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docs (Progress with #1252)
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35 changes: 35 additions & 0 deletions doc/source/tutorial-pdf-over-poses.rst
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.. _tutorial-pdf-over-poses:

===================================================================
Probability Density Functions (PDFs) over spatial transformations
===================================================================

These distributions can be used as to represent the robot positining belief
and the map uncertainty in many localization and SLAM algorithms.

They include unimodal Gaussians, sum of Gaussians, sets of particles,
and grid representations, methods to convert between them and to draw
an arbitrary number of samples from any kind of distribution:


- Points:

- R^3 points: `mrpt::poses::CPointPDF <class_mrpt_poses_CPointPDF.html>`_.

- SE(2) poses:

- `mrpt::poses::CPosePDF <class_mrpt_poses_CPosePDF.html>`_

- SE(3) poses:

- `mrpt::poses::CPose3DPDF <class_mrpt_poses_CPose3DPDF.html>`_ (for poses as rotation matrix and yaw/pitch/roll).

- `mrpt::poses::CPose3DQuatPDF <class_mrpt_poses_CPose3DQuatPDF.html>`_ (for poses as quaternions) poses.

Each of the above abstract classes has implementations for different
kinds of representing the spatial uncertainty:
particles using importance sampling, a single monomodal gaussian,
or a sum of gaussians.

The technical report :cite:`blanco_se3_tutorial` contains most
of the derivations of the implemented Jacobians.
41 changes: 41 additions & 0 deletions doc/source/tutorial-slam-algorithms.rst
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.. _tutorial-slam-algorithms:

===========================================================================
SLAM algorithms in MRPT
===========================================================================

Not all SLAM algorithms fit any kind of observation (sensor data) and produce any map type.
The following summarizes the SLAM algorithms implemented in MRPT and their associated map and observation types,
grouped by input sensors.

- 2D laser scanner `mrpt::obs::CObservation2DRangeScan <class_mrpt_obs_CObservation2DRangeScan.html>`_:
- To generate 2D occupancy grids (`mrpt::maps::COccupancyGridMap2D <class_mrpt_maps_COccupancyGridMap2D.html>`_)
or point clouds (`mrpt::maps::CPointsMap <class_mrpt_maps_CPointsMap.html>`_).

- `rbpf-slam <page_app_rbpf-slam.html>`_
- `icp-slam <page_app_icp-slam.html>`_

- Sonar sensors `mrpt::obs::CObservationRange <class_mrpt_obs_CObservationRange.html>`_:
- To generate 2D occupancy grids (`mrpt::maps::COccupancyGridMap2D <class_mrpt_maps_COccupancyGridMap2D.html>`_):

- `rbpf-slam <page_app_rbpf-slam.html>`_

- Range-bearing landmarks (`mrpt::obs::CObservationBearingRange <class_mrpt_obs_CObservationBearingRange.html>`_):

- EKF-based SLAM: `kf-slam <page_app_kf-slam.html>`_


- Monocular image features (visual keypoint matches from a visual SLAM front-end):

- Back-end only: `vision_bundle_adj_example <page_vision_bundle_adj_example.html>`_


- Range-only sensors (`mrpt::obs::CObservationBeaconRanges <class_mrpt_obs_CObservationBeaconRanges.html>`_):

- `Range-only SLAM page <range_only_localization_mapping.html>`_


- Relative poses (Pose-graph or Graph-SLAM):

- Graph-SLAM maps (Write me!) https://www.mrpt.org/Graph-SLAM_maps

2 changes: 2 additions & 0 deletions doc/source/tutorials.rst
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:caption: Robotics, SLAM

tutorial-slam-for-beginners-the-basics
tutorial-slam-algorithms
tutorial-mrpt-maps-model
range_only_localization_mapping
tutorial-icp-alignment
Expand All @@ -41,6 +42,7 @@ Note: This page is in the process of being imported from https://www.mrpt.org/tu

page_tutorial_math_levenberg_marquardt
tutorial-ransac
tutorial-pdf-over-poses

.. toctree::
:maxdepth: 2
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