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/index_main.rst

@@ -31,7 +31,7 @@ See this paper for more details:
 
 .. toctree::
    :maxdepth: 2
-   :caption: Guide
+   :caption: Contents
 
    getting_started
    modules/introduction

docs/modules/aerial_navigation/aerial_navigation_main.rst

@@ -3,6 +3,10 @@
 Aerial Navigation
 =================
 
-.. include:: drone_3d_trajectory_following/drone_3d_trajectory_following.rst
-.. include:: rocket_powered_landing/rocket_powered_landing.rst
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
+
+   drone_3d_trajectory_following/drone_3d_trajectory_following
+   rocket_powered_landing/rocket_powered_landing
 

docs/modules/appendix/appendix_main.rst

@@ -3,7 +3,10 @@
 Appendix
 ==============
 
-.. include:: Kalmanfilter_basics.rst
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
 
-.. include:: Kalmanfilter_basics_2.rst
+   Kalmanfilter_basics
+   Kalmanfilter_basics_2
 

docs/modules/arm_navigation/arm_navigation_main.rst

@@ -3,26 +3,10 @@
 Arm Navigation
 ==============
 
-.. include:: planar_two_link_ik.rst
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
 
-
-N joint arm to point control
-----------------------------
-
-N joint arm to a point control simulation.
-
-This is a interactive simulation.
-
-You can set the goal position of the end effector with left-click on the
-plotting area.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/n_joint_arm_to_point_control/animation.gif
-
-In this simulation N = 10, however, you can change it.
-
-Arm navigation with obstacle avoidance
---------------------------------------
-
-Arm navigation with obstacle avoidance simulation.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/arm_obstacle_navigation/animation.gif
+   planar_two_link_ik
+   n_joint_arm_to_point_control
+   obstacle_avoidance_arm_navigation

docs/modules/arm_navigation/n_joint_arm_to_point_control_main.rst

@@ -0,0 +1,13 @@
+N joint arm to point control
+----------------------------
+
+N joint arm to a point control simulation.
+
+This is a interactive simulation.
+
+You can set the goal position of the end effector with left-click on the
+plotting area.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/n_joint_arm_to_point_control/animation.gif
+
+In this simulation N = 10, however, you can change it.

docs/modules/arm_navigation/obstacle_avoidance_arm_navigation_main.rst

@@ -0,0 +1,6 @@
+Arm navigation with obstacle avoidance
+--------------------------------------
+
+Arm navigation with obstacle avoidance simulation.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/arm_obstacle_navigation/animation.gif

docs/modules/bipedal/bipedal_main.rst

@@ -3,5 +3,9 @@
 Bipedal
 =================
 
-.. include:: bipedal_planner/bipedal_planner.rst
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
+
+   bipedal_planner/bipedal_planner
 

docs/modules/control/control_main.rst

@@ -3,7 +3,10 @@
 Control
 =================
 
-.. include:: inverted_pendulum_control/inverted_pendulum_control.rst
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
 
-.. include:: move_to_a_pose_control/move_to_a_pose_control.rst
+   inverted_pendulum_control/inverted_pendulum_control
+   move_to_a_pose_control/move_to_a_pose_control
 

docs/modules/control/inverted_pendulum_control/inverted_pendulum_control_main.rst

@@ -9,7 +9,7 @@ The objective of the control system is to balance the inverted pendulum by apply
 Modeling
 ~~~~~~~~~~~~
 
-.. image:: inverted_pendulum_control/inverted-pendulum.png
+.. image:: inverted-pendulum.png
     :align: center
 
 - :math:`M`: mass of the cart

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>`_

docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst

@@ -16,7 +16,7 @@ a ``numpy array``, and numbers close to 1 means the cell is occupied
 free (*marked with green*). The grid has the ability to represent
 unknown (unobserved) areas, which are close to 0.5.
 
-.. figure:: lidar_to_grid_map_tutorial/grid_map_example.png
+.. figure:: grid_map_example.png
 
 In order to construct the grid map from the measurement we need to
 discretise the values. But, first let’s need to ``import`` some
@@ -68,7 +68,7 @@ From the distances and the angles it is easy to determine the ``x`` and
 
 
 
-.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_5_0.png
+.. image:: lidar_to_grid_map_tutorial_5_0.png
 
 
 The ``lidar_to_grid_map.py`` contains handy functions which can used to
@@ -89,7 +89,7 @@ map. Let’s see how this works.
 
 
 
-.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_7_0.png
+.. image:: lidar_to_grid_map_tutorial_7_0.png
 
 
 .. code:: ipython3
@@ -106,7 +106,7 @@ map. Let’s see how this works.
 
 
 
-.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_8_0.png
+.. image:: lidar_to_grid_map_tutorial_8_0.png
 
 
 To fill empty areas, a queue-based algorithm can be used that can be
@@ -163,7 +163,7 @@ from a center point (e.g. (10, 20)) with zeros:
 
 
 
-.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_12_0.png
+.. image:: lidar_to_grid_map_tutorial_12_0.png
 
 
 Let’s use this flood fill on real data:
@@ -194,5 +194,5 @@ Let’s use this flood fill on real data:
 
 
 
-.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_14_1.png
+.. image:: lidar_to_grid_map_tutorial_14_1.png
 

docs/modules/mapping/mapping_main.rst

@@ -2,11 +2,14 @@
 
 Mapping
 =======
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
 
-.. include:: gaussian_grid_map/gaussian_grid_map.rst
-.. include:: ray_casting_grid_map/ray_casting_grid_map.rst
-.. include:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial.rst
-.. include:: k_means_object_clustering/k_means_object_clustering.rst
-.. include:: circle_fitting/circle_fitting.rst
-.. include:: rectangle_fitting/rectangle_fitting.rst
+   gaussian_grid_map/gaussian_grid_map
+   ray_casting_grid_map/ray_casting_grid_map
+   lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial
+   k_means_object_clustering/k_means_object_clustering
+   circle_fitting/circle_fitting
+   rectangle_fitting/rectangle_fitting
 

docs/modules/path_planning/bezier_path/bezier_path_main.rst

@@ -5,13 +5,13 @@ A sample code of Bezier path planning.
 
 It is based on 4 control points Beizer path.
 
-.. image:: Bezier_path/Figure_1.png
+.. image:: Figure_1.png
 
 If you change the offset distance from start and end point,
 
 You can get different Beizer course:
 
-.. image:: Bezier_path/Figure_2.png
+.. image:: Figure_2.png
 
 Ref:
 

docs/modules/path_planning/bspline_path/bspline_path_main.rst

@@ -1,7 +1,7 @@
 B-Spline planning
 -----------------
 
-.. image:: bspline_path/Figure_1.png
+.. image:: Figure_1.png
 
 This is a path planning with B-Spline curse.
 

docs/modules/path_planning/cubic_spline/cubic_spline_main.rst

@@ -8,6 +8,6 @@ with cubic spline.
 
 Heading angle of each point can be also calculated analytically.
 
-.. image:: cubic_spline/Figure_1.png
-.. image:: cubic_spline/Figure_2.png
-.. image:: cubic_spline/Figure_3.png
+.. image:: Figure_1.png
+.. image:: Figure_2.png
+.. image:: Figure_3.png

docs/modules/path_planning/dubins_path/dubins_path_main.rst

@@ -19,12 +19,12 @@ Possible path will be at least one of these six types: RSR, RSL, LSR, LSL, RLR,
 
 For example, one of RSR Dubins paths is:
 
-.. image:: dubins_path/RSR.jpg
+.. image:: RSR.jpg
    :width: 400px
 
 one of RLR Dubins paths is:
 
-.. image:: dubins_path/RLR.jpg
+.. image:: RLR.jpg
    :width: 200px
 
 Dubins path planner can output three types and distances of each course segment.

docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach_main.rst

@@ -1,3 +1,5 @@
+.. _dynamic_window_approach:
+
 Dynamic Window Approach
 -----------------------
 

docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst

@@ -14,7 +14,7 @@ Path optimization sample
 Lookup table generation sample
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. image:: model_predictive_trajectory_generator/lookup_table.png
+.. image:: lookup_table.png
 
 Ref:
 

docs/modules/path_planning/path_planning_main.rst

@@ -3,24 +3,28 @@
 Path Planning
 =============
 
-.. include:: dynamic_window_approach/dynamic_window_approach.rst
-.. include:: bugplanner/bugplanner.rst
-.. include:: grid_base_search/grid_base_search.rst
-.. include:: model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst
-.. include:: state_lattice_planner/state_lattice_planner.rst
-.. include:: prm_planner/prm_planner.rst
-.. include:: visibility_road_map_planner/visibility_road_map_planner.rst
-.. include:: vrm_planner/vrm_planner.rst
-.. include:: rrt/rrt.rst
-.. include:: cubic_spline/cubic_spline.rst
-.. include:: bspline_path/bspline_path.rst
-.. include:: clothoid_path/clothoid_path.rst
-.. include:: eta3_spline/eta3_spline.rst
-.. include:: bezier_path/bezier_path.rst
-.. include:: quintic_polynomials_planner/quintic_polynomials_planner.rst
-.. include:: dubins_path/dubins_path.rst
-.. include:: reeds_shepp_path/reeds_shepp_path.rst
-.. include:: lqr_path/lqr_path.rst
-.. include:: hybridastar/hybridastar.rst
-.. include:: frenet_frame_path/frenet_frame_path.rst
-.. include:: coverage_path/coverage_path.rst
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
+
+   dynamic_window_approach/dynamic_window_approach
+   bugplanner/bugplanner
+   grid_base_search/grid_base_search
+   model_predictive_trajectory_generator/model_predictive_trajectory_generator
+   state_lattice_planner/state_lattice_planner
+   prm_planner/prm_planner
+   visibility_road_map_planner/visibility_road_map_planner
+   vrm_planner/vrm_planner
+   rrt/rrt
+   cubic_spline/cubic_spline
+   bspline_path/bspline_path
+   clothoid_path/clothoid_path
+   eta3_spline/eta3_spline
+   bezier_path/bezier_path
+   quintic_polynomials_planner/quintic_polynomials_planner
+   dubins_path/dubins_path
+   reeds_shepp_path/reeds_shepp_path
+   lqr_path/lqr_path
+   hybridastar/hybridastar
+   frenet_frame_path/frenet_frame_path
+   coverage_path/coverage_path

docs/modules/path_planning/rrt/rrt_main.rst

@@ -14,7 +14,7 @@ This is a simple path planning code with Rapidly-Exploring Random Trees
 Black circles are obstacles, green line is a searched tree, red crosses
 are start and goal positions.
 
-.. include:: rrt/rrt_star.rst
+.. include:: rrt_star.rst
 
 
 RRT with dubins path

docs/modules/path_planning/rrt/rrt_star.rst

@@ -10,7 +10,7 @@ Black circles are obstacles, green line is a searched tree, red crosses are star
 Simulation
 ^^^^^^^^^^
 
-.. image:: rrt/rrt_star/rrt_star_1_0.png
+.. image:: rrt_star/rrt_star_1_0.png
    :width: 600px
 
 

docs/modules/path_planning/visibility_road_map_planner/visibility_road_map_planner_main.rst

@@ -24,7 +24,7 @@ We assume this planner can be provided these information in the below figure.
 - 2. Goal point (Blue point)
 - 3. Obstacle polygons (Black lines)
 
-.. image:: visibility_road_map_planner/step0.png
+.. image:: step0.png
    :width: 400px
 
 
@@ -33,7 +33,7 @@ Step1: Generate visibility nodes based on polygon obstacles
 
 The nodes are generated by expanded these polygons vertexes like the below figure:
 
-.. image:: visibility_road_map_planner/step1.png
+.. image:: step1.png
    :width: 400px
 
 Each polygon vertex is expanded outward from the vector of adjacent vertices.
@@ -49,7 +49,7 @@ If the arc is collided, the graph is removed.
 
 The blue lines are generated visibility graphs in the figure:
 
-.. image:: visibility_road_map_planner/step2.png
+.. image:: step2.png
    :width: 400px
 
 
@@ -58,7 +58,7 @@ Step3: Search the shortest path in the graphs using Dijkstra algorithm
 
 The red line is searched path in the figure:
 
-.. image:: visibility_road_map_planner/step3.png
+.. image:: step3.png
    :width: 400px
 
 You can find the details of Dijkstra algorithm in :ref:`dijkstra`.

docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst

@@ -2,16 +2,16 @@
 Nonlinear Model Predictive Control with C-GMRES
 -----------------------------------------------
 
-.. image:: cgmres_nmpc/cgmres_nmpc_1_0.png
+.. image:: cgmres_nmpc_1_0.png
    :width: 600px
 
-.. image:: cgmres_nmpc/cgmres_nmpc_2_0.png
+.. image:: cgmres_nmpc_2_0.png
    :width: 600px
 
-.. image:: cgmres_nmpc/cgmres_nmpc_3_0.png
+.. image:: cgmres_nmpc_3_0.png
    :width: 600px
 
-.. image:: cgmres_nmpc/cgmres_nmpc_4_0.png
+.. image:: cgmres_nmpc_4_0.png
    :width: 600px
 
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/cgmres_nmpc/animation.gif

docs/modules/path_tracking/path_tracking_main.rst

@@ -3,11 +3,14 @@
 Path Tracking
 =============
 
-.. include:: pure_pursuit_tracking/pure_pursuit_tracking.rst
-.. include:: stanley_control/stanley_control.rst
-.. include:: rear_wheel_feedback_control/rear_wheel_feedback_control.rst
-.. include:: lqr_steering_control/lqr_steering_control.rst
-.. include:: lqr_speed_and_steering_control/lqr_speed_and_steering_control.rst
-.. include:: model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.rst
-.. include:: cgmres_nmpc/cgmres_nmpc.rst
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
 
+   pure_pursuit_tracking/pure_pursuit_tracking
+   stanley_control/stanley_control
+   rear_wheel_feedback_control/rear_wheel_feedback_control
+   lqr_steering_control/lqr_steering_control
+   lqr_speed_and_steering_control/lqr_speed_and_steering_control
+   model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control
+   cgmres_nmpc/cgmres_nmpc

docs/modules/slam/FastSLAM1/FastSLAM1_main.rst

@@ -11,7 +11,7 @@ Simulation
 
 This is a feature based SLAM example using FastSLAM 1.0.
 
-.. image:: FastSLAM1/FastSLAM1_1_0.png
+.. image:: FastSLAM1_1_0.png
    :width: 600px
 
 The blue line is ground truth, the black line is dead reckoning, the red
@@ -46,8 +46,8 @@ an array of landmark locations
 I.e. Each particle maintains a deterministic pose and n-EKFs for each
 landmark and update it with each measurement.
 
-Algorithm walkthrough
-~~~~~~~~~~~~~~~~~~~~~
+Algorithm walk through
+~~~~~~~~~~~~~~~~~~~~~~~
 
 The particles are initially drawn from a uniform distribution the
 represent the initial uncertainty. At each time step we do:
@@ -75,7 +75,7 @@ which are the linear and angular velocity repsectively.
 
 :math:`\begin{equation*} \begin{bmatrix} x_{t+1} \\ y_{t+1} \\ \theta_{t+1} \end{bmatrix}= \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix}\begin{bmatrix} x_{t} \\ y_{t} \\ \theta_{t} \end{bmatrix}+ \begin{bmatrix} \Delta t cos(\theta) & 0\\ \Delta t sin(\theta) & 0\\ 0 & \Delta t \end{bmatrix} \begin{bmatrix} v_{t} + \sigma_v\\ w_{t} + \sigma_w\\ \end{bmatrix} \end{equation*}`
 
-The following snippets playsback the recorded trajectory of each
+The following snippets playback the recorded trajectory of each
 particle.
 
 To get the insight of the motion model change the value of :math:`R` and
@@ -527,8 +527,8 @@ indices
 
 
 
-.. image:: FastSLAM1/FastSLAM1_12_0.png
-.. image:: FastSLAM1/FastSLAM1_12_1.png
+.. image:: FastSLAM1_12_0.png
+.. image:: FastSLAM1_12_1.png
 
 
 References

docs/modules/slam/ekf_slam/ekf_slam_main.rst

@@ -581,7 +581,7 @@ reckoning and control functions are passed along here as well.
     New LM
     New LM
 
-.. image:: ekf_slam/ekf_slam_1_0.png
+.. image:: ekf_slam_1_0.png
 
 References:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/modules/slam/graph_slam/graphSLAM_SE2_example.rst

@@ -44,7 +44,7 @@ The Dataset
 
 
 
-.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png
+.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png
 
 
 Each edge in this dataset is a constraint that compares the measured
@@ -122,7 +122,7 @@ dataset and plot them.
 
 
 
-.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png
+.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png
 
 
 .. code:: ipython3
@@ -131,7 +131,7 @@ dataset and plot them.
 
 
 
-.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png
+.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png
 
 
 Optimization
@@ -165,7 +165,7 @@ different data sources into a single optimization problem.
             6             215.8405          -0.000000
 
 
-.. figure:: graph_slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif
+.. figure:: graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif
 
 .. code:: ipython3
 
@@ -173,7 +173,7 @@ different data sources into a single optimization problem.
 
 
 
-.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png
+.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png
 
 
 .. code:: ipython3
@@ -195,7 +195,7 @@ different data sources into a single optimization problem.
 
 
 
-.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png
+.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png
 
 
 .. code:: ipython3
@@ -204,5 +204,5 @@ different data sources into a single optimization problem.
 
 
 
-.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png
+.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png
 

docs/modules/slam/graph_slam/graphSLAM_doc.rst

@@ -142,7 +142,7 @@ created based on the information of the motion and the observation.
 
 
 
-.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_2_0.png
+.. image:: graphSLAM_doc_files/graphSLAM_doc_2_0.png
 
 
 .. parsed-literal::
@@ -157,7 +157,7 @@ created based on the information of the motion and the observation.
 
 
 
-.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_2_2.png
+.. image:: graphSLAM_doc_files/graphSLAM_doc_2_2.png
 
 
 In particular, the tasks are split into 2 parts, graph construction, and
@@ -289,7 +289,7 @@ robot with 3DoF, namely, :math:`[x, y, \theta]^T`
 
 
 
-.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_4_0.png
+.. image:: graphSLAM_doc_files/graphSLAM_doc_4_0.png
 
 
 .. code:: ipython3
@@ -420,7 +420,7 @@ zero since :math:`x_j + d_j cos(\psi_j + \theta_j)` should equal
 
 
 
-.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_9_1.png
+.. image:: graphSLAM_doc_files/graphSLAM_doc_9_1.png
 
 
 Since the constraints equations derived before are non-linear,
@@ -494,9 +494,9 @@ Similarly, :math:`B = \frac{\partial e_{ij}}{\partial \boldsymbol{x}_j}`
 
 
 
-.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_11_1.png
+.. image:: graphSLAM_doc_files/graphSLAM_doc_11_1.png
 
-.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_11_2.png
+.. image:: graphSLAM_doc_files/graphSLAM_doc_11_2.png
 
 .. code:: ipython3
 

docs/modules/slam/graph_slam/graphSLAM_formulation.rst

@@ -70,7 +70,7 @@ Using Bayes’ rule, we can write this probability as
 
 since :math:`p(\mathcal{Z})` is a constant (albeit, an unknown constant)
 and we assume that :math:`p(\mathbf{p}_1, \ldots, \mathbf{p}_N)` is
-uniformly distributed `PROBABILISTIC ROBOTICS`_. Therefore, we
+uniformly distributed. Therefore, we
 can use Eq. :eq:`infomat` and and Eq. :eq:`bayes` to simplify the Graph SLAM
 optimization as follows:
 

docs/modules/slam/graph_slam/graph_slam_main.rst

@@ -13,9 +13,9 @@ The black stars are landmarks for graph edge generation.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/GraphBasedSLAM/animation.gif
 
-.. include:: graph_slam/graphSLAM_doc.rst
-.. include:: graph_slam/graphSLAM_formulation.rst
-.. include:: graph_slam/graphSLAM_SE2_example.rst
+.. include:: graphSLAM_doc.rst
+.. include:: graphSLAM_formulation.rst
+.. include:: graphSLAM_SE2_example.rst
 
 References:
 ~~~~~~~~~~~

docs/modules/slam/slam_main.rst

@@ -5,9 +5,12 @@ SLAM
 
 Simultaneous Localization and Mapping(SLAM) examples
 
-.. include:: iterative_closest_point_matching/iterative_closest_point_matching.rst
-.. include:: ekf_slam/ekf_slam.rst
-.. include:: FastSLAM1/FastSLAM1.rst
-.. include:: FastSLAM2/FastSLAM2.rst
-.. include:: graph_slam/graph_slam.rst
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
 
+   iterative_closest_point_matching/iterative_closest_point_matching
+   ekf_slam/ekf_slam
+   FastSLAM1/FastSLAM1
+   FastSLAM2/FastSLAM2
+   graph_slam/graph_slam