Isaac ROS Depth Segmentation

Overview

Isaac ROS Depth Segmentation provides NVIDIA NVIDIA-accelerated packages for depth segmentation. The isaac_ros_bi3d package uses the optimized Bi3D DNN model to perform stereo-depth estimation via binary classification, which is used for depth segmentation. Depth segmentation can be used to determine whether an obstacle is within a proximity field and to avoid collisions with obstacles during navigation.

Bi3D is used in a graph of nodes to provide depth segmentation from a time-synchronized input left and right stereo image pair. Images to Bi3D need to be rectified and resized to the appropriate input resolution. The aspect ratio of the image needs to be maintained; hence, a crop and resize may be required to maintain the input aspect ratio. The graph for DNN encode, to DNN inference, to DNN decode is part of the Bi3D node. Inference is performed using TensorRT, as the Bi3D DNN model is designed to use optimizations supported by TensorRT.

Compared to other stereo disparity functions, depth segmentation provides a prediction of whether an obstacle is within a proximity field, as opposed to continuous depth, while simultaneously predicting freespace from the ground plane, which other functions typically do not provide. Also unlike other stereo disparity functions in Isaac ROS, depth segmentation runs on NVIDIA DLA (deep learning accelerator), which is separate and independent from the GPU. For more information on disparity, refer to this page.

Note

This DNN is optimized for and evaluated with RGB global shutter camera images, and accuracy may vary on monochrome images.

Quickstarts

• Isaac ROS Depth Segmentation

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_bi3d
  • Quickstart
  • Try More Examples
  • Model Preparation
  • 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 6.1	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 12.6+

Docker

To simplify development, we strongly recommend leveraging the Isaac ROS Dev Docker images by following these steps. This streamlines 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
2024-12-10	Update to be compatible with JetPack 6.1
2024-09-26	Update for ZED compatibility
2024-05-30	Update to be compatible with JetPack 6.0
2023-10-18	Renamed repository to isaac_ros_depth_segmentation
2023-05-25	Performance improvements
2023-04-05	Source available GXF extensions
2022-08-31	Update to use latest model and to be compatible with JetPack 5.0.2
2022-06-30	Initial release