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.. Automatically generated from Quick-start.md with m2r
.. Manually patched
Quick start
===========
.. contents:: Table of Contents
:depth: 3
Installation
------------
Dependencies
^^^^^^^^^^^^
*
Eigen 3 :
*
Linux ( Ubuntu and similar )
.. code-block:: bash
apt-get install libeigen3-dev
*
OS X
.. code-block:: bash
brew install eigen
*
`lt::optional <https://github.com/TartanLlama/optional>`_ : included in the ``external`` folder
From source
^^^^^^^^^^^
To generate ``manif`` Python bindings run,
.. code-block:: bash
git clone https://github.com/artivis/manif.git
cd manif
python3 -m pip install .
Use manifpy in your project
---------------------------
.. code-block:: python
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 <https://github.com/artivis/manif/tree/devel/examples/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 <https://github.com/artivis/manif/tree/devel/examples/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 <https://github.com/artivis/manif/tree/devel/examples/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 <https://github.com/artivis/manif/tree/devel/examples/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 <https://github.com/artivis/manif/tree/devel/examples/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 <https://github.com/artivis/manif/tree/devel/examples/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,
.. code-block:: bash
cd manif/examples
python3 se2_localization.py
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