Publishing Camera’s Data#

Learning Objectives#

In this tutorial, you learn how to programmatically set up publishers for Isaac Sim Cameras at an approximate frequency.

Getting Started#

Prerequisite

Note

In Windows 10 or 11, depending on your machine’s configuration, RViz2 might not open properly.

Setup a Camera in a Scene#

To begin this tutorial, set up an environment with a isaacsim.sensors.camera Camera object. Running the following code results in a basic warehouse environment loaded with a camera in the scene.

 1import carb
 2from isaacsim import SimulationApp
 3import sys
 4
 5BACKGROUND_STAGE_PATH = "/background"
 6BACKGROUND_USD_PATH = "/Isaac/Environments/Simple_Warehouse/warehouse_with_forklifts.usd"
 7
 8CONFIG = {"renderer": "RayTracedLighting", "headless": False}
 9
10# Example ROS 2 bridge sample demonstrating the manual loading of stages and manual publishing of images
11simulation_app = SimulationApp(CONFIG)
12import omni
13import numpy as np
14from isaacsim.core.api import SimulationContext
15from isaacsim.core.utils import stage, extensions
16from isaacsim.storage.native import get_assets_root_path
17import omni.graph.core as og
18import omni.replicator.core as rep
19import omni.syntheticdata._syntheticdata as sd
20
21from isaacsim.core.utils.prims import set_targets
22from isaacsim.sensors.camera import Camera
23import isaacsim.core.utils.numpy.rotations as rot_utils
24from isaacsim.core.utils.prims import is_prim_path_valid
25from isaacsim.core.nodes.scripts.utils import set_target_prims
26
27# Enable ROS 2 bridge extension
28extensions.enable_extension("isaacsim.ros2.bridge")
29
30simulation_app.update()
31
32simulation_context = SimulationContext(stage_units_in_meters=1.0)
33
34# Locate Isaac Sim assets folder to load environment and robot stages
35assets_root_path = get_assets_root_path()
36if assets_root_path is None:
37    carb.log_error("Could not find Isaac Sim assets folder")
38    simulation_app.close()
39    sys.exit()
40
41# Loading the environment
42stage.add_reference_to_stage(assets_root_path + BACKGROUND_USD_PATH, BACKGROUND_STAGE_PATH)
43
44
45###### Camera helper functions for setting up publishers. ########
46
47# Paste functions from the tutorial here
48# def publish_camera_tf(camera: Camera): ...
49# def publish_camera_info(camera: Camera, freq): ...
50# def publish_pointcloud_from_depth(camera: Camera, freq): ...
51# def publish_depth(camera: Camera, freq): ...
52# def publish_rgb(camera: Camera, freq): ...
53
54###################################################################
55
56# Create a Camera prim. The Camera class takes the position and orientation in the world axes convention.
57camera = Camera(
58    prim_path="/World/floating_camera",
59    position=np.array([-3.11, -1.87, 1.0]),
60    frequency=20,
61    resolution=(256, 256),
62    orientation=rot_utils.euler_angles_to_quats(np.array([0, 0, 0]), degrees=True),
63)
64camera.initialize()
65
66simulation_app.update()
67camera.initialize()
68
69############### Calling Camera publishing functions ###############
70
71# Call the publishers.
72# Make sure you pasted in the helper functions above, and uncomment out the following lines before running.
73
74approx_freq = 30
75#publish_camera_tf(camera)
76#publish_camera_info(camera, approx_freq)
77#publish_rgb(camera, approx_freq)
78#publish_depth(camera, approx_freq)
79#publish_pointcloud_from_depth(camera, approx_freq)
80
81####################################################################
82
83# Initialize physics
84simulation_context.initialize_physics()
85simulation_context.play()
86
87while simulation_app.is_running():
88    simulation_context.step(render=True)
89
90simulation_context.stop()
91simulation_app.close()

Publish Camera Intrinsics to CameraInfo Topic#

The following snippet will publish camera intrinsics associated with an isaacsim.sensors.camera Camera to a sensor_msgs/CameraInfo topic.

 1def publish_camera_info(camera: Camera, freq):
 2    from isaacsim.ros2.core import read_camera_info
 3    # The following code will link the camera's render product and publish the data to the specified topic name.
 4    render_product = camera._render_product_path
 5    step_size = int(60/freq)
 6    topic_name = camera.name+"_camera_info"
 7    queue_size = 1
 8    node_namespace = ""
 9    frame_id = camera.prim_path.split("/")[-1] # This matches what the TF tree is publishing.
10
11    writer = rep.writers.get("ROS2PublishCameraInfo")
12    camera_info, _ = read_camera_info(render_product_path=render_product)
13    writer.initialize(
14        frameId=frame_id,
15        nodeNamespace=node_namespace,
16        queueSize=queue_size,
17        topicName=topic_name,
18        width=camera_info.width,
19        height=camera_info.height,
20        projectionType=camera_info.distortion_model,
21        k=camera_info.k.reshape([1, 9]),
22        r=camera_info.r.reshape([1, 9]),
23        p=camera_info.p.reshape([1, 12]),
24        physicalDistortionModel=camera_info.distortion_model,
25        physicalDistortionCoefficients=camera_info.d,
26    )
27    writer.attach([render_product])
28
29    gate_path = omni.syntheticdata.SyntheticData._get_node_path(
30        "PostProcessDispatch" + "IsaacSimulationGate", render_product
31    )
32
33    # Set step input of the Isaac Simulation Gate nodes upstream of ROS publishers to control their execution rate
34    og.Controller.attribute(gate_path + ".inputs:step").set(step_size)
35    return

Publish Pointcloud from Depth Images#

In the following snippet, a pointcloud is published to a sensor_msgs/PointCloud2 message. This pointcloud is reconstructed from the depth image using the intrinsics of the camera.

 1def publish_pointcloud_from_depth(camera: Camera, freq):
 2    # The following code will link the camera's render product and publish the data to the specified topic name.
 3    render_product = camera._render_product_path
 4    step_size = int(60/freq)
 5    topic_name = camera.name+"_pointcloud" # Set topic name to the camera's name
 6    queue_size = 1
 7    node_namespace = ""
 8    frame_id = camera.prim_path.split("/")[-1] # This matches what the TF tree is publishing.
 9
10    # Note, this pointcloud publisher will convert the Depth image to a pointcloud using the Camera intrinsics.
11    # This pointcloud generation method does not support semantic labeled objects.
12    rv = omni.syntheticdata.SyntheticData.convert_sensor_type_to_rendervar(
13        sd.SensorType.DistanceToImagePlane.name
14    )
15
16    writer = rep.writers.get(rv + "ROS2PublishPointCloud")
17    writer.initialize(
18        frameId=frame_id,
19        nodeNamespace=node_namespace,
20        queueSize=queue_size,
21        topicName=topic_name
22    )
23    writer.attach([render_product])
24
25    # Set step input of the Isaac Simulation Gate nodes upstream of ROS publishers to control their execution rate
26    gate_path = omni.syntheticdata.SyntheticData._get_node_path(
27        rv + "IsaacSimulationGate", render_product
28    )
29    og.Controller.attribute(gate_path + ".inputs:step").set(step_size)
30
31    return

Publish RGB Images#

 1def publish_rgb(camera: Camera, freq):
 2    # The following code will link the camera's render product and publish the data to the specified topic name.
 3    render_product = camera._render_product_path
 4    step_size = int(60/freq)
 5    topic_name = camera.name+"_rgb"
 6    queue_size = 1
 7    node_namespace = ""
 8    frame_id = camera.prim_path.split("/")[-1] # This matches what the TF tree is publishing.
 9
10    rv = omni.syntheticdata.SyntheticData.convert_sensor_type_to_rendervar(sd.SensorType.Rgb.name)
11    writer = rep.writers.get(rv + "ROS2PublishImage")
12    writer.initialize(
13        frameId=frame_id,
14        nodeNamespace=node_namespace,
15        queueSize=queue_size,
16        topicName=topic_name
17    )
18    writer.attach([render_product])
19
20    # Set step input of the Isaac Simulation Gate nodes upstream of ROS publishers to control their execution rate
21    gate_path = omni.syntheticdata.SyntheticData._get_node_path(
22        rv + "IsaacSimulationGate", render_product
23    )
24    og.Controller.attribute(gate_path + ".inputs:step").set(step_size)
25
26    return

Publish Depth Images#

 1def publish_depth(camera: Camera, freq):
 2    # The following code will link the camera's render product and publish the data to the specified topic name.
 3    render_product = camera._render_product_path
 4    step_size = int(60/freq)
 5    topic_name = camera.name+"_depth"
 6    queue_size = 1
 7    node_namespace = ""
 8    frame_id = camera.prim_path.split("/")[-1] # This matches what the TF tree is publishing.
 9
10    rv = omni.syntheticdata.SyntheticData.convert_sensor_type_to_rendervar(
11                            sd.SensorType.DistanceToImagePlane.name
12                        )
13    writer = rep.writers.get(rv + "ROS2PublishImage")
14    writer.initialize(
15        frameId=frame_id,
16        nodeNamespace=node_namespace,
17        queueSize=queue_size,
18        topicName=topic_name
19    )
20    writer.attach([render_product])
21
22    # Set step input of the Isaac Simulation Gate nodes upstream of ROS publishers to control their execution rate
23    gate_path = omni.syntheticdata.SyntheticData._get_node_path(
24        rv + "IsaacSimulationGate", render_product
25    )
26    og.Controller.attribute(gate_path + ".inputs:step").set(step_size)
27
28    return

Publish a TF Tree for the Camera Pose#

The pointcloud, published using the above function, will publish the pointcloud in the ROS camera axes convention (-Y up, +Z forward). To make visualizing this pointcloud easy in ROS using RViz, the following snippet will publish a TF Tree to the /tf, containing two frames.

The two frames are:

  1. {camera_frame_id}: This is the camera’s pose in the ROS camera convention (-Y up, +Z forward). Pointclouds are published in this frame.

  2. {camera_frame_id}_world: This is the camera’s pose in the World axes convention (+Z up, +X forward). This will reflect the true pose of the camera.

The TF Tree looks like this:

  • world -> {camera_frame_id} is a dynamic transform from origin to the camera in the ROS camera convention, following any movement of the camera.

  • {camera_frame_id} -> {camera_frame_id}_world is a static transform consisting of only a rotation and zero translation. This static transform can be represented by the quaternion [0.5, -0.5, 0.5, 0.5] in [w, x, y, z] convention.

Because the pointcloud is published in {camera_frame_id}, it is encouraged to set the frame_id of the pointcloud topic to {camera_frame_id}. The resulting visualization of the pointclouds can be viewed in the world frame in RViz.

 1def publish_camera_tf(camera: Camera):
 2    camera_prim = camera.prim_path
 3
 4    if not is_prim_path_valid(camera_prim):
 5        raise ValueError(f"Camera path '{camera_prim}' is invalid.")
 6
 7    try:
 8        # Generate the camera_frame_id. OmniActionGraph will use the last part of
 9        # the full camera prim path as the frame name, so we will extract it here
10        # and use it for the pointcloud frame_id.
11        camera_frame_id=camera_prim.split("/")[-1]
12
13        # Generate an action graph associated with camera TF publishing.
14        ros_camera_graph_path = "/CameraTFActionGraph"
15
16        # If a camera graph is not found, create a new one.
17        if not is_prim_path_valid(ros_camera_graph_path):
18            (ros_camera_graph, _, _, _) = og.Controller.edit(
19                {
20                    "graph_path": ros_camera_graph_path,
21                    "evaluator_name": "execution",
22                    "pipeline_stage": og.GraphPipelineStage.GRAPH_PIPELINE_STAGE_SIMULATION,
23                },
24                {
25                    og.Controller.Keys.CREATE_NODES: [
26                        ("OnTick", "omni.graph.action.OnTick"),
27                        ("IsaacClock", "isaacsim.core.nodes.IsaacReadSimulationTime"),
28                        ("RosPublisher", "isaacsim.ros2.bridge.ROS2PublishClock"),
29                    ],
30                    og.Controller.Keys.CONNECT: [
31                        ("OnTick.outputs:tick", "RosPublisher.inputs:execIn"),
32                        ("IsaacClock.outputs:simulationTime", "RosPublisher.inputs:timeStamp"),
33                    ]
34                }
35            )
36
37        # Generate 2 nodes associated with each camera: TF from world to ROS camera convention, and world frame.
38        og.Controller.edit(
39            ros_camera_graph_path,
40            {
41                og.Controller.Keys.CREATE_NODES: [
42                    ("PublishTF_"+camera_frame_id, "isaacsim.ros2.bridge.ROS2PublishTransformTree"),
43                    ("PublishRawTF_"+camera_frame_id+"_world", "isaacsim.ros2.bridge.ROS2PublishRawTransformTree"),
44                ],
45                og.Controller.Keys.SET_VALUES: [
46                    ("PublishTF_"+camera_frame_id+".inputs:topicName", "/tf"),
47                    # Note if topic_name is changed to something else besides "/tf",
48                    # it will not be captured by the ROS tf broadcaster.
49                    ("PublishRawTF_"+camera_frame_id+"_world.inputs:topicName", "/tf"),
50                    ("PublishRawTF_"+camera_frame_id+"_world.inputs:parentFrameId", camera_frame_id),
51                    ("PublishRawTF_"+camera_frame_id+"_world.inputs:childFrameId", camera_frame_id+"_world"),
52                    # Static transform from ROS camera convention to world (+Z up, +X forward) convention:
53                    ("PublishRawTF_"+camera_frame_id+"_world.inputs:rotation", [0.5, -0.5, 0.5, 0.5]),
54                ],
55                og.Controller.Keys.CONNECT: [
56                    (ros_camera_graph_path+"/OnTick.outputs:tick",
57                        "PublishTF_"+camera_frame_id+".inputs:execIn"),
58                    (ros_camera_graph_path+"/OnTick.outputs:tick",
59                        "PublishRawTF_"+camera_frame_id+"_world.inputs:execIn"),
60                    (ros_camera_graph_path+"/IsaacClock.outputs:simulationTime",
61                        "PublishTF_"+camera_frame_id+".inputs:timeStamp"),
62                    (ros_camera_graph_path+"/IsaacClock.outputs:simulationTime",
63                        "PublishRawTF_"+camera_frame_id+"_world.inputs:timeStamp"),
64                ],
65            },
66        )
67    except Exception as e:
68        print(e)
69
70    # Add target prims for the USD pose. All other frames are static.
71    set_target_prims(
72        primPath=ros_camera_graph_path+"/PublishTF_"+camera_frame_id,
73        inputName="inputs:targetPrims",
74        targetPrimPaths=[camera_prim],
75    )
76    return

Running the Example#

Enable isaacsim.ros2.bridge extension and set up ROS 2 environment variables following this workflow tutorial. Save the above script and run it using python.sh in the Isaac Sim folder. In our example, {camera_frame_id} is the prim name of the camera, which is floating_camera.

Verify that you observe a floating camera with prim path /World/floating_camera in the scene, and verify that the camera sees a forklift:

Verify that you observe the following:

../_images/isaac_tutorial_ros_camera_publishing_simview.png

If you open a terminal and type ros2 topic list, verify that you observe the following:

ros2 topic list
/camera_camera_info
/camera_depth
/camera_pointcloud
/camera_rgb
/clock
/parameter_events
/rosout
/tf

The frames published by TF will look like the following:

../_images/frames.png

Now, you can visualize the pointcloud and depth images using RViz2. Open RViz2, and set the Fixed Frame field to world.

../_images/rviz.png

Then, enable viewing /camera_depth, /camera_rgb, /camera_pointcloud, and /tf topics.

Verify that the depth image /camera_depth and RGB image /camera_rgb look like this:

../_images/isaac_tutorial_ros_camera_publishing_rgbd.png

The pointcloud will look like so. Verify that the camera frames published by the TF publisher shows the two frames. The image on the left shows the {camera_frame_id}_world frame, and the image on the right shows the {camera_frame_id} frame.

../_images/isaac_tutorial_ros_camera_publishing_pc_frontview.png

From the side view:

../_images/isaac_tutorial_ros_camera_publishing_pc_sideview.png

Summary#

This tutorial demonstrated how to programmatically set up ROS 2 publishers for Isaac Sim Cameras at an approximate frequency.

Next Steps#

Continue on to the next tutorial in our ROS 2 Tutorials series, RTX Lidar Sensors, to learn how to add a RTX Lidar sensor to the Turtlebot3.