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floam package from floam repo

floam

Package Summary

Tags No category tags.
Version 0.1.0
License BSD
Build type CATKIN
Use RECOMMENDED

Repository Summary

Checkout URI https://github.com/flynneva/floam.git
VCS Type git
VCS Version main
Last Updated 2021-08-23
Dev Status DEVELOPED
CI status No Continuous Integration
Released RELEASED
Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Package Description

A generic Lidar SLAM package based on FLOAM which was based on ALOAM by HKUST and LOAM by CMU

Additional Links

Maintainers

  • Evan Flynn

Authors

  • Han Wang

FLOAM

Fast LOAM (Lidar Odometry And Mapping)

This work is an optimized version of A-LOAM and LOAM with the computational cost reduced by up to 3 times. This code is modified from LOAM and A-LOAM .

Modifier: Wang Han, Nanyang Technological University, Singapore

Modifier: Evan Flynn

1. Demo Highlights

Watch our demo at Video Link

2. Evaluation

2.1. Computational efficiency evaluation

Computational efficiency evaluation (based on KITTI dataset): Platform: Intel® Core™ i7-8700 CPU @ 3.20GHz | Dataset | ALOAM | FLOAM | |----------------------------------------------|----------------------------|------------------------| | KITTI | 151ms | 59ms |

Localization error: | Dataset | ALOAM | FLOAM | |----------------------------------------------|----------------------------|------------------------| | KITTI sequence 00 | 0.55% | 0.51% | | KITTI sequence 02 | 3.93% | 1.25% | | KITTI sequence 05 | 1.28% | 0.93% |

2.2. localization result

2.3. mapping result

3. Prerequisites

3.1 Ubuntu and ROS

Ubuntu 64-bit 18.04 or 20.04

ROS Melodic or Noetic. ROS Installation

3.2. **Install dependencies using rosdep

Install dependencies using rosdep:

cd /your/catkin_ws
rosdep install --from-paths src --ignore-src -y

Or you can manually install the dependencies below:

3.3. Ceres Solver

Follow Ceres Installation.

3.4. PCL

Follow PCL Installation.

3.5. Trajectory visualization

For visualization purpose, this package uses hector trajectory sever, you may install the package by

sudo apt-get install ros-melodic-hector-trajectory-server

Alternatively, you may remove the hector trajectory server node if trajectory visualization is not needed

4. Build

Make sure to have installed the required dependencies as above before trying to build this package.

4.1 Clone repository:

    cd ~/catkin_ws/src
    git clone https://github.com/wh200720041/floam.git
    cd ..
    catkin_make
    source ~/catkin_ws/devel/setup.bash

4.2 Download test rosbag

Download KITTI sequence 05 or KITTI sequence 07

Unzip compressed file 2011_09_30_0018.zip. If your system does not have unzip. please install unzip by

sudo apt-get install unzip 

And this may take a few minutes to unzip the file

    cd ~/Downloads
    unzip ~/Downloads/2011_09_30_0018.zip

4.3 Launch ROS

    roslaunch floam floam.launch

if you would like to create the map at the same time, you can run (more cpu cost)

    roslaunch floam floam_mapping.launch

If the mapping process is slow, you may wish to change the rosbag speed by replacing "--clock -r 0.5" with "--clock -r 0.2" in your launch file, or you can change the map publish frequency manually (default is 10 Hz)

4.4 How to use floam nodes

The floam package provides three nodes: floam_lidar_node, floam_odom_node, floam_mapping_node. Each of these nodes feeds the next with the proper data, so the data flow goes:

  1. floam_lidar_node: detects edges and surfaces of input pointcloud
  2. floam_odom_node: estimate odometery based on edges and surfaces
  3. floam_mapping_node: combine pointcloud into map using odom estimation

Each node could be ran without the others, however the inputs of each need to be satisifed in order to run properly.

For example, the floam_mapping_node could be used with any odometry estimation and only requires an odom topic and a pointcloud topic. The odom topic could be coming from robot_localization or some other source for example.

5. Test on other sequence

To generate rosbag file of kitti dataset, you may use the tools provided by kitti_to_rosbag or kitti2bag

The default settings in the main launch file floam_all.launch should be set for this recording. See below for instructions on how to adjust the parameters for more fine-tuned performance

6. The Nodes of floam

The floam package was written in a way to be adjustable and usable for most lidars on the market today. Below is an outline of the parameters that you can adjust to see fine-tune the performance of floam for your device and use-case.

There are three main nodes normally run at once: floam_lidar_node, floam_odom_node and floam_mapping_node. Each one serves a specific purpose and can be ran without the other if needed. The adjustable parameters for each are outlined below.

6.1 floam_lidar_node

The floam_lidar_node has one primary purpose and that is to consume a single points_topic and detect edges and surfaces within the pointcloud. Within the node there are many paramters you can adjust to fine-tune this detection process for your use-case.

There are two different flavors of the lidar node for more efficient processing techniques for organized and unorganized pointclouds.

6.1.1. common parameters

  • points_topic: the raw input pointcloud

  • lidar_frame_id: the frame_id for your lidar (should be the same one that is in the header of your points_topic)

  • frame_id: new frame_id for edge and surface pointclouds.

  • edge_threshold: the threshold of curfavature of a surface to classify it as an edge. A larger edge threshold means more points will be classified as surfaces (see line 156 in src/lidar.cpp for use).

  • min_distance: ignore points within this distance

  • max_distance: ignore points past this distance

6.1.2 scanning lidar parameters

  • scan_lines: number of scan lines in the device (i.e. velodyne, ouster types). if you set this value to 1 then a kdTree is used to search for neighboring points within a radius (TODO: implement k point search method).

  • skip_points: used as an incrementor to decide which points to run the edge detection (kdTree search) on. Essentially "skipping" the number of points specified (i.e. if skip_points is 10, kdTree search will be performed on points 0, 10, 20, etc. in original pointcloud).

  • search_radius: used in kdTree.radiusSearch function to find all points around a specified point within some radius (meters).

[NOT YET USED] - search_k: used in kdTree search to find k nearset points to specified point.

  • period: period of sensor scan (not used anymore)

6.1.3 imaging lidar parameters

  • TODO

6.2 floam_odom_node parameters

  • odom_parent_frame_id: the parent frame of the odometry estimation, defaults to map.

  • edge_cloud_topic: the input pointcloud with labeled edges

  • surface_cloud_topic: the input pointcloud wiht labeled surfaces

6.3 floam_mapping_node parameters

  • map_resolution: defines the resolution of the final map pointcloud (meters)

  • mapping_points_topic: input pointcloud to create map from

  • mapping_odom_topic: input odom to use to create map

  • output_map_topic: output map pointcloud topic

7.Acknowledgements

Thanks for A-LOAM and LOAM(J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time) and LOAM_NOTED.

CHANGELOG

Changelog for package floam

0.1.0 (2021-08-22)

  • increase mapping odom queue size
  • trying to reduce copying
  • improve edge and surface detection
  • trying to improve edge detection
  • rewrote mapping class
  • minor fixes for single-scan device
  • working for unorganized single scanner
  • fix launch files for scanner with 1 scan line
  • functional mapping node
  • functional odom node
  • clean up diff logic, fix edge threshold param name
  • more clean up, default to exact sync in launch
  • fix frame_id, use variable for nearest k
  • use variable for k points to search
  • try different algos for predicting odom
  • clean up odom estimation classes
  • clean up coordinate frames, fix some error prints, transform pointcloud to new frame
  • fix odom node, launch files
  • make edgeThreshold param
  • scanning lidar nodelets functional
  • implement scanning lidar logic too
  • add ros ci actions
  • rewrote mapping nodelet
  • change defaults for lidar launch, add approx sync callback for odom
  • rewrote odom nodelet
  • implemented templates for imager and scanner
  • migrate to templates
  • split lidar class to scanning and imaging
  • edge detection working
  • switch to nodelets
  • fix launch files
  • fix package depends, clean up files
  • fix cmake error
  • add demo link
  • update
  • Merge pull request #24 from Chris7462/master Fix some minor issues
  • add img
  • update rosbag link
  • update edge cost
  • Fix frame_id names and downSizeFilterSurf and downSizeFilterEdge misplaced
  • remove multiple pointcloud subscription
  • change video
  • add video demo
  • change rosbag location
  • add floam ssl description
  • change frame name
  • add resultion setting and add support for velodyne VLP-16
  • fix function mismatch problem
  • add VLP16 description
  • add new points selection rules and some code optimization
  • add tf
  • add trajectory sever install description
  • change rosbag path
  • change picture location
  • change picture location
  • add mapping gif
  • add mapping node
  • add some comments to code
  • change frame name
  • add license
  • add comments to floam
  • remove opencv dependency
  • add csome instruction to roslaunch
  • add csome instruction to roslaunch
  • add csome instruction to roslaunch
  • update some comparison
  • update some comparison
  • update some comparison
  • update some comparison
  • floam
  • Contributors: Evan Flynn, Wang Han

Wiki Tutorials

See ROS Wiki Tutorials for more details.

Source Tutorials

Not currently indexed.

Launch files

  • launch/floam_mapping.launch
      • frame_id [default: base_link]
      • lidar_frame_id [default: velodyne]
      • use_exact_sync [default: true]
      • queue_size [default: 10]
      • map_resolution [default: 10]
      • mapping_points_topic [default: /floam_lidar_node/points_filtered]
      • mapping_odom_topic [default: /floam_odom_node/odom]
      • output_map_topic [default: /floam_mapping_node/map]
  • launch/floam_scanning_lidar.launch
      • points_topic [default: /velodyne_points]
      • frame_id [default: base_link]
      • lidar_frame_id [default: velodyne]
      • floam_frame_id [default: floam_$(arg lidar_frame_id)]
      • max_distance [default: 1000]
      • min_distance [default: 0.0]
      • edge_threshold [default: 0.05]
      • scan_lines [default: 64]
      • skip_points [default: 50]
      • search_k [default: 10]
      • search_radius [default: 0.25]
      • node_name [default: floam]
      • new_manager [default: true]
  • launch/floam_imaging_lidar.launch
      • points_topic [default: points]
      • frame_id [default: base_link]
      • lidar_frame_id [default: hfl118_front]
      • floam_frame_id [default: floam_$(arg lidar_frame_id)]
      • max_distance [default: 90]
      • min_distance [default: 0.5]
      • node_name [default: floam]
      • new_manager [default: true]
  • launch/floam_all.launch
      • isScanning [default: true]
      • points_topic [default: /velodyne_points]
      • frame_id [default: velodyne]
      • lidar_frame_id [default: velodyne]
      • floam_frame_id [default: floam_$(arg lidar_frame_id)]
      • max_distance [default: 200]
      • min_distance [default: 0.5]
      • use_exact_sync [default: true]
      • queue_size [default: 10]
      • edge_threshold [default: 0.05]
      • scan_lines [default: 32]
      • skip_points [default: 1]
      • search_k [default: 10]
      • search_radius [default: 0.25]
      • period [default: 0.2]
      • odom_parent_frame_id [default: map]
      • edge_cloud_topic [default: /floam_lidar_node/points_edge]
      • surface_cloud_topic [default: /floam_lidar_node/points_surface]
      • map_resolution [default: 0.4]
      • mapping_points_topic [default: /floam_lidar_node/points_filtered]
      • mapping_odom_topic [default: /floam_odom_node/odom]
      • output_map_topic [default: /floam_mapping_node/map]
      • node_name_base [default: floam]
      • manager_name [default: $(arg node_name_base)_manager]
      • global_manager_name [default: /$(arg manager_name)]
      • new_manager [default: true]
      • rviz [default: true]
  • launch/floam_odom.launch
      • frame_id [default: base_link]
      • odom_parent_frame_id [default: map]
      • use_exact_sync [default: true]
      • queue_size [default: 5]
      • map_resolution [default: 0.4]
      • edge_cloud_topic [default: /floam_lidar_node/points_edge]
      • surface_cloud_topic [default: /floam_lidar_node/points_surface]
      • odom_topic [default: /floam_odom_node/odom]
  • launch/project_depth_image.launch
      • lidar_name [default: lidar]
      • node_name [default: $(arg lidar_name)]
      • manager_name [default: $(arg lidar_name)_manager]
      • depth_camera_info_topic [default: /$(arg lidar_name)/depth/camera_info]
      • depth_image_topic [default: /$(arg lidar_name)/depth/image_raw]
      • points_topic [default: /$(arg lidar_name)/depth/points]
      • rectify_depth [default: false]

Messages

No message files found.

Services

No service files found

Plugins

Recent questions tagged floam at answers.ros.org

floam package from floam repo

floam

Package Summary

Tags No category tags.
Version 0.1.0
License BSD
Build type CATKIN
Use RECOMMENDED

Repository Summary

Checkout URI https://github.com/flynneva/floam.git
VCS Type git
VCS Version main
Last Updated 2021-08-23
Dev Status DEVELOPED
CI status No Continuous Integration
Released RELEASED
Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Package Description

A generic Lidar SLAM package based on FLOAM which was based on ALOAM by HKUST and LOAM by CMU

Additional Links

Maintainers

  • Evan Flynn

Authors

  • Han Wang

FLOAM

Fast LOAM (Lidar Odometry And Mapping)

This work is an optimized version of A-LOAM and LOAM with the computational cost reduced by up to 3 times. This code is modified from LOAM and A-LOAM .

Modifier: Wang Han, Nanyang Technological University, Singapore

Modifier: Evan Flynn

1. Demo Highlights

Watch our demo at Video Link

2. Evaluation

2.1. Computational efficiency evaluation

Computational efficiency evaluation (based on KITTI dataset): Platform: Intel® Core™ i7-8700 CPU @ 3.20GHz | Dataset | ALOAM | FLOAM | |----------------------------------------------|----------------------------|------------------------| | KITTI | 151ms | 59ms |

Localization error: | Dataset | ALOAM | FLOAM | |----------------------------------------------|----------------------------|------------------------| | KITTI sequence 00 | 0.55% | 0.51% | | KITTI sequence 02 | 3.93% | 1.25% | | KITTI sequence 05 | 1.28% | 0.93% |

2.2. localization result

2.3. mapping result

3. Prerequisites

3.1 Ubuntu and ROS

Ubuntu 64-bit 18.04 or 20.04

ROS Melodic or Noetic. ROS Installation

3.2. **Install dependencies using rosdep

Install dependencies using rosdep:

cd /your/catkin_ws
rosdep install --from-paths src --ignore-src -y

Or you can manually install the dependencies below:

3.3. Ceres Solver

Follow Ceres Installation.

3.4. PCL

Follow PCL Installation.

3.5. Trajectory visualization

For visualization purpose, this package uses hector trajectory sever, you may install the package by

sudo apt-get install ros-melodic-hector-trajectory-server

Alternatively, you may remove the hector trajectory server node if trajectory visualization is not needed

4. Build

Make sure to have installed the required dependencies as above before trying to build this package.

4.1 Clone repository:

    cd ~/catkin_ws/src
    git clone https://github.com/wh200720041/floam.git
    cd ..
    catkin_make
    source ~/catkin_ws/devel/setup.bash

4.2 Download test rosbag

Download KITTI sequence 05 or KITTI sequence 07

Unzip compressed file 2011_09_30_0018.zip. If your system does not have unzip. please install unzip by

sudo apt-get install unzip 

And this may take a few minutes to unzip the file

    cd ~/Downloads
    unzip ~/Downloads/2011_09_30_0018.zip

4.3 Launch ROS

    roslaunch floam floam.launch

if you would like to create the map at the same time, you can run (more cpu cost)

    roslaunch floam floam_mapping.launch

If the mapping process is slow, you may wish to change the rosbag speed by replacing "--clock -r 0.5" with "--clock -r 0.2" in your launch file, or you can change the map publish frequency manually (default is 10 Hz)

4.4 How to use floam nodes

The floam package provides three nodes: floam_lidar_node, floam_odom_node, floam_mapping_node. Each of these nodes feeds the next with the proper data, so the data flow goes:

  1. floam_lidar_node: detects edges and surfaces of input pointcloud
  2. floam_odom_node: estimate odometery based on edges and surfaces
  3. floam_mapping_node: combine pointcloud into map using odom estimation

Each node could be ran without the others, however the inputs of each need to be satisifed in order to run properly.

For example, the floam_mapping_node could be used with any odometry estimation and only requires an odom topic and a pointcloud topic. The odom topic could be coming from robot_localization or some other source for example.

5. Test on other sequence

To generate rosbag file of kitti dataset, you may use the tools provided by kitti_to_rosbag or kitti2bag

The default settings in the main launch file floam_all.launch should be set for this recording. See below for instructions on how to adjust the parameters for more fine-tuned performance

6. The Nodes of floam

The floam package was written in a way to be adjustable and usable for most lidars on the market today. Below is an outline of the parameters that you can adjust to see fine-tune the performance of floam for your device and use-case.

There are three main nodes normally run at once: floam_lidar_node, floam_odom_node and floam_mapping_node. Each one serves a specific purpose and can be ran without the other if needed. The adjustable parameters for each are outlined below.

6.1 floam_lidar_node

The floam_lidar_node has one primary purpose and that is to consume a single points_topic and detect edges and surfaces within the pointcloud. Within the node there are many paramters you can adjust to fine-tune this detection process for your use-case.

There are two different flavors of the lidar node for more efficient processing techniques for organized and unorganized pointclouds.

6.1.1. common parameters

  • points_topic: the raw input pointcloud

  • lidar_frame_id: the frame_id for your lidar (should be the same one that is in the header of your points_topic)

  • frame_id: new frame_id for edge and surface pointclouds.

  • edge_threshold: the threshold of curfavature of a surface to classify it as an edge. A larger edge threshold means more points will be classified as surfaces (see line 156 in src/lidar.cpp for use).

  • min_distance: ignore points within this distance

  • max_distance: ignore points past this distance

6.1.2 scanning lidar parameters

  • scan_lines: number of scan lines in the device (i.e. velodyne, ouster types). if you set this value to 1 then a kdTree is used to search for neighboring points within a radius (TODO: implement k point search method).

  • skip_points: used as an incrementor to decide which points to run the edge detection (kdTree search) on. Essentially "skipping" the number of points specified (i.e. if skip_points is 10, kdTree search will be performed on points 0, 10, 20, etc. in original pointcloud).

  • search_radius: used in kdTree.radiusSearch function to find all points around a specified point within some radius (meters).

[NOT YET USED] - search_k: used in kdTree search to find k nearset points to specified point.

  • period: period of sensor scan (not used anymore)

6.1.3 imaging lidar parameters

  • TODO

6.2 floam_odom_node parameters

  • odom_parent_frame_id: the parent frame of the odometry estimation, defaults to map.

  • edge_cloud_topic: the input pointcloud with labeled edges

  • surface_cloud_topic: the input pointcloud wiht labeled surfaces

6.3 floam_mapping_node parameters

  • map_resolution: defines the resolution of the final map pointcloud (meters)

  • mapping_points_topic: input pointcloud to create map from

  • mapping_odom_topic: input odom to use to create map

  • output_map_topic: output map pointcloud topic

7.Acknowledgements

Thanks for A-LOAM and LOAM(J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time) and LOAM_NOTED.

CHANGELOG

Changelog for package floam

0.1.0 (2021-08-22)

  • increase mapping odom queue size
  • trying to reduce copying
  • improve edge and surface detection
  • trying to improve edge detection
  • rewrote mapping class
  • minor fixes for single-scan device
  • working for unorganized single scanner
  • fix launch files for scanner with 1 scan line
  • functional mapping node
  • functional odom node
  • clean up diff logic, fix edge threshold param name
  • more clean up, default to exact sync in launch
  • fix frame_id, use variable for nearest k
  • use variable for k points to search
  • try different algos for predicting odom
  • clean up odom estimation classes
  • clean up coordinate frames, fix some error prints, transform pointcloud to new frame
  • fix odom node, launch files
  • make edgeThreshold param
  • scanning lidar nodelets functional
  • implement scanning lidar logic too
  • add ros ci actions
  • rewrote mapping nodelet
  • change defaults for lidar launch, add approx sync callback for odom
  • rewrote odom nodelet
  • implemented templates for imager and scanner
  • migrate to templates
  • split lidar class to scanning and imaging
  • edge detection working
  • switch to nodelets
  • fix launch files
  • fix package depends, clean up files
  • fix cmake error
  • add demo link
  • update
  • Merge pull request #24 from Chris7462/master Fix some minor issues
  • add img
  • update rosbag link
  • update edge cost
  • Fix frame_id names and downSizeFilterSurf and downSizeFilterEdge misplaced
  • remove multiple pointcloud subscription
  • change video
  • add video demo
  • change rosbag location
  • add floam ssl description
  • change frame name
  • add resultion setting and add support for velodyne VLP-16
  • fix function mismatch problem
  • add VLP16 description
  • add new points selection rules and some code optimization
  • add tf
  • add trajectory sever install description
  • change rosbag path
  • change picture location
  • change picture location
  • add mapping gif
  • add mapping node
  • add some comments to code
  • change frame name
  • add license
  • add comments to floam
  • remove opencv dependency
  • add csome instruction to roslaunch
  • add csome instruction to roslaunch
  • add csome instruction to roslaunch
  • update some comparison
  • update some comparison
  • update some comparison
  • update some comparison
  • floam
  • Contributors: Evan Flynn, Wang Han

Wiki Tutorials

See ROS Wiki Tutorials for more details.

Source Tutorials

Not currently indexed.

Launch files

  • launch/floam_mapping.launch
      • frame_id [default: base_link]
      • lidar_frame_id [default: velodyne]
      • use_exact_sync [default: true]
      • queue_size [default: 10]
      • map_resolution [default: 10]
      • mapping_points_topic [default: /floam_lidar_node/points_filtered]
      • mapping_odom_topic [default: /floam_odom_node/odom]
      • output_map_topic [default: /floam_mapping_node/map]
  • launch/floam_scanning_lidar.launch
      • points_topic [default: /velodyne_points]
      • frame_id [default: base_link]
      • lidar_frame_id [default: velodyne]
      • floam_frame_id [default: floam_$(arg lidar_frame_id)]
      • max_distance [default: 1000]
      • min_distance [default: 0.0]
      • edge_threshold [default: 0.05]
      • scan_lines [default: 64]
      • skip_points [default: 50]
      • search_k [default: 10]
      • search_radius [default: 0.25]
      • node_name [default: floam]
      • new_manager [default: true]
  • launch/floam_imaging_lidar.launch
      • points_topic [default: points]
      • frame_id [default: base_link]
      • lidar_frame_id [default: hfl118_front]
      • floam_frame_id [default: floam_$(arg lidar_frame_id)]
      • max_distance [default: 90]
      • min_distance [default: 0.5]
      • node_name [default: floam]
      • new_manager [default: true]
  • launch/floam_all.launch
      • isScanning [default: true]
      • points_topic [default: /velodyne_points]
      • frame_id [default: velodyne]
      • lidar_frame_id [default: velodyne]
      • floam_frame_id [default: floam_$(arg lidar_frame_id)]
      • max_distance [default: 200]
      • min_distance [default: 0.5]
      • use_exact_sync [default: true]
      • queue_size [default: 10]
      • edge_threshold [default: 0.05]
      • scan_lines [default: 32]
      • skip_points [default: 1]
      • search_k [default: 10]
      • search_radius [default: 0.25]
      • period [default: 0.2]
      • odom_parent_frame_id [default: map]
      • edge_cloud_topic [default: /floam_lidar_node/points_edge]
      • surface_cloud_topic [default: /floam_lidar_node/points_surface]
      • map_resolution [default: 0.4]
      • mapping_points_topic [default: /floam_lidar_node/points_filtered]
      • mapping_odom_topic [default: /floam_odom_node/odom]
      • output_map_topic [default: /floam_mapping_node/map]
      • node_name_base [default: floam]
      • manager_name [default: $(arg node_name_base)_manager]
      • global_manager_name [default: /$(arg manager_name)]
      • new_manager [default: true]
      • rviz [default: true]
  • launch/floam_odom.launch
      • frame_id [default: base_link]
      • odom_parent_frame_id [default: map]
      • use_exact_sync [default: true]
      • queue_size [default: 5]
      • map_resolution [default: 0.4]
      • edge_cloud_topic [default: /floam_lidar_node/points_edge]
      • surface_cloud_topic [default: /floam_lidar_node/points_surface]
      • odom_topic [default: /floam_odom_node/odom]
  • launch/project_depth_image.launch
      • lidar_name [default: lidar]
      • node_name [default: $(arg lidar_name)]
      • manager_name [default: $(arg lidar_name)_manager]
      • depth_camera_info_topic [default: /$(arg lidar_name)/depth/camera_info]
      • depth_image_topic [default: /$(arg lidar_name)/depth/image_raw]
      • points_topic [default: /$(arg lidar_name)/depth/points]
      • rectify_depth [default: false]

Messages

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Services

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Plugins

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