
  • se3_sam.cpp: 3D smoothing and mapping (SAM) with simultaneous estimation of robot poses and landmark locations, based on SE3 robot poses. This implements a 3D version of the example V.B in the paper.
  • se3_sam_selfcalib.cpp: 3D smoothing and mapping (SAM) with self-calibration, with simultaneous estimation of robot poses, landmark locations and sensor parameters, based on SE3 robot poses. This implements a 3D version of the example V.C in the paper.
  • se_2_3_localization.cpp: A strap down IMU model based 3D robot localization, with measurements of fixed landmarks, using SE_2_3 as extended robot poses (translation, rotation and linear velocity).

To build the demos, simply pass the related flag to CMake,

cmake -DBUILD_EXAMPLES=ON ..
make
cd examples
./se2_localization