isaac_ros_image_pipeline

WARNING

Isaac ROS Image Pipeline has moved to NVIDIA-ISAAC-ROS (2021-10-20). Please visit here to find new development and file issues.

Overview

This metapackage offers similar functionality as the standard, CPU-based image_pipeline metapackage, but does so by leveraging the Jetson platform's specialized computer vision hardware. Considerable effort has been made to ensure that replacing image_pipeline with isaac_ros_image_pipeline on a Jetson device is as painless a transition as possible.

System Requirements

This Isaac ROS package is designed and tested to be compatible with ROS2 Foxy on Jetson hardware.

Jetson

• AGX Xavier or Xavier NX
• JetPack 4.6

x86_64

• CUDA 10.2/11.2 supported discrete GPU
• VPI 1.1.11
• Ubuntu 18.04+

Docker

Precompiled ROS2 Foxy packages are not available for JetPack 4.6 (based on Ubuntu 18.04 Bionic). You can either manually compile ROS2 Foxy and required dependent packages from source or use the Isaac ROS development Docker image from Isaac ROS Common based on images from jetson-containers. The Docker images support both Jetson and x86_64 platfroms. The x86_64 docker image includes VPI Debian packages for CUDA 11.2.

Run the following script in isaac_ros_common to build the image and launch the container:

$ scripts/run_dev.sh <optional_path>

You can either provide an optional path to mirror in your host ROS workspace with Isaac ROS packages, which will be made available in the container as /workspaces/isaac_ros-dev, or you can setup a new workspace in the container.

Package Dependencies

• isaac_ros_common
• image_common
• vision_cv
• OpenCV 4.5+
• scikit-learn
• Eigen3

Note: `isaac_ros_common' is used for running tests and/or creating a development container. It also contains VPI Debian packages that can be installed natively on a development machine without a container.

Quickstart

1. Create a ROS2 workspace if one is not already prepared:
   mkdir -p your_ws/src
   Note: The workspace can have any name; the quickstart assumes you name it your_ws.
2. Clone this metapackage repository to your_ws/src/isaac_ros_image_pipeline. Check that you have Git LFS installed before cloning to pull down all large files.
   cd your_ws/src && git clone https://github.com/NVIDIA-AI-IOT/isaac_ros_image_pipeline
3. Build and source the workspace:
   cd your_ws && colcon build --symlink-install && source install/setup.bash
4. (Optional) Run tests to verify complete and correct installation:
   colcon test
5. Start isaac_ros_image_proc using the prebuilt executable:
   ros2 run isaac_ros_image_proc isaac_ros_image_proc
6. In a separate terminal, spin up a calibrated camera publisher to /image_raw and /camera_info using any package(for example, v4l2_camera):
   ros2 run v4l2_camera v4l2_camera_node
7. Observe the rectified image output in grayscale and color on /image_rect and /image_rect_color, respectively:
   ros2 run image_view image_view --ros-args -r image:=image_rect
ros2 run image_view image_view --ros-args -r image:=image_rect_color

Replacing image_pipeline with isaac_ros_image_pipeline

1. Add a dependency on isaac_ros_image_pipeline to your_package/package.xml and your_package/CMakeLists.txt. If all desired packages under an existing image_pipeline dependency have Isaac ROS alternatives (see Supported Packages), then the original image_pipeline dependency may be removed entirely.
2. Change the package and plugin names in any *.launch.py launch files to use [package name] and isaac_ros::image_proc::[component_name] respectively. For a list of all packages, see Supported Packages. For a list of all ROS2 Components made available, see the per-package detailed documentation below.

Supported Packages

At this time, the packages under the standard image_pipeline have the following support:

See also:

• isaac_ros_apriltag: Accelerated ROS2 wrapper for Apriltag detection
• isaac_ros_common: Utilities for robust ROS2 testing, in conjunction with launch_test

Tutorial - Stereo Image Pipeline

1. Connect a compatible Realsense camera (D435, D455) to your host machine.
2. Build and source the workspace:
   cd your_ws && colcon build --symlink-install && source install/setup.bash
3. Spin up the stereo image pipeline and Realsense camera node with the launchfile:
   ros2 launch isaac_ros_stereo_image_proc isaac_ros_stereo_image_pipeline_launch.py

Note For best performance on Jetson, ensure that power settings are configured appropriately (Power Management for Jetson).

ROS2 Package API

isaac_ros_image_proc

Overview

The isaac_ros_image_proc package offers functionality for rectifying/undistorting images from a monocular camera setup, resizing the image, and changing the image format. It largely replaces the image_proc package, though the image format conversion facility also functions as a way to replace the CPU-based image format conversion in cv_bridge.

Available Components

isaac_ros_stereo_image_proc

Overview

The isaac_ros_stereo_image_proc package offers functionality for handling image pairs from a binocular/stereo camera setup, calculating the disparity between the two images, and producing a point cloud with depth information. It largely replaces the stereo_image_proc package.

Available Components

References

[1] D. Scharstein, H. Hirschmüller, Y. Kitajima, G. Krathwohl, N. Nesic, X. Wang, and P. Westling. High-resolution stereo datasets with subpixel-accurate ground truth. In German Conference on Pattern Recognition (GCPR 2014), Münster, Germany, September 2014.