Isaac ROS Argus Camera
ROS 2 package for sensor processing to output images
-Image sensors are connected on CSI and GMSL hardware interfaces to Jetson platforms
-This package uses dedicated hardware engines to accelerate image processing
-Output images are used in?
Graphs of nodes for AI and CV perception packages
Image compression for capture to disk by event recorders
Live-stream visuals for remote robot teleoperation
Footnote
Accelerated Image Processing:
Package utilizes dedicated hardware engines to accelerate the processing of images from the connected sensors
This acceleration helps in efficiently handling the data from these sensors
Graphs of Nodes for AI and Computer Vision (CV) Perception Packages:
The primary use of the output images is within the ROS 2 ecosystem.
These images are often used as input data for various nodes or modules that perform AI(Artificial Intelligence) and CV(Computer Vision) tasks
Ex. These images can be processed by algorithms that identify objects, track motion, or perform any other perception-related tasks in the context of robotics and autonomous systems
The output images serve as visual input to these algorithms to make decisions and take actions
Image Compression for Capture to Disk by Event Recorders:
Another important use of the output images is for event recording
Images can be compressed and stored on disk by event recorders
This is valuable for later analysis, debugging, or reviewing the data generated by the systemc
It provides a way to capture and store visual information for post-processing or archival purposes
Live-Stream Visuals for Remote Robot Teleoperation:
Output images can also be used for live-streaming visual data
This is particularly useful for remote teleoperation of robots
By transmitting the images in real-time, operators or remote users can gain visual feedback from the robot’s environment, allowing them to control the robot more effectively and make informed decisions
The “Isaac ROS Argus Camera” package processes images from sensors connected to Jetson platforms and provides these images as output
These images are utilized for AI and CV perception tasks, event recording, and live-streaming, making them a crucial part of the sensory data processing pipeline in robotic applications
-Isaac ROS Argus Camera provides with several sensor capture and processing features
Including AWB(auto-white-balance), AE(auto-exposure), and noise reduction
Leveraging hardware engines in Jetson, Argus provides multi-camera frame synchronization, with very high precision frame acquisition timestamping and jitter less than 100us
Example graph of nodes
The Argus Camera module processes sensor image data from the camera for input to vision-based perception graphs, including DNN stereo disparity, AprilTag, VSLAM, and H.264 encode
Each of the nodes in green is GPU accelerated for a high-performance compute graph from Argus camera to vision-based perception functions
-Argus Camera uses dedicated hardware engines to access the full memory bandwidth in Jetson
Raw camera images are delivered via CSI or GMSL interfaces directly to the GPU accelerated memory
The ISP hardware processes the raw image directly into a GPU accelerated output image topic
-Widely available USB and Ethernet plug-in cameras can be used for robotics applications, but there is performance cost for this convenience
The I/O interface (USB or Ethernet) places the image from the camera directly into CPU-accessible memory
The camera driver makes a copy from the I/O interface using the CPU to make the image available to other applications
The Camera driver wrapper node in ROS performs another memory copy with the CPU from the driver to publish the image in ROS
Before a USB or Ethernet image arrives as a published topic, two CPU memory copy actions have been performed for every pixel
In contrast, the Argus Camera module processes sensor data into output image topics in ROS without the CPU touching a single pixel in the image
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Reference:
https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_argus_camera/index.html