Update setup_odom tutorials, and add pages for other tutorials

Signed-off-by: Aarav Gupta <[email protected]>

setup_guides/index.rst

@@ -13,15 +13,14 @@ This section is a collection of guides that aims to provide readers a good resou
 
 To guide you through the first-time setup of your robot, we will be tackling the following topics:
 
-- Introduce TF2 and setup your robot URDF
+- Introduce TF2 and setup your robot URDF or SDF
 - Setup sensor sources for robot odometry
 - Setup sensor sources for perception
 - Configure round or arbitrary shaped footprints for your robot
 - Select and set up planner and controller navigation plugins for your robot's navigation tasks  
 - Lifecycle node management for easy bringup of other related sensors or nodes
 
-The simulator of record in these tutorials is Gazebo Classic, which is the default simulator in ROS 2 Humble, Iron and all previous distributions.
-When using Jazzy or newer, consider that the simulation elements of the tutorial may be out of date and are worth looking at other resources like `nav2_minimal_turtlebot_simulation <https://github.com/ros-navigation/nav2_minimal_turtlebot_simulation>`_ which fully leverages modern Gazebo with Nav2.
+There are tutorials below for the Gazebo Classic simulator and the Gazebo simulator. Gazebo Classic is used with ROS2 Humble and all previous ROS distributions, while Gazebo (Gazebo Harmonic or newer) is used with ROS2 Jazzy or newer.
 
 **Table of Contents:**
 
@@ -30,8 +29,12 @@ When using Jazzy or newer, consider that the simulation elements of the tutorial
 
    transformation/setup_transforms.rst
    urdf/setup_urdf.rst
-   odom/setup_odom.rst
-   sensors/setup_sensors.rst
+   sdf/setup_sdf.rst
+   odom/setup_odom_gz.rst
+   odom/setup_odom_gz_classic.rst
+   odom/setup_robot_localization.rst
+   sensors/setup_sensors_gz.rst
+   sensors/setup_sensors_gz_classic.rst
    footprint/setup_footprint.rst
    algorithm/select_algorithm.rst
 

setup_guides/odom/setup_robot_localization.rst

@@ -0,0 +1,227 @@
+Smoothing Odometry using Robot Localization
+###########################################
+
+In this tutorial, we will discuss how various sources of odometry can be fused to provide a smoothed odometry using the ``robot_localization`` package and we will also show how to publish the ``odom`` => ``base_link`` transform using ``robot_localization``.
+
+
+The ``robot_localization`` package is used to provide a fused and locally accurate smooth odometry information from the data provided by ``N`` odometry sensor inputs. These information can be provided to the package through ``nav_msgs/Odometry``, ``sensor_msgs/Imu``, ``geometry_msgs/PoseWithCovarianceStamped``, and ``geometry_msgs/TwistWithCovarianceStamped`` messages.
+
+A usual robot setup consists of at least the wheel encoders and IMU as its odometry sensor sources. When multiple sources are provided to ``robot_localization``, it is able to fuse the odometry information given by the sensors through the use of state estimation nodes. These nodes make use of either an Extended Kalman filter (``ekf_node``) or an Unscented Kalman Filter (``ukf_node``) to implement this fusion. In addition, the package also implements a ``navsat_transform_node`` which transforms geographic coordinates into the robot’s world frame when working with GPS.
+
+Fused sensor data is published by the ``robot_localization`` package through the ``odometry/filtered`` and the ``accel/filtered`` topics, if enabled in its configuration. In addition, it can also publish the ``odom`` => ``base_link`` transform on the ``/tf`` topic. Do note that if you followed :ref:`setup_odom_gz`, you will need to remove the /tf bridge in ``bridge_config.yaml`` to get the transforms from ``ekf_node`` instead of Gazebo.
+
+.. seealso::
+  More details on ``robot_localization`` can be found in the official `Robot Localization Documentation <http://docs.ros.org/en/noetic/api/robot_localization/html/index.html>`_.
+
+If your robot is only able to provide one odometry source, the use of ``robot_localization`` would have minimal effects aside from smoothing. In this case, an alternative approach is to publish transforms through a tf2 broadcaster in your single source of odometry node. Nevertheless, you can still opt to use ``robot_localization`` to publish the transforms and some smoothing properties may still be observed in the output.
+
+.. seealso::
+  For more information on how to write a tf2 broadcaster, you can check Writing a tf2 broadcaster `(C++)  <https://docs.ros.org/en/rolling/Tutorials/Tf2/Writing-A-Tf2-Broadcaster-Cpp.html>`_  `(Python)  <https://docs.ros.org/en/rolling/Tutorials/Tf2/Writing-A-Tf2-Broadcaster-Py.html>`_.
+
+For the rest of this section, we will show how to use ``robot_localization`` to fuse the sensors of ``sam_bot``. It will use the ``sensor_msgs/Imu`` messages published on ``/demo/Imu`` and the ``nav_msgs/Odometry`` message published on ``/demo/odom`` and then it will publish data on ``odometry/filtered``,  ``accel/filtered``, and ``/tf`` topics.
+
+Configuring Robot Localization
+==============================
+
+Let us now configure the ``robot_localization`` package to use an Extended Kalman Filter (``ekf_node``) to fuse odometry information and publish the ``odom`` => ``base_link`` transform.
+
+First, install the ``robot_localization`` package using your machines package manager or by executing the following command:
+
+.. code-block:: shell
+
+  sudo apt install ros-<ros2-distro>-robot-localization
+
+Next, we specify the parameters of the ``ekf_node`` using a YAML file. Create a directory named ``config`` at the root of your project and create a file named ``ekf.yaml``. Copy the following lines of code into your ``ekf.yaml`` file.
+
+.. code-block:: yaml
+
+  ### ekf config file ###
+  ekf_filter_node:
+      ros__parameters:
+  # The frequency, in Hz, at which the filter will output a position estimate. Note that the filter will not begin
+  # computation until it receives at least one message from one of theinputs. It will then run continuously at the
+  # frequency specified here, regardless of whether it receives more measurements. Defaults to 30 if unspecified.
+          frequency: 30.0
+
+  # ekf_localization_node and ukf_localization_node both use a 3D omnidirectional motion model. If this parameter is
+  # set to true, no 3D information will be used in your state estimate. Use this if you are operating in a planar
+  # environment and want to ignore the effect of small variations in the ground plane that might otherwise be detected
+  # by, for example, an IMU. Defaults to false if unspecified.
+          two_d_mode: false
+
+  # Whether to publish the acceleration state. Defaults to false if unspecified.
+          publish_acceleration: true
+
+  # Whether to broadcast the transformation over the /tf topic. Defaultsto true if unspecified.
+          publish_tf: true
+
+  # 1. Set the map_frame, odom_frame, and base_link frames to the appropriate frame names for your system.
+  #     1a. If your system does not have a map_frame, just remove it, and make sure "world_frame" is set to the value of odom_frame.
+  # 2. If you are fusing continuous position data such as wheel encoder odometry, visual odometry, or IMU data, set "world_frame"
+  #    to your odom_frame value. This is the default behavior for robot_localization's state estimation nodes.
+  # 3. If you are fusing global absolute position data that is subject to discrete jumps (e.g., GPS or position updates from landmark
+  #    observations) then:
+  #     3a. Set your "world_frame" to your map_frame value
+  #     3b. MAKE SURE something else is generating the odom->base_link transform. Note that this can even be another state estimation node
+  #         from robot_localization! However, that instance should *not* fuse the global data.
+          map_frame: map              # Defaults to "map" if unspecified
+          odom_frame: odom            # Defaults to "odom" if unspecified
+          base_link_frame: base_link  # Defaults to "base_link" ifunspecified
+          world_frame: odom           # Defaults to the value ofodom_frame if unspecified
+
+          odom0: demo/odom
+          odom0_config: [true,  true,  true,
+                         false, false, false,
+                         false, false, false,
+                         false, false, true,
+                         false, false, false]
+
+          imu0: demo/imu
+          imu0_config: [false, false, false,
+                        true,  true,  true,
+                        false, false, false,
+                        false, false, false,
+                        false, false, false]
+
+In this configuration, we defined the parameter values of ``frequency``, ``two_d_mode``, ``publish_acceleration``, ``publish_tf``, ``map_frame``, ``odom_frame``, ``base_link_frame``, and ``world_frame``. For more information on the other parameters you can modify, see `Parameters of state estimation nodes <http://docs.ros.org/en/melodic/api/robot_localization/html/state_estimation_nodes.html#parameters>`_, and a sample ``efk.yaml`` can be found `here <https://github.com/cra-ros-pkg/robot_localization/blob/foxy-devel/params/ekf.yaml>`_.
+
+To add a sensor input to the ``ekf_filter_node``, add the next number in the sequence to its base name (odom, imu, pose, twist). In our case, we have one ``nav_msgs/Odometry`` and one ``sensor_msgs/Imu`` as inputs to the filter, thus we use ``odom0`` and ``imu0``. We set the value of ``odom0`` to ``demo/odom``, which is the topic that publishes the ``nav_msgs/Odometry``. Similarly, we set the value of ``imu0`` to the topic that publishes ``sensor_msgs/Imu``, which is ``demo/imu``.
+
+You can specify which values from a sensor are to be used by the filter using the ``_config`` parameter. The order of the values of this parameter is x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. In our example, we set everything in ``odom0_config`` to ``false`` except the 1st, 2nd, 3rd, and 12th entries, which means the filter will only use the x, y, z, and the vyaw values of ``odom0``.
+
+In the ``imu0_config`` matrix, you'll notice that only roll, pitch, and yaw are used. Typical mobile robot-grade IMUs will also provide angular velocities and linear accelerations. For ``robot_localization`` to work properly, you should not fuse in multiple fields that are derivative of each other. Since angular velocity is fused internally to the IMU to provide the roll, pitch and yaw estimates, we should not fuse in the angular velocities used to derive that information. We also do not fuse in angular velocity due to the noisy characteristics it has when not using exceptionally high quality (and expensive) IMUs.
+
+.. seealso::
+  For more advise on configuration of input data to ``robot_localization``, see `Preparing Your Data for Use with robot_localization <http://docs.ros.org/en/melodic/api/robot_localization/html/preparing_sensor_data.html#odometry>`_, and `Configuring robot_localization <http://docs.ros.org/en/melodic/api/robot_localization/html/configuring_robot_localization.html>`_.
+
+
+Launch and Build Files
+======================
+
+Now, let us add the ``ekf_node`` into the launch file. Open ``launch/display.launch.py`` and paste the following lines before the ``return launch.LaunchDescription([`` line.
+
+.. code-block:: shell
+
+  robot_localization_node = Node(
+      package='robot_localization',
+      executable='ekf_node',
+      name='ekf_filter_node',
+      output='screen',
+      parameters=[os.path.join(pkg_share, 'config/ekf.yaml'), {'use_sim_time': LaunchConfiguration('use_sim_time')}]
+  )
+
+Next, add the following launch arguments within the ``return launch.LaunchDescription([`` block.
+
+.. code-block:: shell
+
+  launch.actions.DeclareLaunchArgument(name='use_sim_time', default_value='True',
+                                              description='Flag to enable use_sim_time'),
+
+Lastly, add ``robot_localization_node,`` above the ``rviz_node`` line to launch the robot localization node.
+
+.. code-block:: shell
+
+        robot_state_publisher_node,
+        spawn_entity,
+        robot_localization_node,
+        rviz_node
+  ])
+
+Next, we need to add the ``robot_localization`` dependency to our package definition. Open ``package.xml`` and add the following line below the last ``<exec_depend>`` tag.
+
+.. code-block:: shell
+
+   <exec_depend>robot_localization</exec_depend>
+
+Lastly, open ``CMakeLists.txt`` and append the ``config`` directory inside the ``install(DIRECTORY...)``, as shown in the snippet below.
+
+.. code-block:: shell
+
+  install(
+    DIRECTORY src launch rviz config
+    DESTINATION share/${PROJECT_NAME}
+  )
+
+
+Build, Run and Verification
+===========================
+
+Let us now build and run our package. Navigate to the root of the project and execute the following lines:
+
+.. code-block:: shell
+
+  colcon build
+  . install/setup.bash
+  ros2 launch sam_bot_description display.launch.py
+
+Gazebo and RVIZ should launch. In the RVIZ window, you should see the model and TF frames of ``sam_bot``:
+
+.. image:: images/rviz.png
+    :width: 100%
+    :align: center
+
+Next, let us verify that the ``odometry/filtered``,  ``accel/filtered``, and ``/tf`` topics are active in the system. Open a new terminal and execute:
+
+.. code-block:: shell
+
+  ros2 topic list
+
+You should see ``odometry/filtered``, ``accel/filtered``, and ``/tf`` in the list of the topics.
+
+You can also check the subscriber count of these topics again by executing:
+
+.. code-block:: shell
+
+  ros2 topic info /demo/imu
+  ros2 topic info /demo/odom
+
+You should see that ``/demo/imu`` and ``/demo/odom`` now both have 1 subscriber each.
+
+To verify that the ``ekf_filter_node`` are the subscribers of these topics, execute:
+
+.. code-block:: shell
+
+  ros2 node info /ekf_filter_node
+
+You should see an output as shown below.
+
+.. code-block:: shell
+
+  /ekf_filter_node
+  Subscribers:
+    /demo/imu: sensor_msgs/msg/Imu
+    /demo/odom: nav_msgs/msg/Odometry
+    /parameter_events: rcl_interfaces/msg/ParameterEvent
+    /set_pose: geometry_msgs/msg/PoseWithCovarianceStamped
+  Publishers:
+    /accel/filtered: geometry_msgs/msg/AccelWithCovarianceStamped
+    /diagnostics: diagnostic_msgs/msg/DiagnosticArray
+    /odometry/filtered: nav_msgs/msg/Odometry
+    /parameter_events: rcl_interfaces/msg/ParameterEvent
+    /rosout: rcl_interfaces/msg/Log
+    /tf: tf2_msgs/msg/TFMessage
+  Service Servers:
+     ...
+
+From the output above, we can see that the ``ekf_filter_node`` is subscribed to ``/demo/imu`` and ``/demo/odom``. We can also see that the ``ekf_filter_node`` publishes on the ``odometry/filtered``, ``accel/filtered``, and ``/tf`` topics.
+
+You may also verify that ``robot_localization`` is publishing the ``odom`` => ``base_link`` transform by using the tf2_echo utility. Run the following command in a separate command line terminal:
+
+.. code-block:: shell
+
+  ros2 run tf2_ros tf2_echo odom base_link
+
+You should see a continuous output similar to what is shown below.
+
+.. code-block:: shell
+
+  At time 8.842000000
+  - Translation: [0.003, -0.000, 0.127]
+  - Rotation: in Quaternion [-0.000, 0.092, 0.003, 0.996]
+  At time 9.842000000
+  - Translation: [0.002, -0.000, 0.127]
+  - Rotation: in Quaternion [-0.000, 0.092, 0.003, 0.996]
+
+Conclusion
+**********
+In this guide we have discussed how multiple odometry sensors can be used to provide a filtered and smoothed odometry using ``robot_localization``. We have also checked if the ``odom`` => ``base_link`` transform is being published correctly by ``robot_localization``.

setup_guides/sdf/setup_sdf.rst

@@ -0,0 +1,4 @@
+.. _sdf_handson:
+
+Setting Up The SDF
+#######################

setup_guides/sensors/setup_sensors_gz.rst

@@ -0,0 +1,4 @@
+.. _setup_sensors_gz:
+
+Setting Up Sensors - Gazebo
+###########################

setup_guides/sensors/setup_sensors.rst → setup_guides/sensors/setup_sensors_gz_classic.rst

@@ -1,7 +1,7 @@
-.. _setup_sensors:
+.. _setup_sensors_gz_classic:
 
-Setting Up Sensors
-##################
+Setting Up Sensors - Gazebo Classic
+###################################
 
 In this guide, we will discuss the importance of the sensors in navigating a robot safely and how to set up the sensors with Nav2. In the first half of this tutorial, we will take a brief look at commonly used sensors and common sensor messages in Nav2. Next, we will add a basic sensor setup on our previously built simulated robot, ``sam_bot``. Lastly, we will then verify the simulated sensor messages of ``sam_bot`` by visualizing them in RViz.