Re-architecture document structure (#669)

* Rearchitecture document structure

* Rearchitecture document structure

* Rearchitecture document structure

* Rearchitecture document structure

* Rearchitecture document structure

* Rearchitecture document structure

* Rearchitecture document structure

docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst

@@ -2,7 +2,7 @@
 Extended Kalman Filter Localization
 -----------------------------------
 
-.. image:: extended_kalman_filter_localization_files/extended_kalman_filter_localization_1_0.png
+.. image:: extended_kalman_filter_localization_1_0.png
    :width: 600px
 
 

docs/modules/localization/histogram_filter_localization/histogram_filter_localization_main.rst

@@ -53,12 +53,12 @@ the estimation error due to the movement.
 
 For example, the position probability is peaky with observations:
 
-.. image:: histogram_filter_localization/1.png
+.. image:: 1.png
    :width: 400px
 
 But, the probability is getting uncertain without observations:
 
-.. image:: histogram_filter_localization/2.png
+.. image:: 2.png
    :width: 400px
 
 
@@ -88,12 +88,12 @@ The probability of each grid is updated by this formula:
 
 When the `d` is 3.0, the `h(z)` distribution is:
 
-.. image:: histogram_filter_localization/4.png
+.. image:: 4.png
    :width: 400px
 
 The observation probability distribution looks a circle when a RF-ID is observed:
 
-.. image:: histogram_filter_localization/3.png
+.. image:: 3.png
    :width: 400px
 
 Step4: Estimate position from probability
@@ -110,5 +110,5 @@ There are two ways to calculate the final positions:
 References:
 ~~~~~~~~~~~
 
-- `PROBABILISTIC ROBOTICS`_
+- `_PROBABILISTIC ROBOTICS: <http://www.probabilistic-robotics.org>`_
 - `Robust Vehicle Localization in Urban Environments Using Probabilistic Maps <http://driving.stanford.edu/papers/ICRA2010.pdf>`_

docs/modules/localization/localization_main.rst

@@ -3,11 +3,14 @@
 Localization
 ============
 
-.. include:: extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst
-.. include:: ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization.rst
-.. include:: unscented_kalman_filter_localization/unscented_kalman_filter_localization.rst
-.. include:: histogram_filter_localization/histogram_filter_localization.rst
-.. include:: particle_filter_localization/particle_filter_localization.rst
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
+
+   extended_kalman_filter_localization_files/extended_kalman_filter_localization
+   ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization
+   unscented_kalman_filter_localization/unscented_kalman_filter_localization
+   histogram_filter_localization/histogram_filter_localization
+   particle_filter_localization/particle_filter_localization
 
-.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
 

docs/modules/localization/particle_filter_localization/particle_filter_localization_main.rst

@@ -33,5 +33,5 @@ The covariance matrix :math:`\Xi` from particle information is calculated by the
 References:
 ~~~~~~~~~~~
 
--  `PROBABILISTIC ROBOTICS`_
+- `_PROBABILISTIC ROBOTICS: <http://www.probabilistic-robotics.org>`_
 - `Improving the particle filter in high dimensions using conjugate artificial process noise <https://arxiv.org/pdf/1801.07000.pdf>`_