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Hardware-accelerated, deep-learned freespace segmentation

Isaac ROS Freespace Segmentation

Hardware-accelerated, deep-learned freespace segmentation

Isaac ROS Freespace Segmentation Sample Output

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, 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 Freespace Segmentation Sample Output

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.

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), 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 8GB

x86_64 w/ RTX 4060 Ti

Freespace Segmentation Node



576p



1760 fps


1.2 ms

1410 fps


1.6 ms

1060 fps


2.3 ms

3500 fps


0.32 ms

Freespace Segmentation Graph



576p



45.9 fps


41 ms

27.6 fps


95 ms

21.3 fps


110 ms

91.0 fps


30 ms


Documentation

Please visit the Isaac ROS Documentation to learn how to use this repository.


Packages

Latest

Update 2023-10-18: Updated for Isaac ROS 2.0.0.