mirror of
https://github.com/introlab/rtabmap_ros.git
synced 2026-10-04 00:37:46 +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
116 lines
5.0 KiB
Python
116 lines
5.0 KiB
Python
# Requirements:
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# Download one or more rosbags:
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# * map1.db3: https://drive.google.com/file/d/1XajzWm0u1Tk7m7x63ybcKVMXj80r5P6r/view?usp=drive_link
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# * map2.db3: https://drive.google.com/file/d/1_FxEalE2O-DQKq2tRpLIpDn5Mbvu0jZc/view?usp=drive_link
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# * map3.db3: https://drive.google.com/file/d/1dJzMOoRPA28gQZUIWCeAa08Qn4wG9oRw/view?usp=drive_link
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# * map4.db3: https://drive.google.com/file/d/19Y6yye0ndIIwdhEWMwTdoiSy9WlKS44c/view?usp=drive_link
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# * map5.db3: https://drive.google.com/file/d/1zCx4Q4SftPplQtW1xeG-W3OkbTxwd5GD/view?usp=drive_link
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#
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# Example:
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#
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# SLAM:
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# $ rm ~/.ros/rtabmap.db
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# $ ros2 launch rtabmap_demos multisession_mapping_demo.launch.py
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#
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# Rosbag:
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# $ ros2 bag play map1.db3 --clock
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# when done, you can play the next bag(s):
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# $ ros2 bag play map2.db3 --clock
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# $ ros2 bag play map3.db3 --clock
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# $ ros2 bag play map4.db3 --clock
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# $ ros2 bag play map5.db3 --clock
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#
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# Refer to this paper for more info: https://arxiv.org/abs/2407.15305
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#
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from launch import LaunchDescription
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from launch.actions import DeclareLaunchArgument
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from launch.substitutions import LaunchConfiguration
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from launch.conditions import IfCondition
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from launch_ros.actions import Node
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from launch_ros.actions import SetParameter
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import os
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from ament_index_python.packages import get_package_share_directory
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def generate_launch_description():
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parameters={
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'frame_id':'base_footprint',
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'odom_frame_id':'odom',
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'odom_tf_linear_variance':0.001,
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'odom_tf_angular_variance':0.001,
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'subscribe_rgbd':True,
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'subscribe_scan':True,
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'approx_sync':True,
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'sync_queue_size': 10,
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# RTAB-Map's internal parameters should be strings
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'RGBD/NeighborLinkRefining': 'false',
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'RGBD/ProximityBySpace': 'false', # Referred paper did only global loop closure detection
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'RGBD/OptimizeFromGraphEnd': 'true',
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'Reg/Strategy': '1',
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'Icp/Iterations': '30',
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'Icp/VoxelSize': '0',
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'Vis/MinInliers': '12',
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'Vis/MaxDepth': '0',
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'RGBD/AngularUpdate': '0.01',
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'RGBD/LinearUpdate': '0.01',
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'Rtabmap/TimeThr': '700',
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'Mem/RehearsalSimilarity': '0.30', # Referred paper used 0.45 with SURF, here with SIFT, we will use 0.3
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'Kp/TfIdfLikelihoodUsed': 'false',
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'Bayes/FullPredictionUpdate': 'true',
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'Kp/DetectorStrategy': '1', # Referred paper used SURF (0), here use SIFT as it is available with opencv binaries
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'Vis/FeatureType': '1', # Referred paper used SURF (0), here use SIFT as it is available with opencv binaries
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'Kp/MaxFeatures': '400',
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'Reg/Force3DoF': 'true',
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'RGBD/OptimizeMaxError': '10',
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'Optimizer/Strategy': '2', # Referred paper used TORO (0), latest version recommends GTSAM (2)
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'Optimizer/Iterations': '100',
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'Kp/IncrementalFlann': 'false', # Referred paper didn't use incremental FLANN
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'Icp/MaxTranslation': '0.5',
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}
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remappings=[
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('rgb/image', '/data_throttled_image'),
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('depth/image', '/data_throttled_image_depth'),
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('rgb/camera_info', '/data_throttled_camera_info'),
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('scan', '/base_scan')]
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config_rviz = os.path.join(
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get_package_share_directory('rtabmap_demos'), 'config', 'demo_robot_mapping.rviz'
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)
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return LaunchDescription([
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# Launch arguments
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DeclareLaunchArgument('rtabmap_viz', default_value='true', description='Launch RTAB-Map UI (optional).'),
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DeclareLaunchArgument('rviz', default_value='false', description='Launch RVIZ (optional).'),
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DeclareLaunchArgument('rviz_cfg', default_value=config_rviz, description='Configuration path of rviz2.'),
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SetParameter(name='use_sim_time', value=True),
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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=[parameters,
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{'rgb_image_transport':'compressed',
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'depth_image_transport':'compressedDepth',
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'approx_sync_max_interval': 0.02}],
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remappings=remappings),
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# SLAM node:
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Node(
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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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# Visualization:
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Node(
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package='rtabmap_viz', executable='rtabmap_viz', output='screen',
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condition=IfCondition(LaunchConfiguration("rtabmap_viz")),
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parameters=[parameters],
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remappings=remappings),
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Node(
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package='rviz2', executable='rviz2', name="rviz2", output='screen',
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condition=IfCondition(LaunchConfiguration("rviz")),
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arguments=[["-d"], [LaunchConfiguration("rviz_cfg")]]),
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])
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