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Quick start

Installing manifpy

From conda

manifpy can be installed from the conda-forge,

conda install -c conda-forge manifpy

From source

Getting Pybind11

The Python wrappers are generated using pybind11. So first we need to install it, but we want it available directly in our environment root so that CMake can find it. To do so we can use,

python3 -m pip install "pybind11[global]"

Note that this is not recommended when using one's system Python, as it will add files to /usr/local/include/pybind11 and /usr/local/share/cmake/pybind11.

Another way is to use CMake to install it,

git clone https://github.com/pybind/pybind11.git
cd pybind11 && mkdir build && cd build
cmake ..
make install

Getting the dependencies

  • Eigen 3 :

    • Linux ( Ubuntu and similar )

      apt-get install libeigen3-dev

    • OS X

      brew install eigen

  • lt::optional : included in the external folder

Building

To generate manif Python bindings run,

git clone https://github.com/artivis/manif.git
cd manif
python3 -m pip install .

Testing

To run the tests you will also need numpy,

python3 -m pip install numpy

To run the tests, simply hits:

python3 -m pytest

Use manifpy in your project

from manifpy import SE3

...

state = SE3.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.py: 2D robot localization based on fixed landmarks using SE2 as robot poses. This implements the example V.A in the paper.
  • se3_localization.py: 3D robot localization based on fixed landmarks using SE3 as robot poses. This re-implements the example above but in 3D.
  • se2_sam.py: 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.py: 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.py: 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.py: 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 run a demo, simply go to the manif/examples/ folder and run,

cd manif/examples
python3 se2_localization.py