    colcon build --symlink-install --allow-overriding ackermann_steering_controller ; source install/setup.bash ; ros2 launch car_gazebo car_bringup.launch.py
    [1.045s] WARNING:colcon.colcon_ros.prefix_path.ament:The path '/home/aarjan/minor_ros/install/ackermann_drive_controller' in the environment variable AMENT_PREFIX_PATH doesn't exist
    [1.045s] WARNING:colcon.colcon_ros.prefix_path.catkin:The path '/home/aarjan/minor_ros/install/ackermann_drive_controller' in the environment variable CMAKE_PREFIX_PATH doesn't exist

    Starting >>> ackermann_steering_controller
    Starting >>> car_body
    Starting >>> pubsub
    Finished <<< car_body [0.88s]
    Finished <<< ackermann_steering_controller [1.08s]
    Starting >>> car_gazebo
    Finished <<< car_gazebo [0.24s]
    Finished <<< pubsub [1.60s]

    Summary: 4 packages finished [2.60s]
    [INFO] [launch]: All log files can be found below /home/aarjan/.ros/log/2024-02-12-21-58-28-865613-robotics-25902
    [INFO] [launch]: Default logging verbosity is set to INFO
    [INFO] [ros2_control_node-1]: process started with pid [25905]
    [INFO] [robot_state_publisher-2]: process started with pid [25907]
    [INFO] [spawner-3]: process started with pid [25909]
    [ros2_control_node-1] [WARN] [1707754409.129160634] [controller_manager]: [Deprecated] Passing the robot description parameter directly to the control_manager node is deprecated. Use '~/robot_description' topic from 'robot_state_publisher' instead.

    [ros2_control_node-1] [INFO] [1707754409.129328123] [resource_manager]: Loading hardware 'ackermann_ros2_control'
    [ros2_control_node-1] terminate called after throwing an instance of 'pluginlib::LibraryLoadException'
    [ros2_control_node-1]   what():  According to the loaded plugin descriptions the class ackermann_steering_controller/AckermannSteeringController with base class type hardware_interface::SystemInterface does not exist. Declared types are  fake_components/GenericSystem mock_components/GenericSystem test_hardware_components/TestSystemCommandModes test_hardware_components/TestTwoJointSystem test_system test_unitilizable_system turtlebot3_manipulation_hardware/TurtleBot3ManipulationSystemHardware

    [ros2_control_node-1] Stack trace (most recent call last):
    [ros2_control_node-1] #16   Object "", at 0xffffffffffffffff, in
    [ros2_control_node-1] #15   Object "/opt/ros/humble/lib/controller_manager/ros2_control_node", at 0x55fb7f788d84, in
    [robot_state_publisher-2] [INFO] [1707754409.131011613] [robot_state_publisher]: got segment base_footprint
    [robot_state_publisher-2] [INFO] [1707754409.131083738] [robot_state_publisher]: got segment base_link
    [robot_state_publisher-2] [INFO] [1707754409.131088347] [robot_state_publisher]: got segment chassis
    [robot_state_publisher-2] [INFO] [1707754409.131091102] [robot_state_publisher]: got segment front_left_wheel
    [robot_state_publisher-2] [INFO] [1707754409.131093672] [robot_state_publisher]: got segment front_right_wheel
    [robot_state_publisher-2] [INFO] [1707754409.131095965] [robot_state_publisher]: got segment left_steering_link
    [robot_state_publisher-2] [INFO] [1707754409.131098241] [robot_state_publisher]: got segment rear_left_wheel
    [robot_state_publisher-2] [INFO] [1707754409.131100390] [robot_state_publisher]: got segment rear_right_wheel
    [robot_state_publisher-2] [INFO] [1707754409.131102542] [robot_state_publisher]: got segment right_steering_link


Before we dive into the code, let’s quickly recap how ros2_control will work
with our robot. At one end, we have a body velocity request, that’s the
velocity we want the robot to be moving through space, and we call this the
_command velocity_ . This could come from an operator, or something like a
navigation stack. At the other end, we’ve got the physical robot, with two
drive wheels, each of which are velocity controlled.


[ CHANGELOG.rst
](/PickNikRobotics/topic_based_ros2_control/blob/main/CHANGELOG.rst
"CHANGELOG.rst")

|

|


rear_wheels_radius: 0.115


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the Nimbro gives a spoken feedback with the recognized
gesture.
Nimbro Camera
Gesture Recognition
Module
ROS
Publisher
ROS
Subscriber
Speech Module
ROS
Publisher
ROS
Subscriber
Fig. 5: Diagram of system interface over ROS network with
Publisher-Subscriber nodes.
is composed of two main modules: one module for recognizing the learned gestures from visual input and the other for
speaking the recognized gestures with Nimbro. To interface
the different modules and devices of our architecture we use
the Robot Operating System (ROS).
For our interface implementation, we rely on a synchronous RPC-style communication over a ROS network
implemented with publisher-subscriber nodes. The publisher
node will continually broadcast a message. We broadcast a
message over the network using a message-adapted class.
The subscriber node receives the messages on a given topic
via a master node, which keeps a registry of who is publishing and who subscribing. This architecture represents a
robust interface to connect different applications, e.g. written
in different programming languages, over a common network
of communication.
A diagram of our system interface over the ROS network
is illustrated in Figure 5. The gesture recognition module
receives input, i.e. raw RGB images, from the Nimbro
camera over the ROS network. Results of the recognition,
i.e. gesture labels, are published for the speech module.
V. RESULTS
The offline experiment used the recorded dataset to train
the model and to perform a classification of the gestures. The
results showed that the mean F-Score for all the gestures was
Fig. 6: F-Score obtained in both experiments. Nimbro experiment presented lower results, but still demonstrate the
capability of the model in recognizing dynamic gestures in


[ README.md ](/PickNikRobotics/topic_based_ros2_control/blob/main/README.md
"README.md")

|


[ .clang-format ](/PickNikRobotics/topic_based_ros2_control/blob/main/.clang-
format ".clang-format")

|


compete well with other methods, resulting in rank 6 of 20
final presentations.
Although the presented work showed promising results,
we point out some critical issues: standard methods for
gesture recognition is to use specifically coloured objects
like a red ball to be tracked or as in [4] to use gloves.
This, however, introduces an additional preprocessing step of
reduce the input to a particular colour or learning a specific
colour distribution. We also recognized that in literature only
a few papers deal with CNN in the domain of dynamic
gestures. As mentioned above, this is due to the fact that
CNN were introduced rather for single image processing and
classification. The extension of this approach as in [5] for
action recognition motivates us to this more neurobiological
approach for commanding gestures comprising motions.
Therefore, we extend the idea of CNNs in two ways:
first, we enhance the computational capacity of the model
by introducing 3D kernels suitable for noise-affected images
as is the case when captured in a stream in ambient living
situations and sensor limitations. Second, we extend CNN
to the temporal domain by adding the capability to generate
and learn motion representations. In our paper, we focus on
gestural communication with the Nimbro robot platform.
Therefore, we defined 5 command gestures: ’Circle’,
’Point Left’, ’Point Right’, ’Stop’ and ’Turn Around’, and
’Stand Still’ as our reference standing position. In our
scenario, we also embed a simple speech response toprovide
user feedback.
The paper structure is as follows: In the first section,
we introduce our deep neural architecture and processing
stages in the network. We then explain our experimental
methodology in section 2 and present our results derived
from the experiments in the following section. Finally, we
provide the reader with a discussion part and some future
work suggestions.
II. DEEP TEMPORAL AND SPATIAL FEATURE
EXTRACTION LEARNING FRAMEWORK
In order to extract the temporal and spatial features of a
gesture sequence, we use a deep neural network architecture.
This architecture is able to create a representation of motion


As a first test, we want to:


14 Oct 2022


[ transformations (5) ](/categories#transformations) [ linear (1)
](/categories#linear) [ coordinate (4) ](/categories#coordinate) [ 2d (1)
](/categories#2d) [ transforms (1) ](/categories#transforms) [ rotations (1)
](/categories#rotations) [ posts (1) ](/categories#posts) [ ros (23)
](/categories#ros)


Remember if you haven’t cloned your main project package (e.g. ` articubot_one
` for me) onto the robot yet to do that one as well, and if you _have_ already
cloned it you’ll want to pull the latest changes (from the last tutorial) from
git.


The tool we’re going to use to link up this command velocity to the actual
motor velocities is ros2_control. This little ecosystem consists of three main
parts:


**3. Wheel separation**


[ CHANGELOG.rst
](/PickNikRobotics/topic_based_ros2_control/blob/main/CHANGELOG.rst
"CHANGELOG.rst")

|


##  Conclusion


    cd robot_ws/src
    git clone https://github.com/joshnewans/serial
    git clone https://github.com/joshnewans/diffdrive_arduino
    cd ..
    colcon build --symlink-install

---|---
`


  [ Contributors  4
](/PickNikRobotics/topic_based_ros2_control/graphs/contributors)

  * [ ![@JafarAbdi](https://avatars.githubusercontent.com/u/16278108?s=64&v=4) ](https://github.com/JafarAbdi) [ **JafarAbdi** Jafar Uruç  ](https://github.com/JafarAbdi)
  * [ ![@MarqRazz](https://avatars.githubusercontent.com/u/25058794?s=64&v=4) ](https://github.com/MarqRazz) [ **MarqRazz** Marq Rasmussen  ](https://github.com/MarqRazz)
  * [ ![@moriarty](https://avatars.githubusercontent.com/u/55676?s=64&v=4) ](https://github.com/moriarty) [ **moriarty** Alex Moriarty  ](https://github.com/moriarty)
  * [ ![@kineticsystem](https://avatars.githubusercontent.com/u/5970998?s=64&v=4) ](https://github.com/kineticsystem) [ **kineticsystem** Giovanni  ](https://github.com/kineticsystem)


VI. CONCLUSION AND FUTURE WORK
We presented a neural framework extended from CNNs for
the recognition of commanding gestures in a HRI-scenario.
To test our approach we set up two experiments and extended
the recognition with audio feedback for the user. Opposed
to [4] our framework needs no additional input devices nor
specific colour definitions, which is beneficial for a natural
user interface. Our work shows that the proposed model is
suited to be used in a real-world scenario. After the training,
the recognition time is small and the spatial-temporal features
are extracted and learned. Although we conducted rather
initial experiments, we could show promising results for
gesture classification. We detected very good accuracy for the
’circle’, ’point right’, ‘stop‘ and the ’turn’ gesture, but lower
accuracy for ’point left’ and ’stand’, our baseline gesture.
This is due to the fact, that our system relies on motion
information and has only minor focus for hand shape. The
’point left’ gesture executed with the same hand as the ’point
right’ carries more motion information as the arm crosses the
body. For the ’point right’ on the contrary the arm is raised
up almost equal to the ’stop gesture’. The only difference is
the orientation of the hand. So, we suggest to also provide
a shape- and orientation representation for such low-motion
gestures as can be found in the dichotomized streams in the
visual cortex. Another interesting point worth mentioning
is that in our live experiments with the Nimbro we could
show that our command vocabulary is recognized in realtime indifferent to a specific user, which puts emphasis on
the generalization capabilities of the MCCNN. In line with
that our approach also allows us to cope with intra- and intersubject variability in gesture performance, as our subjects
performed each gesture several times and had no information
about the execution speed. No time warping mechanisms
or similar are necessary as is standard in variable-length
sequence processing, thus avoids additional preprocessing.
Therefore, we would like to use our extended gesture
database with gestures performed with one- and two hands
from multiple subjects. Until now, our setting is restricted to
one-hand gestures and simplified illumination conditions. As
the model generates the motion features using raw images,
light conditions may affect the final results and our system
is limited to one moving person in the scene. Thus, we are


We should see the laser data come up in RViz, and hopefully as we drive
around, the detected objects should stay in approximately the same place.
There will be some errors due to timing and also odometry drift.


##  Getting the hardware interface


**Thank You!**


real-time using the humanoid robot.
96.85%. In Figure 6 we show that Point Left and Standing
produced a lower F-Score compared to the others, due to the
fact that the motion images generated from both are very
similar. Even with the visual similarity, the model was still
able to identify and classify them with an elevated F-Score.
One of the problems of a deep neural architecture is the
necessary time to train the model. One of the advantages of
the MCCNN is to be able to use smaller images and still be
able to classify the presented patterns. In our experiments,
each training routine, with 100 epochs, took 165.60 minutes
to complete, with a standard deviation of 0.027 after 30
executions. The recognition time was 0.039 seconds, with a
standard deviation of 0.0022. With such a smaller recognition
time, it is possible to use the proposed model in a real-time
classification scenario.
In the Nimbro experiment, the model was trained using the
recorded dataset, in the same way as for the first experiment.
After the training, it was deployed in the Nimbro and used
to classify the input obtained by its camera. Three different
persons executed the gestures, none of them present in the
previously recorded dataset. The F-Scores for this experiment
are also shown in Figure 6.
Figure 7 shows the confusion matrix obtained for the
Nimbro experiment. In the confusion matrix we see that
most of the misclassification are related to the ”Stand”
position. This happens because most of the gestures contain
the ”Stand” position in the beginning and ending of their
execution. If the gesture is executed too fast or too slow, the
”Stand” position will still be captured and will be highlighted
by the motion representation layer.
When using the live classification with Nimbro the results
are lower compared to the offline experiment, but still have
a significant F-score value when applied to a real-world
scenario. The experiment shows that the model was able
to recognize gestures with different persons, that were not
present in the training set, and in real-time.
Fig. 7: Confusion matrix obtained by Nimbro experiment,
using the Nimbro‘s camera to recognize the gestures in a
real-time recognition scenario.


**1. Encoder accuracy**


    robot_description = Command(['ros2 param get --hide-type /robot_state_publisher robot_description'])

---|---
`


**Here is the ros2_control linked with hardware linked directly as specified
in urdf of car.**


[ CONTRIBUTING.md
](/PickNikRobotics/topic_based_ros2_control/blob/main/CONTRIBUTING.md
"CONTRIBUTING.md")

|


###  Sensor Integration


![Josh Newans](/assets/images/logo3.png)


![Radius test](/media/assets/posts/mobile-robot/13-ros2-control-
real/radius.png)


Contact us
Submit your inquiry and we will contact you


[ LICENSE ](/PickNikRobotics/topic_based_ros2_control/blob/main/LICENSE
"LICENSE")

|


![Command velocity](/media/assets/posts/mobile-robot/13-ros2-control-
real/cmd_vel.png)


  [ Releases ](/PickNikRobotics/topic_based_ros2_control/releases)

[ 3  tags  ](/PickNikRobotics/topic_based_ros2_control/tags)


###  Basic driving


  * [ __ ](https://twitter.com/intent/tweet?text=Making a Mobile Robot #13 - Using ros2_control on a real robot&url=https://articulatedrobotics.xyz/mobile-robot-13-ros2-control-real/)
  * [ __ ](https://facebook.com/sharer.php?u=https://articulatedrobotics.xyz/mobile-robot-13-ros2-control-real/)
  * [ __ ](https://www.linkedin.com/shareArticle?mini=true&url=https://articulatedrobotics.xyz/mobile-robot-13-ros2-control-real/)


This is also a chance to start testing our sensors (lidar, camera, and/or
depth camera). We can simply run the [ launch
](https://github.com/joshnewans/articubot_one/blob/main/launch/rplidar.launch.py)
[ files
](https://github.com/joshnewans/articubot_one/blob/main/launch/camera.launch.py)
we created for these in previous tutorials.


###  Controller sim time


**means that ackermann_steering_controller is exported with base class type as
controller_interface::ChainableControllerInterface but ros2_control_node is
expecting it to be of base class type as
hardware_interface::SystemInterface.**


See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/283646155
Real-time gesture recognition using a humanoid robot with a deep neural
architecture
Article · February 2015
DOI: 10.1109/HUMANOIDS.2014.7041431
CITATIONS
36
READS
636
4 authors:
Pablo Barros
University of Hamburg
95 PUBLICATIONS   1,746 CITATIONS
SEE PROFILE
German I. Parisi
University of Hamburg
64 PUBLICATIONS   3,439 CITATIONS
SEE PROFILE
Doreen Jirak
Istituto Italiano di Tecnologia
26 PUBLICATIONS   466 CITATIONS
SEE PROFILE
Stefan Wermter
University of Hamburg
666 PUBLICATIONS   10,341 CITATIONS
SEE PROFILE
All content following this page was uploaded by German I. Parisi on 01 June 2016.
The user has requested enhancement of the downloaded file.
In: Proc. of the IEEE-RAS International Conference on Humanoid Robots (Humanoids 14), pp. 83, Madrid, Spain (2014)
Real-time Gesture Recognition Using a Humanoid Robot with a Deep
Neural Architecture
Pablo Barros, German I. Parisi, Doreen Jirak and Stefan Wermter
Abstract— Dynamic gesture recognition is one of the most
interesting and challenging areas of Human-Robot-Interaction
(HRI). Problems like image segmentation, temporal and spatial
feature extraction and real-time recognition are the most prominent issues to name in this context. This work proposes a deep
neural model to recognize dynamic gestures with minimal image
preprocessing and real-time recognition in an experimental set
up using a humanoid robot. We conduct two experiments with
command gestures in an offline fashion and for demonstration


    robot_description_config = Command(['xacro ', xacro_file, ' use_ros2_control:=', use_ros2_control, ' sim_mode:=', use_sim_time])

---|---
`


  License

[ BSD-3-Clause license
](/PickNikRobotics/topic_based_ros2_control/blob/main/LICENSE)

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](/PickNikRobotics/topic_based_ros2_control/tags) [ Activity
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[ .pre-commit-config.yaml
](/PickNikRobotics/topic_based_ros2_control/blob/main/.pre-commit-config.yaml
".pre-commit-config.yaml")

|


Again, this will cause problems for the Gazebo simulation but we’ll fix that
up later.


* [ ros ](/categories#ros)


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###  Xacro switch


One problem we’ll run into is that we need the robot state publisher to have
finished starting up before we run the ` ros2 param get ` command, or else
we’ll fail to start the controller manager and nothing will work. The simplest
(though probably not the best) way to do this is to simply add a time delay.
The example below uses 3 seconds which should be enough.


    1
    2


  Documentation

  * [ Installation ](/PickNikRobotics/topic_based_ros2_control/blob/main/doc/installation.md)
  * [ User Guide ](/PickNikRobotics/topic_based_ros2_control/blob/main/doc/user.md)
  * [ Developers Guide ](/PickNikRobotics/topic_based_ros2_control/blob/main/doc/developer.md)


Now we can update that ` parameters ` line from before, to include our robot
description and controller parameters:


We’ll get rid of the Gazebo launch and spawner since we don’t need them any
more, but something we will need is a controller manager. Remember that last
time the Gazebo plugin was running a controller manager for us, but now we
need to start one ourselves. This starts simply enough, we can copy one of our
spawners, change the name of the variable, and also change the executable to `
ros2_control_node ` . We’ll also leave a spot for the parameters we’re about
to fill in.


###  Getting the robot description


###  Add the controller manager node


  * Run our ` launch_robot.launch.py ` script on the Pi, via SSH
  * Start RViz on our development machine, with the fixed frame set to ` odom ` and various displays enabled (e.g. TF, RobotModel)
  * On the dev machine, run ` teleop_twist_keyboard ` (or your preferred teleop method) with the topics remapped like in the previous tutorial ( ` -r /cmd_vel:=/diff_cont/cmd_vel_unstamped ` )


Hello!


Now we need to update our URDF file to reflect the new hardware interface.
Remember that we are editing these files **on the Raspberry Pi** .


The distance travelled in RViz should be the same as in real life. If not, you
need to adjust your wheel radius.


[ src ](/PickNikRobotics/topic_based_ros2_control/tree/main/src "src")

|


![Lidar test](/media/assets/posts/mobile-robot/13-ros2-control-real/lidar.png)


**Here is the parameters file for the ackermann_steering_controller package**


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> Note: To edit files on the Pi I like to use VS Code Remote Development. You
> can also just do it in the terminal with your favourite command line editor,
> or on your development machine and sync the files across to test them.


    # Delete the Gazebo stuff and replace it with this
    controller_manager = Node(
            package='controller_manager',
            executable='ros2_control_node',
            parameters=[XXXXXXXXX],
            )

---|---
`


[ ROS2 Camera Topic Publish Hz Problem
](https://forums.developer.nvidia.com/t/ros2-camera-topic-publish-hz-
problem/231683/2)


The source code of the subscriber node [ zed_video_sub_tutorial.cpp
](https://github.com/stereolabs/zed-
ros2-examples/tree/master/tutorials/zed_video_tutorial/src) :


## Metadata topic

The metadata messages store the camera's available metadata in a *json* format. To learn more, a dedicated script for echoing a metadata topic in runtime is attached. For instance, use the following command to echo the camera/depth/metadata topic:
```
python3 src/realsense-ros/realsense2_camera/scripts/echo_metadada.py /camera/camera/depth/metadata
```

<hr>


  ### Example
#### Manually loading multiple components into the same process
* Start the component:
  ```bash
  ros2 run rclcpp_components component_container
  ```


  * [ Home ](/)
  * [ Categories ](/categories)
  * [ FAQ/Guidelines ](/guidelines)
  * [ Terms of Service ](https://www.nvidia.com/en-us/about-nvidia/legal-info/)
  * [ Privacy Policy ](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/)


ros2 launch zed_display_rviz2 display_zed_cam.launch.py camera_model:=<camera model>


For various options on stepping time forward at specific frequencies/step
sizes.


Tutorials


  Create a Point Cloud (PointCloud2)

To create Point Cloud, run the ` point_cloud_node ` (the default topic for the
` DisparityImage ` is ` /disparity ` so we don’t explicitly pass it below)

    ros2 run stereo_image_proc point_cloud_node \
      --ros-args --remap left/image_rect_color:=left/image_rect \
      --param approximate_sync:=True \
      --param avoid_point_cloud_padding:=True

Now, view the output ( ` /points2 ` ):

    $ ros2 topic list
    /disparity
    /left/camera_info
    /left/image_rect
    /parameter_events
    /points2
    /right/camera_info
    /right/image_rect
    /rosout$ ros2 topic info /points2
    Type: sensor_msgs/msg/PointCloud2
    Publisher count: 1
    Subscription count: 0$ ros2 topic echo /points2 --no-arr
    header:
      stamp:
        sec: 1610071282
        nanosec: 552372030
      frame_id: foo
    height: 720
    width: 1280
    fields: '<sequence type: sensor_msgs/msg/PointField, length: 4>'
    is_bigendian: false
    point_step: 16
    row_step: 20480
    data: '<sequence type: uint8, length: 14745600>'


<hr>


The _Camera Helper Node_ is abstracting a complex postprocessing network from
the users. Once you press _Play_ with a Camera Helper Node connected, you may
see that in the list of Action Graphs when you click on the icon on the upper
left corner of the Action Graph window, a new one appears: `
/Render/PostProcessing/SDGPipeline  ` . This graph is automatically created by
the Camera Helper Node. The pipeline retrieves relevant data from the
renderer, process it, and send them to the corresponding ROS publisher. This
graph is only created in the session you are running. It will not be saved as
part of your asset and will not appear in the Stage tree.


We will check the images published this time in RViz2. In a ROS2-sourced
terminal, type in the command ` rviz2  ` to open RViz. Add a Image display
type and set the topic to ` rgb  ` .


  * ` Camera ` : Displays an image from a camera, with the visualized world rendered behind it
  * ` Image ` : Displays an image from a topic of type ` sensor_msgs/Image `


This tutorial introduces how to publish camera and perception data in ROS2.


##  Video with RVIZ2  #


    Intel RealSense ROS1 Wrapper

    Intel Realsense ROS1 Wrapper is not supported anymore, since our developers team are focusing on ROS2 distro.

    For ROS1 wrapper, go to ros1-legacy branch


Yes the same applies to ROS2 Omnigraph nodes as well.
Note that if your current FPS is lower than 30, there is no way to increase
this because its GPU/CPU limited performance wise.


* Completed tutorial on [ OmniGraph  ](../gui_tutorials/tutorial_gui_omnigraph.html#isaac-sim-app-tutorial-gui-omnigraph) and [ Add Camera and Sensors  ](../gui_tutorials/tutorial_gui_camera_sensors.html#isaac-sim-app-tutorial-gui-camera-sensors)


To visualize the video stream published by the ZED node, you can use two
different plugins:


###  Introduction  #


Node declaration:


      --remap /left/image_raw:=/left/image_rect \
      --param camera_frame_id:=test_frame_id

You’ll have to update the ` Global Options > Fixed Frame ` to use either `
test_frame_id ` or ` test_child_frame_id ` . Below are images of the finish
product. I bend the stereo camera frame (made of cardboard) to change the
position of the cameras to change the disparity image. You can see the arms of
the workout stand move farther away (disparity is blue) and then move closer
(disparity is green).

⚠ **Apologies for the weird blinking/flicker at the end of the GIF** . I am
not a photo editor and used [ peek ](https://github.com/phw/peek) to capture
these.

#  Summary

Creating PointCloud2 message is possible in ROS2 using two USB cameras and
various nodes in the ` image_pipeline ` and ` image_common ` packages. While
this tutorial is incomplete for a production-ready computer vision system, it
should be enough to get you started and ready to apply to more advanced
robotic concepts.

final graph using rqt_graph


Copyright © 2024 Stereolabs Inc.


[![rolling][rolling-badge]][rolling]
[![iron][iron-badge]][iron]
[![humble][humble-badge]][humble]
[![foxy][foxy-badge]][foxy]
[![ubuntu22][ubuntu22-badge]][ubuntu22]
[![ubuntu20][ubuntu20-badge]][ubuntu20]


## Table of contents
  * [ROS1 and ROS2 legacy](#ros1-and-ros2-legacy)
  * [Installation on Ubuntu](#installation-on-ubuntu)
  * [Installation on Windows](#installation-on-windows)
  * [Usage](#usage)
     * [Starting the camera node](#start-the-camera-node)
     * [Camera name and namespace](#camera-name-and-camera-namespace)
     * [Parameters](#parameters)
     * [ROS2-vs-Optical Coordination Systems](#ros2robot-vs-opticalcamera-coordination-systems)
     * [TF from coordinate A to coordinate B](#tf-from-coordinate-a-to-coordinate-b)
     * [Extrinsics from sensor A to sensor B](#extrinsics-from-sensor-a-to-sensor-b)
     * [Topics](#published-topics)
     * [RGBD Topic](#rgbd-topic)
     * [Metadata Topic](#metadata-topic)
     * [Post-Processing Filters](#post-processing-filters)
     * [Available Services](#available-services)
     * [Efficient intra-process communication](#efficient-intra-process-communication)
  * [Contributing](CONTRIBUTING.md)
  * [License](LICENSE)


  * [ Omniverse Glossary of Terms ](../common/glossary-of-terms.html)
  * [ Feedback and Forums ](../common/feedback.html)
  * [ Omniverse Licenses ](../common/legal.html)


is_dense: false


For the purpose of this tutorial, let’s add two stationary cameras, naming
them _Camera_1_ and _Camera_2_ , viewing the room from two different
perspectives. The procedures for adding cameras to the stage can be found in [
Add Camera and Sensors
](../gui_tutorials/tutorial_gui_camera_sensors.html#isaac-sim-app-tutorial-
gui-camera-sensors) .


  Enabling accel and gyro is achieved either by adding the following parameters to the command line:</br>
`ros2 launch realsense2_camera rs_launch.py pointcloud.enable:=true enable_gyro:=true enable_accel:=true` </br>
or in runtime using the following commands:
```
ros2 param set /camera/camera enable_accel true
ros2 param set /camera/camera enable_gyro true
```


<hr>


I want to **get the camera topic of fixed fps ( 30 ) when i publish** the
camera topic(/carter1/rgb_left, /carter1/rgb_right) using ros2.


Moving from ros2-legacy to ros2-development


We can visualize depth using the ` rqt_image_view  ` method again: ` ros2  run
rqt_image_view  rqt_image_view  /depth  ` .


* Checkout [ Publishing Camera’s Data  ](tutorial_ros2_camera_publishing.html#isaac-sim-app-tutorial-ros2-camera-publishing) to learn how to publish camera’s data through Python scripting.


$ ros2 run zed_tutorial_video zed_tutorial_video --ros-args -r left_image:=/zed/zed_node/left/image_rect_color -r right_image:=/zed/zed_node/right/image_rect_color


## ROS2(Robot) vs Optical(Camera) Coordination Systems:


The current QoS of the topic itself, is the same as Depth and Color streams (SYSTEM_DEFAULT)


## TF from coordinate A to coordinate B:


  * Video with RVIZ2
  * Video subscribing in C++


  * [ 1\. Introductory ](../introductory_tutorials/index.html)
  * [ 2\. GUI ](../gui_tutorials/index.html)
  * [ 3\. Development Tools ](../development_tools_tutorials.html)
  * [ 4\. Core API ](../core_api_tutorials/index.html)
  * [ 5\. Scene Setup ](../environment_setup_tutorials.html)
  * [ 6\. Motion Generation ](../advanced_tutorials/tutorials_advanced_motion_generation.html)
  * [ 7\. ROS / ROS 2 ](../ros_ros2_tutorials.html)
    * [ 7.1. ROS Tutorials (Linux Only) ](../ros_tutorials/index.html)
    * [ 7.2. ROS 2 Tutorials (Linux & Windows) ](index.html)
      * [ 7.2.1. URDF Import: Turtlebot ](tutorial_ros2_turtlebot.html)
      * [ 7.2.2. Driving TurtleBot via ROS2 messages ](tutorial_ros2_drive_turtlebot.html)
      * 7.2.3. ROS2 Cameras
        * 7.2.3.1. Learning Objectives
        * 7.2.3.2. Getting Started
        * 7.2.3.3. Camera Publisher
          * 7.2.3.3.1. Setting Up Cameras
          * 7.2.3.3.2. Building the Graph for a RGB publisher
          * 7.2.3.3.3. Graph Explained
        * 7.2.3.4. Depth and other Perception Ground Truth data
          * 7.2.3.4.1. Verifying ROS connection
        * 7.2.3.5. Troubleshooting
        * 7.2.3.6. Additional Publishing Options
        * 7.2.3.7. Summary
          * 7.2.3.7.1. Next Steps
          * 7.2.3.7.2. Further Learning
      * [ 7.2.4. Publishing Camera’s Data ](tutorial_ros2_camera_publishing.html)
      * [ 7.2.5. ROS2 Clock ](tutorial_ros2_clock.html)
      * [ 7.2.6. RTX Lidar Sensors ](tutorial_ros2_rtx_lidar.html)
      * [ 7.2.7. ROS2 Transform Trees and Odometry ](tutorial_ros2_tf.html)
      * [ 7.2.8. ROS2 Navigation ](tutorial_ros2_navigation.html)
      * [ 7.2.9. Multiple Robot ROS2 Navigation ](tutorial_ros2_multi_navigation.html)
      * [ 7.2.10. ROS2 Joint Control: Extension Python Scripting ](tutorial_ros2_manipulation.html)
      * [ 7.2.11. MoveIt 2 ](tutorial_ros2_moveit.html)
      * [ 7.2.12. ROS2 Bridge in Standalone Workflow ](tutorial_ros2_python.html)
      * [ 7.2.13. ROS 2 Python Custom Messages ](tutorial_ros2_custom_message_python.html)
      * [ 7.2.14. ROS 2 Navigation with Block World Generator ](tutorial_ros2_navigation_block_world.html)
      * [ 7.2.15. ROS 2 Custom C++ OmniGraph Node ](tutorial_ros2_omnigraph_cpp_node.html)
      * [ 7.2.16. Interfacing with NVIDIA Isaac ROS GEMs ](tutorial_external_ros_gems.html)
  * [ 8\. Mapping ](../advanced_tutorials/tutorials_mapping.html)
  * [ 9\. Isaac Gym ](../isaac_gym_tutorials/index.html)


This will stream relevant camera sensors and publish on the appropriate ROS topics.


  * The subscriber to the ` right_image ` topic calls the ` imageRightRectCallback ` function when it receives a message of type ` sensor_msgs/Image ` that matches that topic
  * The subscriber to the ` left_image ` topic calls the ` imageLeftRectCallback ` function when it receives a message of type ` sensor_msgs/Image ` that matches that topic


###  Camera  #


RGBD new topic, publishing [RGB + Depth] in the same message (see RGBD.msg for reference). For now, works only with depth aligned to color images, as color and depth images are synchronized by frame time tag.


###  The code  #


](https://medium.com/business?source=post_page-----
3b18903e7329--------------------------------)


Key parameters:


#### Parameters that can be modified during runtime:
- All of the filters and sensors inner parameters.
- Video Sensor Parameters: (```depth_module``` and ```rgb_camera```)
  - They have, at least, the **<stream_type>_profile** parameter.
    - The profile parameter is a string of the following format: \<width>X\<height>X\<fps> (The dividing character can be X, x or ",". Spaces are ignored.)
    - For example: ```depth_module.depth_profile:=640x480x30 depth_module.infra_profile:=640x480x30 rgb_camera.color_profile:=1280x720x30```
    - Note: The param **depth_module.infra_profile** is common for all infra streams. i.e., infra 0, 1 & 2.
    - If the specified combination of parameters is not available by the device, the default or previously set configuration will be used.
      - Run ```ros2 param describe <your_node_name> <param_name>``` to get the list of supported profiles.
    - Note: Should re-enable the stream for the change to take effect.
  - ***<stream_name>*_format**
    - This parameter is a string used to select the stream format.
    - <stream_name> can be any of *infra, infra1, infra2, color, depth*.
    - For example: ```depth_module.depth_format:=Z16 depth_module.infra1_format:=y8 rgb_camera.color_format:=RGB8```
    - This parameter supports both lower case and upper case letters.
    - If the specified parameter is not available by the stream, the default or previously set configuration will be used.
      - Run ```ros2 param describe <your_node_name> <param_name>``` to get the list of supported formats.
    - Note: Should re-enable the stream for the change to take effect.
  - If the stream doesn't support the user selected profile \<width>X\<height>X\<fps> + \<format>, it will not be opened and a warning message will be shown.
    - Should update the profile settings and re-enable the stream for the change to take effect.
    - Run ```rs-enumerate-devices``` command to know the list of profiles supported by the connected sensors.
- **enable_*<stream_name>***:
  - Choose whether to enable a specified stream or not. Default is true for images and false for orientation streams.
  - <stream_name> can be any of *infra, infra1, infra2, color, depth, gyro, accel*.
  - For example: ```enable_infra1:=true enable_color:=false```
- **enable_sync**:
  - gathers closest frames of different sensors, infra red, color and depth, to be sent with the same timetag.
  - This happens automatically when such filters as pointcloud are enabled.
- ***<stream_type>*_qos**:
  - Sets the QoS by which the topic is published.
  - <stream_type> can be any of *infra, infra1, infra2, color, depth, gyro, accel*.
  -  Available values are the following strings: `SYSTEM_DEFAULT`, `DEFAULT`, `PARAMETER_EVENTS`, `SERVICES_DEFAULT`, `PARAMETERS`, `SENSOR_DATA`.
  - For example: ```depth_qos:=SENSOR_DATA```
  - Pointcloud QoS is controlled with the `pointcloud.pointcloud_qos` parameter in the pointcloud filter, refer to the Post-Processing Filters section for details.
  - Reference: [ROS2 QoS profiles formal documentation](https://docs.ros.org/en/rolling/Concepts/About-Quality-of-Service-Settings.html#qos-profiles)
- **Notice:** ***<stream_type>*_info_qos** refers to both camera_info topics and metadata topics.
- **tf_publish_rate**:
  - double, rate (in Hz) at which dynamic transforms are published
  - Default value is 0.0 Hz *(means no dynamic TF)*
  - This param also depends on **publish_tf** param


> **Note** : The code of this tutorial instantiates a ` rclcpp::Node ` without
> **subclassing** it. This was the typical usage model in the original **ROS,
> but unfortunately, this style of coding is not compatible with composing
> multiple nodes into a single process. Thus, it is no longer the recommended
> style for ROS 2. Please refer to the “ [ Depth sensing
> ](https://www.stereolabs.com/docs/ros2/depth-sensing/#depth-subscribing-
> in-c) ” tutorial for C++ tutorials written with a coding style more targeted
> to ROS 2.


* BoundingBox 3D


For getting a sense of the latency reduction, a frame latency reporter tool is available via a launch file.
The launch file loads the wrapper and a frame latency reporter tool component into a single container (so the same process).
The tool prints out the frame latency (`now - frame.timestamp`) per frame.


© Copyright 2023-2024, NVIDIA.  Last updated on Apr 15, 2024.


    #include <rclcpp/rclcpp.hpp>
    #include <rclcpp/qos.hpp>
    #include <sensor_msgs/msg/image.hpp>

    rclcpp::Node::SharedPtr g_node = nullptr;

    /**
     * Subscriber callbacks. The argument of the callback is a constant pointer to the received message
     */

    void imageRightRectifiedCallback(const sensor_msgs::msg::Image::SharedPtr msg) {
        RCLCPP_INFO(g_node->get_logger(),
                    "Right Rectified image received from ZED\tSize: %dx%d - Timestamp: %u.%u sec ",
                    msg->width, msg->height,
                    msg->header.stamp.sec,msg->header.stamp.nanosec);
    }

    void imageLeftRectifiedCallback(const sensor_msgs::msg::Image::SharedPtr msg) {
        RCLCPP_INFO(g_node->get_logger(),
                    "Left  Rectified image received from ZED\tSize: %dx%d - Timestamp: %u.%u sec ",
                    msg->width, msg->height,
                    msg->header.stamp.sec,msg->header.stamp.nanosec);
    }

    int main(int argc, char* argv[]) {
        rclcpp::init(argc, argv);

        // Create the node
        g_node = rclcpp::Node::make_shared("zed_video_tutorial");

        /* Note: it is very important to use a QOS profile for the subscriber that is compatible
         * with the QOS profile of the publisher.
         * The ZED component node uses a default QoS profile with reliability set as "RELIABLE"
         * and durability set as "VOLATILE".
         * To be able to receive the subscribed topic the subscriber must use compatible
         * parameters.
         */


To publish images on demand or periodically at a specified rate, you will need
to use Python scripting. Go to [ ROS2 Camera
](tutorial_ros2_python.html#isaac-sim-app-tutorial-ros2-python-camera) for
examples.


    . install/local_setup.bash
    ros2 run opencv_cam opencv_cam_main --ros-args --param index:=4

In a separate terminal, double check that the topic is created and creates
data:

    $ . install/local_setup.bash$ ros2 topic list
    /image_raw
    /parameter_events
    /rosout$ ros2 topic echo --no-arr /image_raw
    header:
      stamp:
        sec: 1610063135
        nanosec: 248209953
      frame_id: camera_frame
    height: 720
    width: 1280
    encoding: bgr8
    is_bigendian: 0
    step: 3840
    data: '<sequence type: uint8, length: 2764800>'
    ---

It looks like the camera is sending ` Image ` data to the ` /image_raw `
topic. Let’s view this data using the image viewer

    ros2 run image_view image_view --ros-args --remap /image:=/image_raw

taa-daa! you should see the contents of the camera in the image viewer

bottom of my tea pot

#  Next Step: Calibration


  * [ Scene Setup ](../how_to_guides/environment_setup.html)
  * [ Robot Simulation ](../how_to_guides/robots_simulation.html)
  * [ Sensor Simulation ](../how_to_guides/sensors_simulation.html)
  * [ Rendering ](../how_to_guides/tutorial_advanced_live_data.html)
  * [ Frequently Used Snippets ](../how_to_guides/reference_python_snippets.html)


- TF msg expresses a transform from coordinate frame "header.frame_id" (source) to the coordinate frame child_frame_id (destination) [Reference](http://docs.ros.org/en/noetic/api/geometry_msgs/html/msg/Transform.html)
- In RealSense cameras, the origin point (0,0,0) is taken from the left IR (infra1) position and named as "camera_link" frame
- Depth, left IR and "camera_link" coordinates converge together.
- Our wrapper provide static TFs between each sensor coordinate to the camera base (camera_link)
- Also, it provides TFs from each sensor ROS coordinates to its corrosponding optical coordinates.
- Example of static TFs of RGB sensor and Infra2 (right infra) sensor of D435i module as it shown in rviz2:
![example](https://user-images.githubusercontent.com/99127997/230148106-0f79cbdb-c401-4d09-b386-a366af18e5f7.png)


* Depth


Further details on efficient intra-process communication can be found [here](https://docs.ros.org/en/humble/Tutorials/Intra-Process-Communication.html#efficient-intra-process-communication).


* Camera Info


The tool is not built unless asked for. Turn on `BUILD_TOOLS` during build to have it available:
```bash
colcon build --cmake-args '-DBUILD_TOOLS=ON'
```


[ ![Logo](../_static/main_ov_logo_rect.png) ](../../../index.html)


* **Isaac Create Render Product** : Creating a render product prim which acquires the rendered data from the given camera prim and outputs the path to the render product prim. Rendering can be enabled/disabled on command by checking/unchecking the ` enabled  ` field.


##  7.2.3.5.  Troubleshooting  


##  7.2.3.6.  Additional Publishing Options  


  Written by  Jeff Gensler

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# Installation on Windows
  **PLEASE PAY ATTENTION: RealSense ROS2 Wrapper is not meant to be supported on Windows by our team, since ROS2 and its packages are still not fully supported over Windows. We added these installation steps below in order to try and make it easier for users who already started working with ROS2 on Windows and want to take advantage of the capabilities of our RealSense cameras**


[ ](https://github.com/stereolabs/zed-sdk "GitHub")


# Installation on Ubuntu

<details>
  <summary>
    Step 1: Install the ROS2 distribution
  </summary>

- #### Ubuntu 22.04:
  - [ROS2 Iron](https://docs.ros.org/en/iron/Installation/Ubuntu-Install-Debians.html)
  - [ROS2 Humble](https://docs.ros.org/en/humble/Installation/Ubuntu-Install-Debians.html)
  #### Ubuntu 20.04
	- [ROS2 Foxy](https://docs.ros.org/en/foxy/Installation/Ubuntu-Install-Debians.html)
</details>

<details>
  <summary>
    Step 2: Install latest Intel® RealSense™ SDK 2.0
  </summary>


<hr>


**Camera Helper Node**


Each Camera Helper node can only retrieve one type of data. You can indicate
which type you wish to assign to the node in the dropdown menu for the field `
type  ` in the Camera Helper Node’s Property tab.


###  7.2.3.7.2.  Further Learning  


[ ![image](https://global.discourse-
cdn.com/nvidia/optimized/3X/7/1/71b9a70eeb28d37ce139c911c289a3731bd93171_2_690x291.jpeg)
image  1920×810 48.1 KB  ](https://global.discourse-
cdn.com/nvidia/original/3X/7/1/71b9a70eeb28d37ce139c911c289a3731bd93171.jpeg
"image")


[ @Hammad_M ](/u/hammad_m) Can you help to respond this thread? [ @phr0201
](/u/phr0201) can elaborate more on the question if this is not clear to you.
She is looking for a way to set FPS Limit forthe camera topic. Thank you!


##  7.2.3.2.  Getting Started  


[ phr0201  ](https://forums.developer.nvidia.com/u/phr0201) November 8, 2022,
4:48am  5


###  7.2.3.3.2.  Building the Graph for a RGB publisher  


using my phone and later a laptop as I don’t have a printer. This seemed to
work fine for unblocking the project but I would also like to revisit a more
“correct” way to calibrate the camera with a larger checkerboard.

After moving your checkerboard around (X, Y, depth (Z), skew), the “calibrate”
button will light up and the calibration program will output the ` CameraInfo
` to the terminal in the ` .ini ` format. I have had some weird cases where
Ubuntu thought the program needed to be killed (maybe because it hadn’t
updated rendering in ~60 seconds) so just give the program a bit more time
(~1/2 minutes). Here is an example of one of my calibration attempts:

    [image]width
    1280height
    720[narrow_stereo]
    camera matrix
    829.298086 0.000000 664.242945
    0.000000 826.302904 319.057782
    0.000000 0.000000 1.000000distortion
    -0.320171 0.099254 0.000692 0.002468 0.000000rectification
    1.000000 0.000000 0.000000
    0.000000 1.000000 0.000000
    0.000000 0.000000 1.000000projection
    647.238342 0.000000 683.202218 0.000000
    0.000000 768.937317 313.070606 0.000000
    0.000000 0.000000 1.000000 0.000000

I saved this file in the root of the ROS workspace. After you’ve saved the
file, you can re-run the ` opencv_cam ` node so that you’ll also publish the
camera info:

    ros2 run opencv_cam opencv_cam_main --ros-args --param index:=4 --param camera_info_path:=camera-info-left-5.ini

Now, you’ll see the ` /camera_info ` topic:


* Add additional cameras to the scene and onto the robot


###  Running the tutorial  #


  * [ Isaac Sim API Documentation ](../reference_python_api.html)
  * [ Omniverse API Documentation ](../reference_python_api.html#omniverse-api-documentation)


>
> ![../_images/isaac_tutorial_ros_camera_add_viewport.gif](../_images/isaac_tutorial_ros_camera_add_viewport.gif)


* BoundingBox 2D Loose


  * [ GStreamer  ](https://www.stereolabs.com/docs/gstreamer/) [ Camera Source ](https://www.stereolabs.com/docs/gstreamer/zed-camera-source/) [ Metadata ](https://www.stereolabs.com/docs/gstreamer/zed-metadata/) [ Demuxer ](https://www.stereolabs.com/docs/gstreamer/zed-demux/) [ Sensors CSV Sink ](https://www.stereolabs.com/docs/gstreamer/zed-data-csv-sink/) [ Object Detection Overlay ](https://www.stereolabs.com/docs/gstreamer/zed-od-overlay/) [ Data muxer ](https://www.stereolabs.com/docs/gstreamer/zed-datamux/) [ RTSP Server ](https://www.stereolabs.com/docs/gstreamer/rtsp-server/)
  * [ Matlab  ](https://www.stereolabs.com/docs/matlab/)
  * [ OpenNI2  ](https://www.stereolabs.com/docs/openni2/)


How-to Guides


  * [ Legal Information ](https://www.nvidia.com/en-us/about-nvidia/legal-info/)
  * [ Terms of Use ](https://developer.nvidia.com/legal/terms)
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The tutorial node subscribes generic ` left_image ` and ` right_image `
topics, so a remapping is required to connect to the correct topics published
by the ZED node:


If the ZED node is running and a camera is connected or you have loaded an SVO
file, you will receive the following stream of messages confirming that you
have correctly subscribed to the ZED image topics:


Key parameters:


  Stereo Vision Calibration

To begin, start two of the ` opencv_cam ` nodes:

    $ ros2 run opencv_cam opencv_cam_main \
      --ros-args --param index:=4 \
      --remap __ns:=/left \
      --remap /left/image_raw:=/left/image_rect$ ros2 run opencv_cam opencv_cam_main \
      --ros-args --param index:=6 \
      --remap __ns:=/right \
      --remap /right/image_raw:=/right/image_rect

You should have the following topics:

    $ ros2 topic list
    /left/camera_info
    /left/image_rect
    /parameter_events
    /right/camera_info
    /right/image_rect
    /rosout

Now, run the calibration program and supply args to make it run in stereo
mode:

    ros2 run camera_calibration cameracalibrator \
      --size=8x6 \
      --square=0.063 \
      --approximate=0.3 \
      --no-service-check \
      --ros-args --remap /left:=/left/image_rect \
      --remap /right:=/right/image_rect

view of stereo camera calibration


* Instance Labels


The most important lesson of the above code is how the subscribers are
defined:


###  7.2.3.3.3.  Graph Explained  


  * Getting Started
  * [ Get Started With ZED  ](https://www.stereolabs.com/docs/get-started-with-zed/)
  * [ Get Started With ZED X  ](https://www.stereolabs.com/docs/get-started-with-zed-x/) [ Setup for ZED Box Orin NX ](https://www.stereolabs.com/docs/get-started-with-zed-x/zed-box-orin-nx-setup/) [ Setup for AGX Orin / Xavier ](https://www.stereolabs.com/docs/get-started-with-zed-x/jetson-agx-devkit-setup/) [ Setup for Xavier NX DevKits ](https://www.stereolabs.com/docs/get-started-with-zed-x/jetson-xavier-nx-devkit-setup/) [ Setup for Orin Nano / NX DevKits ](https://www.stereolabs.com/docs/get-started-with-zed-x/jetson-orin-devkit-setup/) [ Developing with ZED X on a PC ](https://www.stereolabs.com/docs/get-started-with-zed-x/zed-x-dev-on-pc/) [ Troubleshooting ](https://www.stereolabs.com/docs/get-started-with-zed-x/troubleshooting/) [ Advanced Topics ](https://www.stereolabs.com/docs/get-started-with-zed-x/advanced-topics/)
  * [ Get Started With ZED X One  ](https://www.stereolabs.com/docs/get-started-with-zed-x-one/) [ Setting up the ZED X One Monocular ](https://www.stereolabs.com/docs/get-started-with-zed-x-one/zed-x-one-mono/) [ Setting up the ZED X One Stereo ](https://www.stereolabs.com/docs/get-started-with-zed-x-one/zed-x-one-stereo/) [ Developing with ZED X One Stereo on a PC ](https://www.stereolabs.com/docs/get-started-with-zed-x-one/zed-x-dev-on-pc/) [ Troubleshooting ](https://www.stereolabs.com/docs/get-started-with-zed-x-one/troubleshooting/) [ Advanced Topics ](https://www.stereolabs.com/docs/get-started-with-zed-x-one/advanced-topics/)
  * [ Get Started With ZED Box  ](https://www.stereolabs.com/docs/get-started-with-zed-box/)
  * [ Get Started With ZED Box Orin  ](https://www.stereolabs.com/docs/get-started-with-zed-box-orin-nx/) [ Installation ](https://www.stereolabs.com/docs/get-started-with-zed-box-orin-nx/installation/) [ Reset ](https://www.stereolabs.com/docs/get-started-with-zed-box-orin-nx/reset-update/) [ Using GMSL ](https://www.stereolabs.com/docs/get-started-with-zed-box-orin-nx/gmsl/) [ Using GNSS ](https://www.stereolabs.com/docs/get-started-with-zed-box-orin-nx/gnss/) [ Troubleshooting ](https://www.stereolabs.com/docs/get-started-with-zed-box-orin-nx/troubleshooting/)
  * [ Installation  ](https://www.stereolabs.com/docs/installation/) [ Install on Windows ](https://www.stereolabs.com/docs/installation/windows/) [ Install on Linux ](https://www.stereolabs.com/docs/installation/linux/) [ Install on NVIDIA® Jetson ](https://www.stereolabs.com/docs/installation/jetson/) [ Docker ](https://www.stereolabs.com/docs/installation/docker/) [ Recommended Specifications ](https://www.stereolabs.com/docs/installation/specifications/)
  * [ C++ Development  ](https://www.stereolabs.com/docs/app-development/cpp/) [ Building on Windows ](https://www.stereolabs.com/docs/app-development/cpp/windows/) [ Building on Linux and NVIDIA® Jetson ](https://www.stereolabs.com/docs/app-development/cpp/linux/)
  * [ Python Development  ](https://www.stereolabs.com/docs/app-development/python/install/) [ Run a Python Application ](https://www.stereolabs.com/docs/app-development/python/run/) [ Python Virtual Environment ](https://www.stereolabs.com/docs/app-development/python/virtual_env/)
  * [ C# Development  ](https://www.stereolabs.com/docs/app-development/csharp/use/)
  * [ C Development  ](https://www.stereolabs.com/docs/app-development/c/install/) [ Run a C Application ](https://www.stereolabs.com/docs/app-development/c/run/)
  * SDK Overview
  * [ Camera  ](https://www.stereolabs.com/docs/video/) [ Camera Controls ](https://www.stereolabs.com/docs/video/camera-controls/) [ Camera Calibration ](https://www.stereolabs.com/docs/video/camera-calibration/) [ Video Recording ](https://www.stereolabs.com/docs/video/recording/) [ Local Streaming ](https://www.stereolabs.com/docs/video/streaming/) [ Multi-Camera ](https://www.stereolabs.com/docs/video/multi-camera/) [ Using the API ](https://www.stereolabs.com/docs/video/using-video/)
  * [ Sensors  ](https://www.stereolabs.com/docs/sensors/) [ IMU ](https://www.stereolabs.com/docs/sensors/imu/) [ Magnetometer ](https://www.stereolabs.com/docs/sensors/magnetometer/) [ Barometer ](https://www.stereolabs.com/docs/sensors/barometer/) [ Temperature Sensors ](https://www.stereolabs.com/docs/sensors/temperature/) [ Time Synchronization ](https://www.stereolabs.com/docs/sensors/time-synchronization/) [ Using the API ](https://www.stereolabs.com/docs/sensors/using-sensors/)
  * [ Depth Sensing  ](https://www.stereolabs.com/docs/depth-sensing/) [ Depth Settings ](https://www.stereolabs.com/docs/depth-sensing/depth-settings/) [ Confidence Filtering ](https://www.stereolabs.com/docs/depth-sensing/confidence-filtering/) [ Using the API ](https://www.stereolabs.com/docs/depth-sensing/using-depth/)
  * [ Positional Tracking  ](https://www.stereolabs.com/docs/positional-tracking/) [ Relocalization ](https://www.stereolabs.com/docs/positional-tracking/area-memory/) [ Coordinate Frames ](https://www.stereolabs.com/docs/positional-tracking/coordinate-frames/) [ Use Cases ](https://www.stereolabs.com/docs/positional-tracking/use-cases/) [ Using the API ](https://www.stereolabs.com/docs/positional-tracking/using-tracking/)
  * [ Global Localization  ](https://www.stereolabs.com/docs/global-localization/) [ Setting up GNSS / RTK ](https://www.stereolabs.com/docs/global-localization/using-gnss-linux/)
  * [ Spatial Mapping  ](https://www.stereolabs.com/docs/spatial-mapping/) [ Using the API ](https://www.stereolabs.com/docs/spatial-mapping/using-mapping/) [ Plane Detection ](https://www.stereolabs.com/docs/spatial-mapping/plane-detection/)
  * [ Object Detection  ](https://www.stereolabs.com/docs/object-detection/) [ Using the API ](https://www.stereolabs.com/docs/object-detection/using-object-detection/) [ Custom Detector ](https://www.stereolabs.com/docs/object-detection/custom-od/)
  * [ Body Tracking  ](https://www.stereolabs.com/docs/body-tracking/) [ Using the API ](https://www.stereolabs.com/docs/body-tracking/using-body-tracking/)
  * [ Fusion  ](https://www.stereolabs.com/docs/fusion/overview/) [ ZED360 ](https://www.stereolabs.com/docs/fusion/zed360/)
  * Code Samples
  * [ Tutorials  ](https://www.stereolabs.com/docs/tutorials/) [ Tutorial - Hello ZED ](https://www.stereolabs.com/docs/tutorials/hello-zed/) [ Tutorial - Image Capture ](https://www.stereolabs.com/docs/tutorials/image-capture/) [ Tutorial - Depth Perception ](https://www.stereolabs.com/docs/tutorials/depth-sensing/) [ Tutorial - Camera Tracking ](https://www.stereolabs.com/docs/tutorials/positional-tracking/) [ Tutorial - Spatial Mapping ](https://www.stereolabs.com/docs/tutorials/spatial-mapping/) [ Tutorial - Object Detection ](https://www.stereolabs.com/docs/tutorials/3d-object-detection/) [ Tutorial - Using Sensors ](https://www.stereolabs.com/docs/tutorials/using-sensors/) [ Tutorial - Body Tracking ](https://www.stereolabs.com/docs/tutorials/body-tracking/)
  * [ Samples  ](https://www.stereolabs.com/docs/samples/)
  * Integrations
  * [ Overview  ](https://www.stereolabs.com/docs/integrations/)
  * [ ROS  ](https://www.stereolabs.com/docs/ros/) [ ZED Node ](https://www.stereolabs.com/docs/ros/zed-node/) [ ZED Nodelets ](https://www.stereolabs.com/docs/ros/zed-nodelets/) [ Data Display with Rviz ](https://www.stereolabs.com/docs/ros/rviz/) [ Video Capture ](https://www.stereolabs.com/docs/ros/video/) [ Depth Perception ](https://www.stereolabs.com/docs/ros/depth-sensing/) [ Positional Tracking ](https://www.stereolabs.com/docs/ros/positional-tracking/) [ Plane Detection ](https://www.stereolabs.com/docs/ros/plane_detection/) [ Object Detection ](https://www.stereolabs.com/docs/ros/object-detection/) [ Getting Sensor Data ](https://www.stereolabs.com/docs/ros/sensor-data/)
  * [ ROS 2  ](https://www.stereolabs.com/docs/ros2/) [ ZED Node ](https://www.stereolabs.com/docs/ros2/zed-node/) [ Data Display with Rviz2 ](https://www.stereolabs.com/docs/ros2/rviz2/) [ Video Capture ](https://www.stereolabs.com/docs/ros2/video/) [ Depth Perception ](https://www.stereolabs.com/docs/ros2/depth-sensing/) [ Positional Tracking ](https://www.stereolabs.com/docs/ros2/positional-tracking/) [ Geo Tracking ](https://www.stereolabs.com/docs/ros2/geo-tracking/) [ Plane Detection ](https://www.stereolabs.com/docs/ros2/plane-detection/) [ Object Detection ](https://www.stereolabs.com/docs/ros2/object-detection/) [ Body Tracking ](https://www.stereolabs.com/docs/ros2/body-tracking/) [ Region of Interest ](https://www.stereolabs.com/docs/ros2/region-of-interest/) [ Custom messages ](https://www.stereolabs.com/docs/ros2/custom-msgs/) [ Composition ](https://www.stereolabs.com/docs/ros2/ros2-composition/) [ Robot Integration ](https://www.stereolabs.com/docs/ros2/ros2-robot-integration/)
  * [ Isaac Sim  ](https://www.stereolabs.com/docs/isaac-sim/) [ Getting started with Isaac Sim ](https://www.stereolabs.com/docs/isaac-sim/isaac_sim/) [ Setting up ZED in Isaac Sim ](https://www.stereolabs.com/docs/isaac-sim/setting_up_zed_isaac_sim/) [ Using ZED with ROS 2 and Isaac Sim ](https://www.stereolabs.com/docs/isaac-sim/ros2_integration/)
  * [ OpenCV  ](https://www.stereolabs.com/docs/opencv/) [ Python Interface ](https://www.stereolabs.com/docs/opencv/python/) [ OpenCV Calibration ](https://www.stereolabs.com/docs/opencv/calibration/)
  * [ PyTorch  ](https://www.stereolabs.com/docs/pytorch/)
  * [ YOLO  ](https://www.stereolabs.com/docs/yolo/) [ YOLO_python ](https://www.stereolabs.com/docs/yolo/python/)
  * [ Docker  ](https://www.stereolabs.com/docs/docker/) [ Install Guide on Linux ](https://www.stereolabs.com/docs/docker/install-guide-linux/) [ Install Guide on NVIDIA® Jetson ](https://www.stereolabs.com/docs/docker/install-guide-jetson/) [ Creating a Docker Image ](https://www.stereolabs.com/docs/docker/creating-your-image/) [ Orchestrate containers ](https://www.stereolabs.com/docs/docker/orchestration/) [ Using OpenCV ](https://www.stereolabs.com/docs/docker/using-opencv/) [ Create an OpenCV image ](https://www.stereolabs.com/docs/docker/configure-opencv-dockerfile/) [ Using ROS/2 ](https://www.stereolabs.com/docs/docker/using-ros/) [ Create a ROS image ](https://www.stereolabs.com/docs/docker/configure-ros-dockerfile/) [ Create a ROS 2 image ](https://www.stereolabs.com/docs/docker/configure-ros2-dockerfile/) [ Building Images for NVIDIA® Jetson ](https://www.stereolabs.com/docs/docker/building-arm-container-on-x86/)
  * [ Unity  ](https://www.stereolabs.com/docs/unity/) [ Key Concepts & Scripts ](https://www.stereolabs.com/docs/unity/basic-concepts/) [ Building Applications ](https://www.stereolabs.com/docs/unity/building-applications-with-zed/) [ AR Video Passthrough ](https://www.stereolabs.com/docs/unity/video-passthrough/) [ Build Your First AR/MR App ](https://www.stereolabs.com/docs/unity/creating-mixed-reality-app/) [ Body Tracking ](https://www.stereolabs.com/docs/unity/body-tracking/) [ Spatial Mapping ](https://www.stereolabs.com/docs/unity/spatial-mapping-unity/) [ Mixed Reality Capture ](https://www.stereolabs.com/docs/unity/mixed-reality-capture/) [ Object Detection ](https://www.stereolabs.com/docs/unity/object-detection/) [ Lighting and Shadows ](https://www.stereolabs.com/docs/unity/lighting/) [ Motion Controllers in AR ](https://www.stereolabs.com/docs/unity/motion-controllers-passthrough/) [ Object Placement ](https://www.stereolabs.com/docs/unity/object-placement/) [ Using OpenCV in Unity ](https://www.stereolabs.com/docs/unity/using-opencv-with-unity/) [ Green Screen VR Capture ](https://www.stereolabs.com/docs/unity/green-screen-vr/) [ Sample scenes ](https://www.stereolabs.com/docs/unity/samples/)
  * [ Unreal Engine 5  ](https://www.stereolabs.com/docs/ue5/) [ Configuring your Project ](https://www.stereolabs.com/docs/ue5/configuring-your-project/) [ Body Tracking ](https://www.stereolabs.com/docs/ue5/body-tracking/) [ Camera Tracking ](https://www.stereolabs.com/docs/ue5/camera-tracking/) [ AR/MR ](https://www.stereolabs.com/docs/ue5/ar_mr_app/) [ Spatial Mapping ](https://www.stereolabs.com/docs/ue5/spatial-mapping/) [ Object Detection ](https://www.stereolabs.com/docs/ue5/object-detection/) [ Background Subtraction ](https://www.stereolabs.com/docs/ue5/background-subtraction/)
  * [ Live Link  ](https://www.stereolabs.com/docs/livelink/)
    * [ Live Link for UE5  arrow  ](https://www.stereolabs.com/docs/livelink/livelink-ue5/) [ Animating New Avatars ](https://www.stereolabs.com/docs/livelink/animate-new-avatar/) [ Building ZED Live Link ](https://www.stereolabs.com/docs/livelink/building-the-plugin/)
[ Live Link for Unity ](https://www.stereolabs.com/docs/livelink/livelink-
unity/)


[ smoon  ](https://forums.developer.nvidia.com/u/smoon) November 7, 2022,
2:52am  3


**Prerequisite**


Open a new console and use this command to connect the camera to the ROS 2
network:


    void imageRightRectifiedCallback(const sensor_msgs::msg::Image::SharedPtr msg) {
        RCLCPP_INFO(g_node->get_logger(),
                    "Right Rectified image received from ZED\tSize: %dx%d - Timestamp: %u.%u sec ",
                    msg->width, msg->height,
                    msg->header.stamp.sec,msg->header.stamp.nanosec);
    }

    void imageLeftRectifiedCallback(const sensor_msgs::msg::Image::SharedPtr msg) {
        RCLCPP_INFO(g_node->get_logger(),
                    "Left  Rectified image received from ZED\tSize: %dx%d - Timestamp: %u.%u sec ",
                    msg->width, msg->height,
                    msg->header.stamp.sec,msg->header.stamp.nanosec);
    }


The ` Camera ` plugin allows you to visualize an image from a topic of type `
sensor_msgs/Image ` . It acts as if the source of the image is placed on its
virtual frame and renders all virtual objects in front of it.


###  7.2.3.3.1.  Setting Up Cameras  


##  7.2.3.1.  Learning Objectives  


<hr>


  - Defaults to True
    - So, static TFs will be published by default
    - If dynamic TFs are needed, user should set the param **tf_publish_rate** to >0.0 Hz
  - If set to false, both static and dynamic TFs won't be published, even if the param **tf_publish_rate** is set to >0.0 Hz
- **diagnostics_period**:
  - double, positive values set the period between diagnostics updates on the `/diagnostics` topic.
  - 0 or negative values mean no diagnostics topic is published. Defaults to 0.</br>
The `/diagnostics` topic includes information regarding the device temperatures and actual frequency of the enabled streams.


- Extrinsic msg is made up of two parts:
  - float64[9] rotation  (Column - major 3x3 rotation matrix)
  - float64[3] translation  (Three-element translation vector, in meters)


[ Hammad_M  ](https://forums.developer.nvidia.com/u/Hammad_M) November 8,
2022, 6:31pm  6


By the way, Your examples are all based on **ros1** , but i want to use
**ros2.**
Because i want to get the **30 fps camera topics** (e.g /carter1/rgb_left) in
**isaac** **sim (2022.1.1 release version)** based on ros2.
Is it possible applying above examples in ros2 environment?


###  7.2.3.7.1.  Next Steps  


By expanding ` Visibility ` , you can select/deselect what will be visible in
the ` camera view ` . Only active plugins can be selected.


- `enable_rgbd`: new paramter, to enable/disable rgbd topic, changeable during runtime
- `align_depth.enable`: align depth images to rgb images
- `enable_sync`: let librealsense sync between frames, and get the frameset with color and depth images combined
- `enable_color` + `enable_depth`: enable both color and depth sensors


A ` rclcpp::Subscription ` is a ROS object that listens to the network and
waits for its own topic message to be available. When a message is received,
it executes the callback assigned to it.


Also take a look at ` standalone_examples/api/omni.isaac.core/time_stepping.py
`


Once you specify a type for a Camera Helper node and activated it (i.e.
started simulation and the underlying SDGPipeline has been generated), you
cannot change the type and reuse the node. You can either use a new node, or
reload your stage and regenerate the SDGPipeline with the modified type.


### Latency test tool and launch file


is then manually run each physics step:


- ROS2 Coordinate System: (X: Forward, Y:Left, Z: Up)
- Camera Optical Coordinate System: (X: Right, Y: Down, Z: Forward)
- References: [REP-0103](https://www.ros.org/reps/rep-0103.html#coordinate-frame-conventions), [REP-0105](https://www.ros.org/reps/rep-0105.html#coordinate-frames)
- All data published in our wrapper topics is optical data taken directly from our camera sensors.
- static and dynamic TF topics publish optical CS and ROS CS to give the user the ability to move from one CS to other CS.


The full source code of this tutorial is available on GitHub in the [
zed_video_tutorial ](https://github.com/stereolabs/zed-
ros2-examples/tree/master/tutorials/zed_video_tutorial) sub-package.


* Add camera publishers


3. Is it possible to get the topic of fixed fps if i use the **Enterprise version** ?


[ Hammad_M  ](https://forums.developer.nvidia.com/u/Hammad_M) November 8,
2022, 3:30am  4


> ros2 topic list
/camera/camera/color/camera_info
/camera/camera/color/image_raw
/camera/camera/color/metadata
/camera/camera/depth/camera_info
/camera/camera/depth/image_rect_raw
/camera/camera/depth/metadata
/camera/camera/extrinsics/depth_to_color
/camera/camera/imu


#  [ ROS2 Camera topic publish Hz control (Camera FPS Fix problem using ros2)
](/t/ros2-camera-topic-publish-hz-control-camera-fps-fix-problem-using-
ros2/231024)


  * Video with RVIZ2
  * Video subscribing in C++


In this case, the QOS profile is configured to keep the last received 10
messages with “best effort” reliability and “volatile” durability. This
configuration is highly compatible with many possible publisher
configurations.


  Visualize the Point Cloud

Finally, we are ready to view the PointCloud in Rviz2:

    $ rviz2

To add the PointCloud, click ` Add ` . Select ` By topic ` > ` /points2 ` > `
PointCloud2 ` and then click ` OK ` .

You’ll then see the following in the ` rviz2 ` terminal’s output:

    [INFO] [1610071478.345338507] [rviz]: Message Filter dropping message: frame 'camera_frame' at time 1610071477.160 for reason 'Unknown'

This is because the the point’s x/y/z coordinates are relative to the left
camera but the left camer’s “frame” does not exist in Rviz’s “world.”
Fortunately, we can fake the position of a frame using the following:

    $ ros2 run tf2_ros static_transform_publisher \
      0 0 4 \
      0 1.5708 1.5708 \
      test_frame_id \
      test_child_frame_id

Next, we need our left camera to reference the ` test_frame_id ` . This
information is located in the ` Header ` of all of the messages so you should
be able to use ` ros2 topic echo ` to double check that it propagates. Here is
the updated ` opencv_cam ` command:

    ros2 run opencv_cam opencv_cam_main \
      --ros-args --param index:=6 \
      --param camera_info_path:=camera-info-left-5.ini \
      --remap __ns:=/left \


###  7.2.3.4.1.  Verifying ROS connection  


```
administrator@perclnx466 ~/ros2_humble $ ros2 topic echo /camera/camera/extrinsics/depth_to_color
rotation:
- 0.9999583959579468
- 0.008895332925021648
- -0.0020127370953559875
- -0.008895229548215866
- 0.9999604225158691
- 6.045500049367547e-05
- 0.0020131953060626984
- -4.254872692399658e-05
- 0.9999979734420776
translation:
- 0.01485931035131216
- 0.0010161789832636714
- 0.0005317096947692335
---
```


- Extrinsic from sensor A to sensor B means the position and orientation of sensor A relative to sensor B.
- Imagine that B is the origin (0,0,0), then the Extrensics(A->B) describes where is sensor A relative to sensor B.
- For example, depth_to_color, in D435i:
  - If we look from behind of the D435i, extrinsic from depth to color, means, where is the depth in relative to the color.
  - If we just look at the X coordinates, in the optical coordiantes (again, from behind) and assume that COLOR(RGB) sensor is (0,0,0), we can say that DEPTH sensor is on the right of RGB by 0.0148m (1.48cm).


These boolean paramters should be true to enable rgbd messages:


## RGBD Topic


## Start the camera node

  #### with ros2 run:
    ros2 run realsense2_camera realsense2_camera_node
    # or, with parameters, for example - temporal and spatial filters are enabled:
    ros2 run realsense2_camera realsense2_camera_node --ros-args -p enable_color:=false -p spatial_filter.enable:=true -p temporal_filter.enable:=true

  #### with ros2 launch:
    ros2 launch realsense2_camera rs_launch.py
    ros2 launch realsense2_camera rs_launch.py depth_module.depth_profile:=1280x720x30 pointcloud.enable:=true


###  Image  #


</details>

<details>
  <summary>
    Step 3: Install Intel® RealSense™ ROS2 wrapper
  </summary>

#### Option 1: Install debian package from ROS servers (Foxy EOL distro is not supported by this option):
  - [Configure](http://wiki.ros.org/Installation/Ubuntu/Sources) your Ubuntu repositories
  - Install all realsense ROS packages by ```sudo apt install ros-<ROS_DISTRO>-realsense2-*```
  - For example, for Humble distro: ```sudo apt install ros-humble-realsense2-*```

#### Option 2: Install from source

  - Create a ROS2 workspace
      ```bash
      mkdir -p ~/ros2_ws/src
      cd ~/ros2_ws/src/
      ```

  - Clone the latest ROS2 Intel® RealSense™  wrapper from [here](https://github.com/IntelRealSense/realsense-ros.git) into '~/ros2_ws/src/'
      ```bashrc
      git clone https://github.com/IntelRealSense/realsense-ros.git -b ros2-development
      cd ~/ros2_ws
      ```

  - Install dependencies
   ```bash
   sudo apt-get install python3-rosdep -y
   sudo rosdep init # "sudo rosdep init --include-eol-distros" for Foxy and earlier
   rosdep update # "sudo rosdep update --include-eol-distros" for Foxy and earlier
   rosdep install -i --from-path src --rosdistro $ROS_DISTRO --skip-keys=librealsense2 -y
   ```


The launch file accepts a parameter, `intra_process_comms`, controlling whether zero-copy is turned on or not. Default is on:
```bash
ros2 launch realsense2_camera rs_intra_process_demo_launch.py intra_process_comms:=true
```


Important


First, the ROS 2 environment is initialized with the ` rclcpp::init ` command.
Then, the ` zed_video_tutorial ` node is created and a shared pointer ` g_node
` to it is initialized.


Thank you for replying me!


  How to create Point Clouds using two USB cameras

[ ![Jeff
Gensler](https://miro.medium.com/v2/resize:fill:88:88/1*Irw8Ao7_8-_ON4LpwG-_0Q.png)
](/?source=post_page-----3b18903e7329--------------------------------)

[ Jeff Gensler ](/?source=post_page-----
3b18903e7329--------------------------------)

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Jan 8, 2021

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Listen


3. Build an Action Graph with the nodes and connection of the following image, and parameters using the table below.


2. Click on the _New Action Graph_ Icon in middle of the Action Graph Window, or _Edit Action Graph_ if you want to append the camera publisher to an existing action graph.


Thank you!


##  7.2.3.3.  Camera Publisher  


  Next Steps

After you have PointCloud data, you’ll want other pieces of data that help
represent the state of the robot in a world. Notably:

  * [ IMU data ](https://en.wikipedia.org/wiki/Inertial_measurement_unit) to measure movement
  * More sophisticated joint and frame data

The next step is to explore various [ SLAM
](https://en.wikipedia.org/wiki/Simultaneous_localization_and_mapping)
packages to construct a “map” from the point cloud data. I will explore
collecting IMU and Servo data with [ rosserial
](http://wiki.ros.org/rosserial) to try and understand more about visualizing
and reporting the state of [ a robot arm ](https://www.amazon.com/Lightweight-
Mechanical-Assembled-Raspberry-SNAM1500/dp/B081YH77C3) I purchased a while
back. After, I would like to learn more about the [ MoveIt
](https://moveit.ros.org/) package as well as [ Cartographer ](https://google-
cartographer-ros.readthedocs.io/en/latest/ros_api.html) and [ Octomap
](https://wiki.ros.org/octomap) .

[ Ros2  ](https://medium.com/tag/ros2?source=post_page-----3b18903e7329
---------------ros2-----------------)

[ Computer Vision  ](https://medium.com/tag/computer-vision?source=post_page
-----3b18903e7329---------------computer_vision-----------------)

[ Stereoscopy  ](https://medium.com/tag/stereoscopy?source=post_page-----
3b18903e7329---------------stereoscopy-----------------)

[ Point Cloud  ](https://medium.com/tag/point-cloud?source=post_page-----
3b18903e7329---------------point_cloud-----------------)

[ Stereo Image Proc  ](https://medium.com/tag/stereo-image-
proc?source=post_page-----3b18903e7329---------------
stereo_image_proc-----------------)

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### Available Parameters:
- For the entire list of parameters type `ros2 param list`.
- For reading a parameter value use `ros2 param get <node> <parameter_name>`
  - For example: `ros2 param get /camera/camera depth_module.emitter_enabled`
- For setting a new value for a parameter use `ros2 param set <node> <parameter_name> <value>`
  - For example: `ros2 param set /camera/camera depth_module.emitter_enabled 1`


The ZED node will start to publish image data in the network only if there is
another node that subscribes to the relative topic.


#  Adding Video Capture in ROS 2


  - Result
  ```
  > ros2 node list
  /robot1/D455_1

  > ros2 topic list
  /robot1/D455_1/color/camera_info
  /robot1/D455_1/color/image_raw
  /robot1/D455_1/color/metadata
  /robot1/D455_1/depth/camera_info
  /robot1/D455_1/depth/image_rect_raw
  /robot1/D455_1/depth/metadata
  /robot1/D455_1/extrinsics/depth_to_color
  /robot1/D455_1/imu

  > ros2 service list
  /robot1/D455_1/device_info
  ```


  * [ ](../../../index.html) »
  * [ Omniverse IsaacSim ](../index.html) »
  * [ 7\.  ROS / ROS 2 ](../ros_ros2_tutorials.html) »
  * [ 7.2.  ROS 2 Tutorials (Linux & Windows) ](index.html) »
  * 7.2.3.  ROS2 Cameras
  *   *


In addition to RGB image, the following synthetic sensor and perceptual
information also are available for any camera.


2. Second, I found the RTX Option(real time & interactive & accurate ), **What is differences between them?** When i choose “Interactive option” like below, scene looks like blurry and dark. ( But real-time looks good )


* Additional information about synthetic data generation can be found in the Replicator Tutorial Series.


> **Note** : the old launch command ` $ ros2 launch zed_display_rviz2
> display_<camera model>.launch.py ` is now obsolete and will be removed in a
> future release of the wrapper.


## Published Topics

The published topics differ according to the device and parameters.
After running the above command with D435i attached, the following list of topics will be available (This is a partial list. For full one type `ros2 topic list`):
- /camera/camera/aligned_depth_to_color/camera_info
- /camera/camera/aligned_depth_to_color/image_raw
- /camera/camera/color/camera_info
- /camera/camera/color/image_raw
- /camera/camera/color/metadata
- /camera/camera/depth/camera_info
- /camera/camera/depth/color/points
- /camera/camera/depth/image_rect_raw
- /camera/camera/depth/metadata
- /camera/camera/extrinsics/depth_to_color
- /camera/camera/imu
- /diagnostics
- /parameter_events
- /rosout
- /tf_static


[rolling-badge]: https://img.shields.io/badge/-ROLLING-orange?style=flat-square&logo=ros
[rolling]: https://docs.ros.org/en/rolling/index.html
[foxy-badge]: https://img.shields.io/badge/-foxy-orange?style=flat-square&logo=ros
[foxy]: https://docs.ros.org/en/foxy/index.html
[humble-badge]: https://img.shields.io/badge/-HUMBLE-orange?style=flat-square&logo=ros
[humble]: https://docs.ros.org/en/humble/index.html
[iron-badge]: https://img.shields.io/badge/-IRON-orange?style=flat-square&logo=ros
[iron]: https://docs.ros.org/en/iron/index.html
[ubuntu22-badge]: https://img.shields.io/badge/-UBUNTU%2022%2E04-blue?style=flat-square&logo=ubuntu&logoColor=white
[ubuntu22]: https://releases.ubuntu.com/jammy/
[ubuntu20-badge]: https://img.shields.io/badge/-UBUNTU%2020%2E04-blue?style=flat-square&logo=ubuntu&logoColor=white
[ubuntu20]: https://releases.ubuntu.com/focal/


[ ![image](https://global.discourse-
cdn.com/nvidia/optimized/3X/9/4/946f1fd798db9e1eb49d9d55da4668787e1c02a4_2_500x500.jpeg)
image  1262×1262 109 KB  ](https://global.discourse-
cdn.com/nvidia/original/3X/9/4/946f1fd798db9e1eb49d9d55da4668787e1c02a4.jpeg
"image")


##  7.2.3.7.  Summary  


Enabling stream adds matching topics. For instance, enabling the gyro and accel streams adds the following topics:
- /camera/camera/accel/imu_info
- /camera/camera/accel/metadata
- /camera/camera/accel/sample
- /camera/camera/extrinsics/depth_to_accel
- /camera/camera/extrinsics/depth_to_gyro
- /camera/camera/gyro/imu_info
- /camera/camera/gyro/metadata
- /camera/camera/gyro/sample


[
https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/tutorial_ros_migration.html#periodic-
image-publishing
](https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/tutorial_ros_migration.html#periodic-
image-publishing)


###  The code explained  #


1 Like


$ ros2 run stereolabs_zed_tutorial_video stereolabs_zed_tutorial_video


        rclcpp::QoS video_qos(10);
        video_qos.keep_last(10);
        video_qos.best_effort();
        video_qos.durability_volatile();

        // Create right image subscriber
        auto right_sub = g_node->create_subscription<sensor_msgs::msg::Image>(
                    "right_image", video_qos, imageRightRectifiedCallback );

        // Create left image subscriber
        auto left_sub = g_node->create_subscription<sensor_msgs::msg::Image>(
                    "left_image", video_qos, imageLeftRectifiedCallback );


In this example, we will learn how to


## Extrinsics from sensor A to sensor B:


    [INFO] [zed_video_tutorial]: Left  Rectified image received from ZED	Size: 1280x720 - Timestamp: 1602576933.791896880 sec
    [INFO] [zed_video_tutorial]: Right Rectified image received from ZED	Size: 1280x720 - Timestamp: 1602576933.891931106 sec
    [INFO] [zed_video_tutorial]: Left  Rectified image received from ZED	Size: 1280x720 - Timestamp: 1602576934.91928857 sec
    [INFO] [zed_video_tutorial]: Right Rectified image received from ZED	Size: 1280x720 - Timestamp: 1602576934.91928857 sec
    [INFO] [zed_video_tutorial]: Left  Rectified image received from ZED	Size: 1280x720 - Timestamp: 1602576934.191911460 sec
    [INFO] [zed_video_tutorial]: Right Rectified image received from ZED	Size: 1280x720 - Timestamp: 1602576934.191911460 sec

    [...]


##  Video subscribing in C++  #


  1. At first, I set the **“FPS Limit”** ( =10 _ just test ) But like below figure, It does not apply.
( In figure, FPS Limit is 10, but camera fps published is 22.34 )


    - If **publish_tf:=false**, then no TFs will be published, even if **tf_publish_rate** is >0.0 Hz
    - If **publish_tf:=true** and **tf_publish_rate** set to >0.0 Hz, then dynamic TFs will be published at the specified rate
- **unite_imu_method**:
  - For the D400 cameras with built in IMU components, below 2 unrelated streams (each with it's own frequency) will be created:
    - *gyro* - which shows angular velocity
    - *accel* - which shows linear acceleration.
  - Both streams will publish data to its corresponding topics:
    - '/camera/camera/gyro/sample' & '/camera/camera/accel/sample'
    - Though both topics are of same message type 'sensor_msgs::Imu', only their relevant fields are filled out.
  - A new topic called **imu** will be created, when both *accel* and *gyro* streams are enabled and the param *unite_imu_method* set to > 0.
    - Data from both accel and gyro are combined and published to this topic
    - All the fields of the Imu message are filled out.
    - It will be published at the rate of the gyro.
  - `unite_imu_method` param supports below values:
    - 0 -> **none**: no imu topic
    - 1 -> **copy**: Every gyro message will be attached by the last accel message.
    - 2 -> **linear_interpolation**: Every gyro message will be attached by an accel message which is interpolated to gyro's timestamp.
  - Note: When the param *unite_imu_method* is dynamically updated, re-enable either gyro or accel stream for the change to take effect.
- **accelerate_gpu_with_glsl**:
  - Boolean: GPU accelerated with GLSL for processing PointCloud and Colorizer filters.
  - Note:
    - To have smooth transition between the processing blocks when this parameter is updated dynamically, the node will:
      - Stop the video sensors
      - Do necessary GLSL configuration
      - And then, start the video sensors
    - To enable GPU acceleration, turn ON `BUILD_ACCELERATE_GPU_WITH_GLSL` during build:
    ```bash
    colcon build --cmake-args '-DBUILD_ACCELERATE_GPU_WITH_GLSL=ON'
    ```


</details>


#  7.2.3.  ROS2 Cameras  


The following post processing filters are available:
 - ```align_depth```: If enabled, will publish the depth image aligned to the color image on the topic `/camera/camera/aligned_depth_to_color/image_raw`.
   - The pointcloud, if created, will be based on the aligned depth image.
 - ```colorizer```: will color the depth image. On the depth topic an RGB image will be published, instead of the 16bit depth values .
 - ```pointcloud```: will add a pointcloud topic `/camera/camera/depth/color/points`.
    * The texture of the pointcloud can be modified using the `pointcloud.stream_filter` parameter.</br>
    * The depth FOV and the texture FOV are not similar. By default, pointcloud is limited to the section of depth containing the texture. You can have a full depth to pointcloud, coloring the regions beyond the texture with zeros, by setting `pointcloud.allow_no_texture_points` to true.
    * pointcloud is of an unordered format by default. This can be changed by setting `pointcloud.ordered_pc` to true.
    * The QoS of the pointcloud topic is independent from depth and color streams and can be controlled with the `pointcloud.pointcloud_qos` parameter.
      - The same set of QoS values are supported as other streams, refer to <stream_type>_qos in the Parameters section of this page.
      - The launch file should include the parameter with initial QoS value, for example,`{'name': 'pointcloud.pointcloud_qos',    'default': 'SENSOR_DATA', 'description': 'pointcloud qos'}`
      - The QoS value can also be overridden at launch with command option, for example, `pointcloud.pointcloud_qos:=SENSOR_DATA`
      - At runtime, the QoS can be changed dynamically but require the filter re-enable for the change to take effect, for example,
        ```bash
        ros2 param set /camera/camera pointcloud.pointcloud_qos SENSOR_DATA
        ros2 param set /camera/camera pointcloud.enable false
        ros2 param set /camera/camera pointcloud.enable true
        ```
 - ```hdr_merge```: Allows depth image to be created by merging the information from 2 consecutive frames, taken with different exposure and gain values.
 - `depth_module.hdr_enabled`: to enable/disable HDR. The way to set exposure and gain values for each sequence:
   -  during Runtime:
      - is by first selecting the sequence id, using the `depth_module.sequence_id` parameter and then modifying the `depth_module.gain`, and `depth_module.exposure`.
      - From FW versions 5.14.x.x and above, if HDR is enabled, the preset configs (like exposure, gain, etc.,) cannot be updated.
        - Disable the HDR first using `depth_module.hdr_enabled` parameter and then, update the required presets.
    - during Launch time of the node:
      - is by setting below parameters
        - `depth_module.exposure.1`
        - `depth_module.gain.1`
        - `depth_module.exposure.2`
        - `depth_module.gain.2`
      - Make sure to set `depth_module.hdr_enabled` to true, otherwise these parameters won't be considered.
    - To view the effect on the infrared image for each sequence id use the `filter_by_sequence_id.sequence_id` parameter.
    - For in-depth review of the subject please read the accompanying [white paper](https://dev.intelrealsense.com/docs/high-dynamic-range-with-stereoscopic-depth-cameras).
    - **Note**: Auto exposure functionality is not supported when HDR is enabled. i.e., Auto exposure will be auto-disabled if HDR is enabled.


[ camera ](https://forums.developer.nvidia.com/tag/camera) , [ ros
](https://forums.developer.nvidia.com/tag/ros) , [ isaacsim
](https://forums.developer.nvidia.com/tag/isaacsim) , [ isaac-sdk
](https://forums.developer.nvidia.com/tag/isaac-sdk) , [ custom-robot
](https://forums.developer.nvidia.com/tag/custom-robot)


**Please choose only one option from the 3 options below (in order to prevent multiple versions installation and workspace conflicts)**


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#  ROS2 Image Pipeline Tutorial


  - The following filters have detailed descriptions in : https://github.com/IntelRealSense/librealsense/blob/master/doc/post-processing-filters.md
    - ```disparity_filter``` - convert depth to disparity before applying other filters and back.
    - ```spatial_filter``` - filter the depth image spatially.
    - ```temporal_filter``` - filter the depth image temporally.
    - ```hole_filling_filter``` - apply hole-filling filter.
    - ```decimation_filter``` - reduces depth scene complexity.


  * [ Documentation ](https://www.stereolabs.com/docs/)
  * [ Samples ](https://www.stereolabs.com/docs/samples/)
  * [ API Reference ](https://www.stereolabs.com/docs/api/)
  * [ Support ](https://support.stereolabs.com/hc/en-us/)
  * [ Downloads ](https://www.stereolabs.com/developers/release/)


      ```bash
      > cd C:\ros2_ws\realsense-ros
      ```
5. Build librealsense2 package only
      ```bash
      > colcon build --packages-select librealsense2 --cmake-args -DBUILD_EXAMPLES=OFF -DBUILD_WITH_STATIC_CRT=OFF -DBUILD_GRAPHICAL_EXAMPLES=OFF
      ```
	  - User can add `--event-handlers console_direct+` parameter to see more debug outputs of the colcon build
6. Build the other packages
	```bash
	> colcon build --packages-select realsense2_camera_msgs realsense2_description realsense2_camera
	```
	- User can add `--event-handlers console_direct+` parameter to see more debug outputs of the colcon build


        rclcpp::init(argc, argv);

        // Create the node
        g_node = rclcpp::Node::make_shared("zed_video_tutorial");


You can however run slower than realtime and use the sim_time clock message
from isaac sim to keep things in sync.


ZED:


For more information about QoS compatibility, refer to the [ ZED node guide
](https://www.stereolabs.com/docs/ros2/zed-node/#published-topics)


  * [ Scene Setup and Assets ](../features/environment_setup/index.html)
  * [ Physics Simulation ](../features/physics/index.html)
  * [ Robots Simulation ](../features/robots_simulation/index.html)
  * [ Sensors Simulation ](../features/sensors_simulation/index.html)
  * [ Debugging ](../features/debugging/index.html)
  * [ Mapping ](../features/mapping/index.html)
  * [ Warehouse Logistics ](../features/warehouse_logistics/index.html)


Isaac Sim


7. Setup environment with new installed packages (Do this for every new terminal/cmd you open):
      ```bash
      > call install\setup.bat
      ```
</details>


### Limitations


###  Conclusion  #


> ros2 service list
/camera/camera/device_info
```


> |
>
> rgb
>
> frameId
>
> |
>
> turtle

Ticking this graph will automatically create a new render product assigned to
_Camera_1_ .


* Changed Parameters:
    - **"stereo_module"**, **"l500_depth_sensor"** are replaced by **"depth_module"**
    - For video streams: **\<module>.profile** replaces **\<stream>_width**, **\<stream>_height**, **\<stream>_fps**
        - **ROS2-legacy (Old)**:
          - ros2 launch realsense2_camera rs_launch.py depth_width:=640 depth_height:=480 depth_fps:=30.0 infra1_width:=640 infra1_height:=480 infra1_fps:=30.0
        - **ROS2-development (New)**:
          - ros2 launch realsense2_camera rs_launch.py depth_module.profile:=640x480x30
    - Removed paramets **\<stream>_frame_id**, **\<stream>_optical_frame_id**. frame_ids are now defined by camera_name
    - **"filters"** is removed. All filters (or post-processing blocks) are enabled/disabled using **"\<filter>.enable"**
    - **"align_depth"** is now a regular processing block and as such the parameter for enabling it is replaced with **"align_depth.enable"**
    - **"allow_no_texture_points"**, **"ordered_pc"** are now belong to the pointcloud filter and as such are replaced by **"pointcloud.allow_no_texture_points"**, **"pointcloud.ordered_pc"**
    - **"pointcloud_texture_stream"**, **"pointcloud_texture_index"** belong now to the pointcloud filter and were renamed to match their librealsense' names: **"pointcloud.stream_filter"**, **"pointcloud.stream_index_filter"**
- Allow enable/disable of sensors in runtime (parameters **\<stream>.enable**)
- Allow enable/disable of filters in runtime (parameters **\<filter_name>.enable**)
- **unite_imu_method** parameter is now changeable in runtime.
- **enable_sync** parameter is now changeable in runtime.


<hr>


In this tutorial, you will learn how to write a simple C++ node that
subscribes to messages of type ` sensor_msgs/Image ` . This lets you retrieve
the Left and Right rectified images published by the ZED node.


[ Omniverse  ](/c/omniverse/simulation/69) [ Isaac Sim
](/c/omniverse/simulation/69)


__ [ Omniverse IsaacSim ](../index.html)


  -  Source environment
   ```bash
   ROS_DISTRO=<YOUR_SYSTEM_ROS_DISTRO>  # set your ROS_DISTRO: iron, humble, foxy
   source /opt/ros/$ROS_DISTRO/setup.bash
   cd ~/ros2_ws
   . install/local_setup.bash
   ```

  </details>


## Camera Name And Camera Namespace


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JavaScript enabled


Concepts


* Examples of running similar environment using Standalone Python workflow is outlined [ here  ](tutorial_ros2_python.html#isaac-sim-app-tutorial-ros2-python-stereo) .


Features