Merge pull request #6 from LCAS/assessment_map

Map of the assessment task, vanilla and refined

src/limo_navigation/launch/ekf_localization.launch.py

@@ -0,0 +1,72 @@
+import os
+
+from ament_index_python.packages import get_package_share_directory
+
+from launch import LaunchDescription
+from launch.actions import DeclareLaunchArgument, IncludeLaunchDescription
+from launch.launch_description_sources import PythonLaunchDescriptionSource
+from launch.substitutions import LaunchConfiguration
+from launch_ros.actions import Node
+
+def generate_launch_description():
+    # Get the launch directory
+    launch_dir = os.path.join(
+        get_package_share_directory('limo_navigation'), 'launch')
+
+    # Create the launch configuration variables
+    # namespace = LaunchConfiguration('namespace')
+    use_sim_time = LaunchConfiguration('use_sim_time')
+    # autostart = LaunchConfiguration('autostart')
+
+    # lifecycle_nodes = [
+    #     'map_server',
+    #     'amcl',
+    #     'controller_server',
+    #     'planner_server',
+    #     'behavior_server',
+    #     'bt_navigator',
+    #     'waypoint_follower']
+
+    # declare_namespace = DeclareLaunchArgument(
+    #     'namespace', default_value='',
+    #     description='Top-level namespace')
+
+    declare_use_sim_time = DeclareLaunchArgument(
+        'use_sim_time', default_value='false',
+        description='Use simulation (Gazebo) clock if true')
+
+    # declare_autostart = DeclareLaunchArgument(
+    #     'autostart', default_value='true',
+    #     description='Automatically startup the nav2 stack')
+
+    robot_localization_node = Node(
+       package='robot_localization',
+       executable='ekf_node',
+       name='ekf_filter_node',
+       output='screen',
+       parameters=[os.path.join(get_package_share_directory('limo_navigation'), 'params/ekf.yaml'), {'use_sim_time': use_sim_time}]
+    )
+
+    # start_lifecycle_mgr = Node(
+    #     package='nav2_lifecycle_manager',
+    #     executable='lifecycle_manager',
+    #     name='lifecycle_manager_navigation',
+    #     output='screen',
+    #     parameters=[{'use_sim_time': use_sim_time},
+    #                 {'autostart': autostart},
+                    # {'node_names': lifecycle_nodes}])
+
+    # Create the launch description and populate
+    ld = LaunchDescription()
+
+    # Declare the launch options
+    ld.add_action(declare_use_sim_time)
+    # ld.add_action(declare_autostart)
+    # ld.add_action(declare_namespace)
+
+    # ld.add_action(launch_limo_localization)
+    ld.add_action(robot_localization_node)
+
+    # ld.add_action(start_lifecycle_mgr)
+
+    return ld

src/limo_navigation/maps/assessment_map.yaml

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+image: assessment_map.pgm
+mode: trinary
+resolution: 0.05
+origin: [-1.51, -0.758, 0]
+negate: 0
+occupied_thresh: 0.65
+free_thresh: 0.25

src/limo_navigation/params/ekf.yaml

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+### 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: true
+
+# Whether to publish the acceleration state. Defaults to false if unspecified.
+        publish_acceleration: false
+
+# Whether to broadcast the transformation over the /tf topic. Defaults to true if unspecified.
+        publish_tf: false
+
+# 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 of odom_frame if unspecified
+
+        odom0: /odom
+        odom0_config: [true,  true,  true,
+                       false, false, false,
+                       false, false, false,
+                       false, false, true,
+                       false, false, false]
+
+        imu0: /imu
+        imu0_config: [false, false, false,
+                      true,  true,  true,
+                      false, false, false,
+                      false, false, false,
+                      false, false, false]