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Docker

This folder contains a list of Dockerfile files to build Docker images ready to start the nodes of the ZED ROS2 Wrapper:

  • Dockerfile.desktop-humble: development desktop image for ROS2 Humble, running on the specified Ubuntu and CUDA versions. The ZED Wrapper is copied from the source file of the current branch and compiled.
  • Dockerfile.l4t-humble: Jetson image for ROS2 Humble, running on the given L4T version (L4T35.4 by default).

Cross compilation

You can easily compile the image for jetson from your usual Desktop PC. For that you just need to run the following line before:

docker run --rm --privileged multiarch/qemu-user-static --reset -p yes

Build the Docker images

We provide a script to build your image with the right L4T / ZED SDK version.

  • Checkout the tag or commit of the ROS2 wrapper that you need.
git checkout 4.1.2

You can also modify the sources.

  • Build the image
./jetson_build_dockerfile_from_sdk_and_l4T_version.sh <l4T version> <ZED SDK version>

or

./desktop_build_dockerfile_from_sdk_ubuntu_and_cuda_version.sh <Ubuntu version> <CUDA version> <ZED SDK version>

Examples:

./jetson_build_dockerfile_from_sdk_and_l4T_version.sh l4t-r35.4.1 zedsdk4.1.2
./desktop_build_dockerfile_from_sdk_ubuntu_and_cuda_version.sh ubuntu22.04 cuda12.1.0 zedsdk4.1.2

That will produce the image zed_ros2_l4t_image or zed_ros2_desktop_image. Some configuration will not work (for example, if a specific ZED SDK does not exist for a given Ubuntu/CUDA/L4T version, or if the given ros2 wrapper is not compatible with this version)

Run the Docker image

NVIDIA runtime

NVIDIA drivers must be accessible from the Docker image to run the ZED SDK code on the GPU. You'll need :

  • The nvidia container runtime installed, following this guide
  • A specific docker runtime environment with -gpus all or -e NVIDIA_DRIVER_CAPABILITIES=all
  • Docker privileged mode with --privileged

Volumes

A few volumes should also be shared with the host.

  • /usr/local/zed/settings:/usr/local/zed/settings if you plan to use the robot in an Internet-negated area, and you previously downloaded the camera calibration files by following this guide.
  • /usr/local/zed/resources:/usr/local/zed/resources if you plan to use the AI module of the ZED SDK (Object Detection, Skeleton Tracking, NEURAL depth) we suggest binding mounting a folder to avoid downloading and optimizing the AI models each time the Docker image is restarted. The first time you use the AI model inside the Docker image, it will be downloaded and optimized in the local bound-mounted folder, and stored there for the next runs.
  • /dev:/dev to share the video devices
  • For GMSL cameras (ZED X) you'll also need
    • /tmp:/tmp
    • /var/nvidia/nvcam/settings/:/var/nvidia/nvcam/settings/
    • /etc/systemd/system/zed_x_daemon.service:/etc/systemd/system/zed_x_daemon.service

Start the Docker container

The following command starts an interactive session:

docker run --runtime nvidia -it --privileged --ipc=host --pid=host -e NVIDIA_DRIVER_CAPABILITIES=all -e DISPLAY \
  -v /dev:/dev -v /tmp/.X11-unix/:/tmp/.X11-unix \
  -v ${HOME}/zed_docker_ai/:/usr/local/zed/resources/ \
  <image_tag>

For GMSL cameras

docker run --runtime nvidia -it --privileged --ipc=host --pid=host -e NVIDIA_DRIVER_CAPABILITIES=all -e DISPLAY \
  -v /dev:/dev \
  -v /tmp:/tmp \
  -v /var/nvidia/nvcam/settings/:/var/nvidia/nvcam/settings/ \
  -v /etc/systemd/system/zed_x_daemon.service:/etc/systemd/system/zed_x_daemon.service \
  -v ${HOME}/zed_docker_ai/:/usr/local/zed/resources/ \
  <image_tag>