mirror of
https://github.com/introlab/rtabmap_ros.git
synced 2026-10-04 08:47:45 +08:00
matlabbe
3f6adad463
* Updating examples and README * updated main readme * odom: Added always_check_imu_tf parameter. sync: increased default sync_queue_size from 2 to 5 (to be more flexible to different hardware), added input and output diagnostics. rtabmap_viz: fixed node with same name redeclared warning. * update readme * Added husky demo * converted demo_robot_mapping.launch to ros2 * Converted stereo_outdoor demo from ros1 to ros2 * Converted multi-session demo to ros2 * Converted demo_find_object launch to ros2 * renamed files * uniformized default qos, increased sync_queue_size default to 10 * Added vlp16 + imu example * Added husky 3D lidar demos * moved demos in subdir * Added vlp16+zed example * rtabmap_viz: ignore odom update if tf not ready when not using topic (to avoid showing red screen). Added champ VSLAM demo. * forwarded camera_model arg for zed examples, removed qos for turtlebot4 demo * pointcloud_to_depthimage: added more error logs, removed output camera_info published twice and match ros1 namespaces * Created two base general examples for 3D lidar usage, then create hardware specific examples on top of them. * Added isaac sim demo * Final update of all demos. Fixed MapCloud rviz plugin crashing when floor/ceiling filtering is used and resulting cloud is empty. * Fixed 2d scan deskewing output frame, moved nav2 params under "params" folder, added number of topics processed/dropped for odometry, added icp_odometry option for turtlebot3 scan-only demo. * rtabmap_demos: added README with examples * Added TOC * bump version to 0.21.9
113 lines
4.3 KiB
Python
113 lines
4.3 KiB
Python
# Example:
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#
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# Bringup turtlebot3:
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# $ export TURTLEBOT3_MODEL=waffle
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# $ export LDS_MODEL=LDS-01
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# $ ros2 launch turtlebot3_bringup robot.launch.py
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#
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# SLAM:
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# $ ros2 launch rtabmap_demos turtlebot3_rgbd_scan.launch.py
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#
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# Navigation (install nav2_bringup package):
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# $ ros2 launch nav2_bringup navigation_launch.py
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# $ ros2 launch nav2_bringup rviz_launch.py
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#
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# Teleop:
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# $ ros2 run turtlebot3_teleop teleop_keyboard
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from launch import LaunchDescription
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from launch.actions import DeclareLaunchArgument, SetEnvironmentVariable
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from launch.substitutions import LaunchConfiguration
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from launch.conditions import IfCondition, UnlessCondition
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from launch_ros.actions import Node
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def generate_launch_description():
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use_sim_time = LaunchConfiguration('use_sim_time')
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localization = LaunchConfiguration('localization')
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parameters={
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'frame_id':'base_footprint',
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'use_sim_time':use_sim_time,
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'subscribe_rgbd':True,
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'subscribe_scan':True,
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'use_action_for_goal':True,
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# RTAB-Map's parameters should be strings:
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'Reg/Strategy':'1',
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'Reg/Force3DoF':'true',
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'RGBD/NeighborLinkRefining':'True',
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'Grid/RayTracing':'true', # Fill empty space
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'Grid/3D':'false', # Use 2D occupancy
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'Grid/RangeMax':'3',
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'Grid/NormalsSegmentation':'false', # Use passthrough filter to detect obstacles
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'Grid/Sensor':'2', # Use both laser scan and camera for obstacle detection in global map
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'Grid/MaxGroundHeight':'0.05', # All points above 5 cm are obstacles
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'Grid/MaxObstacleHeight':'0.4', # All points over 1 meter are ignored
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'Grid/RangeMin':'0.2', # ignore laser scan points on the robot itself
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'Optimizer/GravitySigma':'0' # Disable imu constraints (we are already in 2D)
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}
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remappings=[
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('rgb/image', '/camera/image_raw'),
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('rgb/camera_info', '/camera/camera_info'),
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('depth/image', '/camera/depth/image_raw')]
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return LaunchDescription([
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# Launch arguments
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DeclareLaunchArgument(
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'use_sim_time', default_value='false',
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description='Use simulation (Gazebo) clock if true'),
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DeclareLaunchArgument(
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'localization', default_value='false',
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description='Launch in localization mode.'),
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# Nodes to launch
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Node(
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package='rtabmap_sync', executable='rgbd_sync', output='screen',
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parameters=[{'approx_sync':False, 'use_sim_time':use_sim_time}],
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remappings=remappings),
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# SLAM Mode:
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Node(
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condition=UnlessCondition(localization),
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package='rtabmap_slam', executable='rtabmap', output='screen',
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parameters=[parameters],
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remappings=remappings,
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arguments=['-d']),
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# Localization mode:
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Node(
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condition=IfCondition(localization),
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package='rtabmap_slam', executable='rtabmap', output='screen',
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parameters=[parameters,
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{'Mem/IncrementalMemory':'False',
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'Mem/InitWMWithAllNodes':'True'}],
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remappings=remappings),
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Node(
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package='rtabmap_viz', executable='rtabmap_viz', output='screen',
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parameters=[parameters],
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remappings=remappings),
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# Obstacle detection with the camera for nav2 local costmap.
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# First, we need to convert depth image to a point cloud.
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# Second, we segment the floor from the obstacles.
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Node(
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package='rtabmap_util', executable='point_cloud_xyz', output='screen',
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parameters=[{'decimation': 2,
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'max_depth': 3.0,
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'voxel_size': 0.02}],
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remappings=[('depth/image', '/camera/depth/image_raw'),
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('depth/camera_info', '/camera/camera_info'),
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('cloud', '/camera/cloud')]),
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Node(
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package='rtabmap_util', executable='obstacles_detection', output='screen',
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parameters=[parameters],
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remappings=[('cloud', '/camera/cloud'),
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('obstacles', '/camera/obstacles'),
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('ground', '/camera/ground')]),
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])
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