isaac_ros_freespace_segmentation

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isaac_ros_freespace_segmentation

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NVIDIA-accelerated, deep-learned freespace segmentation

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# Isaac ROS Freespace Segmentation

NVIDIA-accelerated, deep-learned freespace segmentation

## Overview

Isaac ROS Freespace Segmentation contains an ROS 2 package to produce
occupancy grids for navigation. By processing a freespace segmentation
mask with the pose of the robot relative to the ground, Bi3D Freespace
produces an occupancy grid for
[Nav2](https://github.com/ros-planning/navigation2), which is used to
avoid obstacles during navigation. This package is GPU accelerated to
provide real-time, low latency results in a robotics application. Bi3D
Freespace provides an additional occupancy grid source for mobile robots
(ground based).

`isaac_ros_bi3d` is used in a graph of nodes to provide a freespace
segmentation mask as one output from a time-synchronized input left and
right stereo image pair. The freespace mask is used by
`isaac_ros_bi3d_freespace` with TF pose of the camera relative to the
ground to compute planar freespace into an occupancy grid as input to
[Nav2](https://github.com/ros-planning/navigation2).

There are multiple methods to predict the occupancy grid as an input to
navigation. None of these methods are perfect; each has limitations on
the accuracy of its estimate from the sensor providing measured
observations. Each sensor has a unique field of view, range to provide
its measured view of the world, and corresponding areas it does not
measure. Bi3D Freespace provides a diverse approach to
identifying obstacles from freespace. Stereo camera input used for this
function is diverse relative to lidar, and has a better vertical field
of view than most lidar units, allowing for perception of low lying
obstacles that lidar can miss. Bi3D Freespace provides a
robust, vision-based complement to lidar occupancy scanning.

## Isaac ROS NITROS Acceleration

This package is powered by [NVIDIA Isaac Transport for ROS (NITROS)](https://developer.nvidia.com/blog/improve-perception-performance-for-ros-2-applications-with-nvidia-isaac-transport-for-ros/), which leverages type adaptation and negotiation to optimize message formats and dramatically accelerate communication between participating nodes.

## Performance

| Sample Graph | Input Size | AGX Orin | Orin NX | Orin Nano Super 8GB | x86_64 w/ RTX 4090 |
|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [Freespace Segmentation Node](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/benchmarks/isaac_ros_bi3d_freespace_benchmark/scripts/isaac_ros_bi3d_fs_node.py) | 576p | [3340 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_bi3d_fs_node-agx_orin.json) 1.7 ms @ 30Hz | [2530 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_bi3d_fs_node-orin_nx.json) 1.5 ms @ 30Hz | [2140 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_bi3d_fs_node-orin_nano.json) 1.9 ms @ 30Hz | [3500 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_bi3d_fs_node-x86-4090.json) 0.44 ms @ 30Hz |
| [Freespace Segmentation Graph](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/benchmarks/isaac_ros_bi3d_freespace_benchmark/scripts/isaac_ros_bi3d_fs_graph.py) | 576p | [40.3 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_bi3d_fs_graph-agx_orin.json) 79 ms @ 30Hz | [27.6 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_bi3d_fs_graph-orin_nx.json) 98 ms @ 30Hz | [31.8 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_bi3d_fs_graph-orin_nano.json) 55 ms @ 30Hz | [102 fps](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_benchmark/blob/main/results/isaac_ros_bi3d_fs_graph-x86-4090.json) 30 ms @ 30Hz |

---

## Documentation

Please visit the [Isaac ROS Documentation](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_freespace_segmentation/index.html) to learn how to use this repository.

---

## Packages

* [`isaac_ros_bi3d_freespace`](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_freespace_segmentation/isaac_ros_bi3d_freespace/index.html)
  * [Quickstart](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_freespace_segmentation/isaac_ros_bi3d_freespace/index.html#quickstart)
  * [Try More Examples](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_freespace_segmentation/isaac_ros_bi3d_freespace/index.html#try-more-examples)
  * [API](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_freespace_segmentation/isaac_ros_bi3d_freespace/index.html#api)

## Latest

Update 2024-12-10: Update to be compatible with JetPack 6.1

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