RealSense Camera Examples

This page contains tutorials for running nvblox together with Visual SLAM on a RealSense camera.


This tutorial requires a compatible RealSense camera from the list of available cameras.


  1. Complete the Isaac ROS NvBlox RealSense Setup tutorial.

  2. Complete the nvblox quickstart.

  3. If you installed nvblox as a Debian package, you will also need to clone isaac_ros_nvblox under ${ISAAC_ROS_WS}/src:

    cd ${ISAAC_ROS_WS}/src
    git clone --recursive
  4. Stop Git tracking the COLCON_IGNORE file in the realsense_splitter package and remove it.

    cd ${ISAAC_ROS_WS}/src/isaac_ros_nvblox/nvblox_examples/realsense_splitter && \
        git update-index --assume-unchanged COLCON_IGNORE && \
        rm COLCON_IGNORE


    Note: The COLCON_IGNORE file was added to remove the dependency to realsense-ros for users that don’t want to run the RealSense examples.

  5. Launch the Docker container using the script (if not already launched):

    cd ${ISAAC_ROS_WS}/src/isaac_ros_common && \
  6. Build the realsense_splitter and realsense2*

    cd /workspaces/isaac_ros-dev
    colcon build --symlink-install --packages-up-to-regex realsense*
    source install/setup.bash

RealSense Example

This example runs nvblox-based reconstruction from a single RealSense camera, either from live data coming directly off a RealSense camera, or from recorded data coming from a ROSbag.

  1. Start the Isaac ROS Dev Docker container (if not started in the install step)

    cd $ISAAC_ROS_WS && ./src/isaac_ros_common/scripts/
  2. Navigate (inside the docker) to the workspace folder, and source the workspace

    cd /workspaces/isaac_ros-dev
    source install/setup.bash
  3. Run the RealSense example, either live from a sensor or from a recorded ROSbag.

    ros2 launch nvblox_examples_bringup


If you want to restrict odometry to a 2D plane (for example, to run a robot in a flat environment), you can use the enable_ground_constraint_in_odometry argument.

Recording Data with RealSense

To record RealSense data for nvblox:

  1. Connect the camera, start the Docker container and source the workspace as explained in RealSense Camera Examples.

  2. Start recording:

    ros2 launch nvblox_examples_bringup
  3. Stop the recording when done

  4. The resulting ROSbag can be run using the instructions above.

Reconstruction With People

This tutorial demonstrates how to perform dynamic people reconstruction in nvblox using RealSense data. For more information on how people reconstruction works, see Technical Details.


If you are on a desktop machine, we recommend using the PeopleSemSegNet. On Jetson platforms we recommend the lighter PeopleSemSegNet ShuffleSeg model that is provided in Isaac ROS Image Segmentation for better segmentation performance.

  1. Download and install the PeopleSemSegNet model assets:

    sudo apt-get install -y ros-humble-isaac-ros-peoplesemseg-models-install &&
    ros2 run isaac_ros_peoplesemseg_models_install --eula &&
    ros2 run isaac_ros_peoplesemseg_models_install --eula
  2. Below we provide run instructions for both the full and light segmentation models (PeopleSemSegNet and ShuffleSeg) respectively, running from both a ROSbag and live from a RealSense camera.

    ros2 launch nvblox_examples_bringup \

Reconstruction With Dynamic Scene Elements

This tutorial demonstrates how build a reconstruction with dynamic elements in the scene (people and non-people) using RealSense data. For more information about how dynamic reconstruction works in nvblox see Technical Details.

  1. Below we provide run instructions for running from both a ROSbag and live from a RealSense camera.

    ros2 launch nvblox_examples_bringup \

Visualizing in Foxglove

The examples in previous sections on this page have used rviz for visualization. RViz is our default visualization tool in the case that nvblox is running on the same computer that is displaying the visualization. In the case that you’d like to visualize a reconstruction streamed from a remote machine, for example a robot, our recommended method is to use Foxglove.

To visualize with foxglove please see Foxglove Visualization. Ensure that you additionally install the nvblox Foxglove extension. The animation above shows the results of visualizing the /nvblox_node/mesh and /nvblox/static_esdf_pointcloud topics.


When visualizing from a remote machine over WiFi, bandwidth is limited and easily exceeded. Exceeding this bandwidth can lead to poor visualization results. For best results we recommend visualizing a limited number of topics, and to avoiding visualizing high-bandwidth topics for example images. Furthermore, it is necessary to limit bandwidth of the mesh transmitted by nvblox. Nvblox exposes a parameter for this purpose mesh_bandwidth_limit_mbps. When visualizing over WiFi we recommend setting this to 30 here.


See RealSense Issues.