Isaac ROS AprilTag

Overview

Isaac ROS AprilTag contains a ROS 2 package for detection of AprilTags, a type of fiducial marker that provides a point of reference or measure. AprilTag detections are GPU-accelerated for high performance.

A common graph of nodes connects from an input camera through rectify and resize to AprilTag. Rectify warps the input camera image into a rectified, undistorted output image; this node may not be necessary if the camera driver provides rectified camera images. Resize is often used to downscale higher resolution cameras into the desired resolution for AprilTags if needed. The input resolution to AprilTag is selected by the required detection distance for the application, as a minimum number of pixels are required to perform an AprilTag detection and classification. For example, an 8mp input image of 3840×2160 may be much larger than necessary and a 4:1 downscale to 1920x1080 could make more efficient use of compute resources and satisfy the required detection distance of the application. Each of the green nodes in the above diagram is GPU accelerated, allowing for a high-performance compute graph from Argus Camera to ApriTag. For USB and Ethernet cameras, the graph is accelerated from Rectify through AprilTag

As illustrated above, detections are provided in an output array for the number of AprilTag detections in the input image. Each entry in the array contains the ID (two-dimensional bar code) for the AprilTag, the four corners ((x0, y0), (x1, y1), (x2, y2), (x3, y3)) and center (x, y) of the input image, and the pose of the AprilTag.

Note

This package is a GPU-accelerated drop-in replacement for the CPU version of ROS AprilTag

Note

For more information, including the paper and the reference CPU implementation, refer to the AprilTag page

Quickstarts

• Isaac ROS AprilTag

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

• isaac_ros_apriltag
  • Quickstart
  • Try More Examples
  • Troubleshooting
  • API

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 other than Humble are not supported. This package depends on specific ROS 2 implementation features that were introduced beginning with the Humble release. ROS 2 versions after Humble have not yet been tested.

Platform	Hardware	Software	Notes
Jetson	Jetson Orin	JetPack 6.0	For best performance, ensure that power settings are configured appropriately. Jetson Orin Nano 4GB may not have enough memory to run many of the Isaac ROS packages and is not recommended.
x86_64	Ampere or higher NVIDIA GPU Architecture with 8 GB RAM or higher	Ubuntu 22.04+	CUDA 12.2+

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
2024-09-26	Update for ZED compatibility
2024-05-30	Update to be compatible with JetPack 6.0
2023-10-18	Improved throughput performance, exposed new tuning parameters
2023-05-25	Performance improvements
2023-04-05	Source available GXF extensions
2022-10-19	Updated OSS licensing
2022-08-31	Update to be compatible with JetPack 5.0.2
2022-06-30	Update to use NITROS for improved performance
2021-11-15	Isaac Sim HIL documentation update
2021-11-15	Added launch file to work with Isaac Sim
2021-10-20	Migrated to NVIDIA-ISAAC-ROS
2021-08-11	Initial release to NVIDIA-AI-IOT