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Quick-start.md

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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.

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)', 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