Isaac ROS Image Pipeline

https://media.githubusercontent.com/media/NVIDIA-ISAAC-ROS/.github/main/resources/isaac_ros_docs/repositories_and_packages/isaac_ros_image_pipeline/100_right.jpg/ https://media.githubusercontent.com/media/NVIDIA-ISAAC-ROS/.github/main/resources/isaac_ros_docs/repositories_and_packages/isaac_ros_image_pipeline/300_right_hallway2_rect.png/ https://media.githubusercontent.com/media/NVIDIA-ISAAC-ROS/.github/main/resources/isaac_ros_docs/repositories_and_packages/isaac_ros_image_pipeline/300_right_hallway2_gray_rect.png/

Overview

Isaac ROS Image Pipeline is a metapackage of functionality for image processing. Camera output often needs pre-processing to meet the input requirements of multiple different perception functions. This can include cropping, resizing, mirroring, correcting for lens distortion, and color space conversion. For stereo cameras, additional processing is required to produce disparity between left + right images and a point cloud for depth perception.

This package is accelerated using the GPU and specialized hardware engines for image computation, replacing the CPU-based image_pipeline metapackage. Considerable effort has been made to ensure that replacing image_pipeline with isaac_ros_image_pipeline on a Jetson or GPU is as painless a transition as possible.

Note

Some image pre-processing functions use specialized hardware engines, which offload the GPU to make more compute available for other tasks.

https://media.githubusercontent.com/media/NVIDIA-ISAAC-ROS/.github/main/resources/isaac_ros_docs/repositories_and_packages/isaac_ros_image_pipeline/isaac_ros_image_pipeline_nodegraph.png/

Rectify corrects for lens distortion from the received camera sensor message. The rectified image is resized to the input resolution for disparity, using a crop before resizing to maintain image aspect ratio. The image is color space converted to YUV from RGB using the luma channel (the Y in YUV) to compute disparity using SGM. This common graph of nodes can be performed without the CPU processing a single pixel using isaac_ros_image_pipeline; in comparison, using image_pipeline, the CPU would process each pixel ~3 times.

The Isaac ROS Image Pipeline metapackage offloads the CPU from common image processing tasks so it can perform robotics functions best suited for the CPU.

Quickstarts

Isaac ROS NITROS Acceleration

This package is powered by NVIDIA Isaac Transport for ROS (NITROS), which leverages type adaptation and negotiation to optimize message formats and dramatically accelerate communication between participating nodes.

Packages

Supported Platforms

This package is designed and tested to be compatible with ROS 2 Humble running on Jetson or an x86_64 system with an NVIDIA GPU.

Note

Versions of ROS 2 earlier than Humble are not supported. This package depends on specific ROS 2 implementation features that were only introduced beginning with the Humble release.

Platform

Hardware

Software

Notes

Jetson

Jetson Orin Jetson Xavier

JetPack 5.1.2

For best performance, ensure that power settings are configured appropriately.

x86_64

NVIDIA GPU

Ubuntu 20.04+ CUDA 11.8+

Docker

To simplify development, we strongly recommend leveraging the Isaac ROS Dev Docker images by following these steps. This will streamline your development environment setup with the correct versions of dependencies on both Jetson and x86_64 platforms.

Note

All Isaac ROS Quickstarts, tutorials, and examples have been designed with the Isaac ROS Docker images as a prerequisite.

Customize your Dev Environment

To customize your development environment, reference this guide.

Updates

Date

Changes

2023-10-18

Introduced confidence thresholding for SGM

2023-05-25

Improved stereo rectification

2023-04-05

Source available GXF extensions

2022-10-19

Updated OSS licensing

2022-08-31

Image flip support and update to be compatible with JetPack 5.0.2

2022-06-30

Migrated to NITROS based implementation

2021-10-20

Migrated to NVIDIA-ISAAC-ROS. Fixed handling of extrinsics in Rectify and Disparity nodes.

2021-08-11

Initial release to NVIDIA-AI-IOT