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Discover a faster, easier way to build high-performance solutions with the NVIDIA Isaac™ Robot Operating System (ROS) collection of hardware-accelerated packages. It includes multiple open-source options and is designed to give ROS developers a whole new way to build on NVIDIA hardware such as NVIDIA® Jetson™.

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Key Benefits of Isaac ROS

High Throughput Perception

Isaac ROS provides individual packages (GEMs) and complete pipelines (NITROS) with image-processing and computer vision functionality. These solutions have been highly optimized for NVIDIA GPUs and NVIDIA Jetson platforms.

Modular, Flexible Packages

Modular packages let any ROS developer take exactly what they need to integrate in an application. This means you can now replace an entire pipeline or simply swap out a single algorithm.

Reduced Development Times

Isaac ROS is similar to existing and familiar ROS 2 nodes, making it easier to integrate into existing applications.

A Rich Collection of Perception AI Packages

Access the full range of ROS 2 nodes that combine common image processing and computer vision. These include DNN-based algorithms that are key to delivering high-performance perception and hardware acceleration to ROS-based robotics applications.

 Perception AI packages for Isaac ROS developers

What's New

We’ve open-sourced multiple Isaac ROS packages to help you modify and add new hardware acceleration functionalities to ROS 2. For details, check out the Isaac ROS release notes.

isaac ros occupancy grid map localizer

Occupancy Grid Map Localizer

Initial position of the robot is computed in less than one second—automatically—using the grid-search-localizer package. This eliminates the need to manually provide the initial position for navigation.

Upgraded Visual SLAM

Upgraded Visual SLAM

The updated VSLAM (visual simultaneous localization and mapping) package features the latest cuVSLAM library, offering support for the Leopard Imaging Hawk 3D Depth Camera and huge performance optimizations. You get up to 40% faster speeds than with NVIDIA Xavier™ and up to 4% speed over NVIDIA Orin™.

isaac ros nvblox human detection

NvBlox With Human Detection

Generate clean 3D mesh in a crowded environment with the updated NvBlox Isaac ROS package. It can then detect and remove humans from the frames that NvBlox processes.

NVIDIA Isaac Transport for ROS (NITROS)

 Hardware acceleration efficiency comparison for NITROS

The latest Humble ROS 2 release improves performance on compute platforms that offer hardware accelerators. It enables hardware-acceleration features for type adaptation and type negotiation, eliminating software/CPU overhead and improving performance of hardware acceleration.

The NVIDIA implementation of type adaption and negotiation is called NITROS. These are ROS processing pipelines made up of Isaac ROS hardware-accelerated modules (a.k.a. GEMs). The source code of NITROS is now available for developers to modify, extend, and use in your applications.

NVIDIA Isaac Transport for ROS

H.264 video encode and decode hardware-accelerated packages for NITROS are used for compressed camera data recording and playback for development of AI models and perception functions. They compress two 1080p stereo cameras at 30fps (>120fps total) and reduce the data footprint by ~10X.

Visual SLAM Based Localization

As autonomous machines move around in their environments, they must keep track of where they are. VSLAM provides a method for visually estimating the position of a robot relative to its start position, known as VO (visual odometry).The Isaac ROS GEM for VSLAM provides this powerful functionality to ROS 2 developers.

This GEM offers the best accuracy for a real-time stereo-camera VSLAM solution. You can find publicly available results based on the widely used KITTI database here. For the KITTI benchmark, the algorithm achieves a drift of ~1% in localization and an orientation error of 0.003 degrees per meter of motion. In addition to being very accurate, this GPU-accelerated package runs extremely fast. The package uses cuVSLAM library to find and match more key points in real-time, with fine tuning to minimize overall reprojection error. This is attained by using a combination of visual data and IMU measurements

Isaac ROS Stereo Visual SLAM
Isaac ROS Stereo Visual Odometry

3D Scene Reconstruction With NvBlox

nvblox for 3D scene reconstruction

Knowledge of a robot’s position alone isn't enough to safely navigate complex environments. Robots must also be able to discover obstacles on their own. NvBlox (preview) uses RGB-D data to create a dense 3D representation of the robot's environment. It includes unforeseen obstacles that could cause a danger to the robot if not observed in real time. This data helps generate a temporal costmap for navigation stack.

Isaac ROS NvBlox

DNN Inference Processing

DNN Inference GEM is a set of ROS 2 packages that lets developers use any of NVIDIA’s numerous inference models available on NGC, or even provide their own DNN. Further tuning of pre-trained models or optimizations of developers' own models can be done with the NVIDIA TAO Toolkit.

After optimization, these packages are deployed by the NVIDIA TensorRT™ high-performance inference SDK or Triton™ , NVIDIA’s inference server. If the desired DNN model isn't supported by TensorRT, then Triton can be used to deploy the model.

Additional GEMs incorporating model support are available and include support for U-Net and DOPE. The U-Net package, based on TensorRT, can be used for generating semantic segmentation masks from images and the DOPE package can be used for 3D pose estimation for all detected objects.

DNN Inference GEM is the fastest way to incorporate performant AI inference in a ROS 2 application. The pretrained model—PeopleSemSegNet, pictured in the image (right)—runs at 325fps @544p on NVIDIA Jetson AGX Orin.

Isaac ROS DNN Inference
Isaac ROS Pose Estimation
Isaac ROS Image Segmentation
DNN Inference GEM processing

Stereo Perception

DNN for stereo camera disparity prediction

Stereo perception DNN-based GEMs are designed to help roboticists with common perception tasks.

Enhanced Semi-Supervised stereo disparity (ESS) is a DNN for stereo camera disparity prediction and Bi3D is a DNN for vision-based proximity detection.

Both are pretrained for robotics applications using synthetic data and are intended for commercial use.

Isaac ROS DNN Stereo Disparity
Isaac ROS Proximity Segmentation

High-Performance Perception With NITROS Pipelines

Boost performance with powerful pipelines that take advantage from hardware acceleration additions to ROS 2 Humble.
You can find a complete performance summary here

Input Size
AGX Orin
AGX Xavier
Orin NX
Orin Nano 8GB
x86_64 w/ RTX 4060 Ti
AprilTag Graph 720p 143 fps
16 ms
129 fps
22 ms
82.9 fps
22 ms
58.0 fps
31 ms
349 fps
11 ms
Freespace Segmentation Graph 576p 54.0 fps
36 ms
23.7 fps
150 ms
28.4 fps
120 ms
23.6 fps
140 ms
178 fps
30 ms
Centerpose Pose Estimation Graph VGA 50.2 fps
37 ms
10.2 fps
130 ms
23.7 fps
70 ms
18.4 fps
87 ms
45.0 fps
21 ms
DOPE Pose Estimation Graph VGA 40.5 fps
31 ms
12.5 fps
170 ms
17.6 fps
120 ms
-- 90.9 fps
14 ms
DNN Stereo Disparity Graph 1080p 52.7 fps
21 ms
26.1 fps
41 ms
20.8 fps
50 ms
-- 156 fps
10 ms
Stereo Disparity Graph 1080p 162 fps
14 ms
90.5 fps
21 ms
75.1 fps
20 ms
50.6 fps
28 ms
387 fps
8.3 ms
DetectNet Object Detection Graph 544p 248 fps
9.1 ms
94.0 fps
16 ms
117 fps
14 ms
20 ms
589 fps
4.3 ms
TensorRT Graph
544p 385 fps
6.9 ms
228 fps
10 ms
210 fps
8.3 ms
142 fps
13 ms
827 fps

Mission Dispatch and Client

Isaac ROS Mission Dispatch and Client cloud services

Isaac Mission Dispatch allows a cloud/edge system to send and monitor tasks from a ROS 2 robot with Isaac Mission Client using industry standards for production deployments. Mission Dispatch is a cloud-native microservice that can be integrated as part of larger fleet management systems.

Mission Dispatch and Mission Client are both available in open source and can be used to test robots in simulation for automating test portions of continuous integration and continuous deployments (CI/CD), performing a series of predefined tasks evaluated against expected results. This benefit is in addition to the primary usage of assigning tasks to robots in operation.

Mission Dispatch can be integrated into fleet management systems (e.g., Anyfleet, Roborunner FleetGateway) with Mission Client on the ROS 2 robot. It will also interoperate with other ROS 2 Clients built on VDA5050.

Isaac ROS Mission Dispatch
Isaac ROS Mission Client

Camera/Image Processing

Isaac ROS camera/image processing

The image shows a lens-distorted camera image (left) and rectified image using LDC GEM (right).

In a typical robotics image-processing pipeline, raw data from the camera sensor must be processed before being passed off to a DNN or classic computer vision module for perception processing. This image processing consists of things like Lens Distortion Correction (LDC), image resizing, and image format conversion. If stereo cameras are involved, then estimating disparity is also required. The image processing GEMs have been designed to take advantage of the specialized computer vision hardware available in Jetson solutions, like the GPU, the VIC (Video and Image Compositor), and the PVA (Programmable Vision Accelerator).

For robots using cameras connected via a CSI interface, NVIDIA provides the Argus package for ROS hardware acceleration.

Isaac ROS Image Processing

Isaac ROS Camera Partners

Isaac ROS partners offer drivers that seamlessly integrate with the Isaac ROS GEMs for ROS hardware acceleration. You can see complete list of drivers and compatible hardware here.

Isaac ROS Camera Partner - Leopard Imaging
Isaac ROS Camera Partner - D3 Engineering
Isaac ROS Camera Partner - Framos

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