NVIDIA Isaac ROS in under 5 minutes

At ROSCon 2023, NVIDIA announced the newest Isaac ROS software release, Isaac ROS 2.0. With over two years of dedicated development in the rearview mirror, NVIDIA continues to propel the acceleration of robotics and AI applications on the NVIDIA Jetson platform.

NVIDIA Isaac ROS is a powerful and versatile robotics platform that enables developers to quickly create, deploy, and test robotics applications. It is the result of NVIDIA’s work with the open-source Robot Operating System (ROS) Humble release. Isaac ROS is designed to make it easier for developers to create and simulate robots that can interact with their environment, recognize objects and navigate. Also, NVIDIA is putting a lot of effort to port robotic algorithms to the GPU, allowing for real-time, on-robot data processing and control.

In this article, we’ll give you an overview of Isaac ROS, exploring its features and benefits, and provide a summary of the platform in under 5 minutes !

Isaac ROS 2.0 in a nutshell

Isaac ROS consists of traditional ROS2 nodes and Isaac specific, GPU accelerated packages (GEMs) for hardware-accelerated performance. It fully replaces the older NVIDIA Isaac SDK releases.

Isaac ROS is:

  • A ROS2 software stack containing popular deep-learning and GPU accelerated robotics & vision libraries.
  • A set of open source repositories hosted on GitHub which are licensed under the Apache License 2.0, building on top of the NVIDIA Isaac SDK.
  • Designed specifically to work on the NVIDIA Jetson Orin boards (Nano and up!), but can also work on x86 architectures with an NVIDIA GPU or the previous generation Jetson AGX Xavier boards.
  • Only compatible with the ROS2 Humble release and later
  • Running GEMs (think nodelets for ROS1 connaisseurs) in a single process which are CPU+GPU optimized using the NITROS acceleration architecture

Now let’s dive into the most important parts of this software stack.


ROS2 Humble introduces new hardware-acceleration features, including type adaptation and type negotiation, that significantly increase performance for developers seeking to incorporate AI/machine learning and computer vision functionality into their ROS-based applications. This makes the hardware accelerator zero-copy possible, as long as the nodes live in a single process, similar to the ROS 1 nodelets. This eliminates CPU copy overhead and uses the full potential of the underlying GPU. The NVIDIA implementation of type adaption and negotiation are called NITROS (NVIDIA Isaac Transport for ROS). NVIDIA lists these assumptions:

  • To leverage the benefit of zero-copy in NITROS, all NITROS-accelerated nodes must run in the same process.
  • For a given topic in which type negotiation takes place, there can only be one negotiating publisher.
  • For a NITROS-accelerated node, received-frame IDs are assumed to be constant throughout the runtime.

NVIDIA introduced in the 2.0 release an Isaac ROS NITROS bridge which allows ROS applications that use the original ROS Bridge between ROS 1 and ROS 2 nodes to make use of the GPU zero-copy as well.

Visual SLAM

This ROS2 package performs visual simultaneous localization and mapping (VSLAM) and estimates visual inertial odometry (VIO) using the Isaac cuVSLAM GPU-accelerated library. It takes in a pair of stereo images and camera parameters to publish the current position of the camera relative to its start position. cuVSLAM is not the best performer on the KITTI benchmark, but gives a good, GPU accelerated, starting point for exploratory purposes.

You may learn more about VSLAM on our VSLAM and Navigation page or visit our in-depth review of the cuVSLAM ROS package.

Mission Dispatch and Client

This open-source CPU-only package allows you to assign and monitor tasks from a fleet management system, which uses MQTT, to the robot, which uses ROS2. Mission Dispatch is a cloud-native microservice that can be integrated as part of larger fleet management systems, more specifically, it is an implementation of the VDA5050 standard for AGVs and mobile robots.

Built-in support for various DNN inference models

Isaac ROS contains six built-in Deep Neural Network (DNN) inference models:

  • Stereo disparity estimation,
  • Semantic image segmentation,
  • Object detection including DetectNet,
  • 3D object pose estimation,
  • Proximity segmentation and
  • Obstacle field ranging.

We highlight three repositories that we expect to be used in many robot setups:

ESS Stereo Disparity Estimation

There is a race going on between stereo camera providers to create the best depth images in any given environment. NVIDIA has contributed by providing the ESS Stereo Disparity Estimation DNN and did a major model update in the Isaac ROS 2.0 release. It takes 2 stereo image pairs, normalized by mean 0.5 and standard deviation 0.5 and each of size 576 by 960 pixels. The output will be a depth map and a confidence map with the same resolution. There is also a Light ESS network that works on half the pixel resolution (so a quarter of the size).

The ESS model was trained on 600,000 synthetically generated stereo images in rendered scenes from Omniverse, as well as about 25,000 real sensor frames collected using HAWK stereo cameras.

NVIDIA publishes benchmarks of this model, showing the FPS (ranging from 28FPS on AGX Xavier to 85FPS on AGX Orin) and measuring the % of bad pixels (ie bp2% and bp4%) in the disparity map, deviating from the ground truth disparity map.

Proximity Segmentation

Free space, near and far objects

Proximity segmentation can be used to determine whether an obstacle is within a proximity field and to avoid collisions with obstacles during navigation. Proximity segmentation predicts free space from the ground plane, eliminating the need to perform ground plane removal from the segmentation image.

This is used in turn to produce a 3D occupancy grid that indicates free space in the neighborhood of the robot. This camera-based solution offers a number of appealing advantages over traditional 360° lidar occupancy scanning, including better detection of low-profile obstacles.

Pose Estimation

DOPE works on pre-trained, known objects

Pose Estimation means calculating the 3D position and 3D orientation of an object by analyzing a (single) 2D image or 3D point cloud.

Using a deep learned pose estimation model and a 2D camera, there are two packages that can estimate the pose of a target object.

  • DOPE was created by NVlabs 5 years ago (https://github.com/NVlabs/Deep_Object_Pose) and stands for Deep Object Pose Estimation. It does 6-DoF pose estimation of known objects from a 2D camera. The DOPE network has been trained on the following objects: cracker box, sugar box, tomato soup can, mustard bottle, potted meat can, and a gelatin box. Training is not officially supported, but there are scripts available to do so.
  • The CenterPose network is a more recent NVlabs creation (https://github.com/NVlabs/CenterPose ) that was presented in ICRA 2022. It is a keypoint-based approach for category-level object pose estimation, which operates on unknown object instances within a known category using a single 2D image.

CenterPose works on known categories of unknown objects.

Support for the Pre-trained Models from NVIDIA GPU Cloud

The NVIDIA GPU Cloud (NGC) hosts a catalog of (encrypted) Deep Learning pre-trained models that can be converted to the Isaac ROS stack. The NGC platform includes pre-trained models and workflows for a variety of deep learning tasks, such as image recognition, natural language processing, and speech recognition.

TAO Transfer Learning

TAO is a set of tools and workflows designed to help developers adapt pre-trained deep learning models to their specific use cases. This is called Transfer Learning. The TAO toolkit decrypts NGC models, does data preparation, model adaptation, and optimization for deployment on NVIDIA hardware. By using the TAO toolkit, developers can quickly and easily customize pre-trained models for their specific needs, without having to start from scratch with their own training data.

H.264 Video Encode and Decode

Hardware-accelerated packages for compressed video data recording and playback. Video data collection is an important part of training AI perception models. The performance of these new GEMs on the NVIDIA Jetson AGX Orin platform can reduce the image data footprint by ~10x.

What is not included ?

While the NVIDIA Isaac ROS software release includes a range of features and packages that are useful for building autonomous mobile robots, there are still some capabilities that are not included and may need to be sourced from elsewhere. Here are a few examples:

Robot hardware

The Isaac ROS software is a set of software tools and packages designed to run on top of the NVIDIA Jetson CPU/GPU board. Developers will still need to source and build the actual robot hardware, including things like the chassis, wheels, motors, sensors, cameras and other components. Take a look at our previous blog post A Guide to Creating a Mobile Robot for Indoor Transportation for some tips and tricks there.

NVIDIA is providing 2 reference designs, one is the Carter robot and one is the Kaya robot.

The NVIDIA Carter 2.0 robot

Navigation algorithms

While the Isaac ROS software includes some packages related to navigation, such as Free Space Segmentation and VSLAM, the NVIDIA Isaac Navigation GEM has not been used in the ROS stack. In addition, the Isaac SDK depends on LIDAR for tasks like path planning and obstacle avoidance.

User interfaces

While the Isaac ROS software includes some packages for monitoring and managing the robot, developers may need to build custom user interfaces and dashboards for specific applications. The most popular visualization and interaction dashboards today are RViz, Robot Web Tools and Foxglove Studio.

Machine learning models

While the Isaac ROS software includes some packages for training and deploying machine learning models, developers may need to source or develop their own models or use the NVIDIA GPU Cloud (NGC) for specific tasks or applications.

It’s a wrap !

Okay, it maybe took a bit more than five minutes, but this is a fully packed release ;-)

Further reading:

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