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UPS 6000 and UP Xtreme i11 Robot Kits
Create Your Own Robot Kit
Perception
Navigation
Simulation
Benchmarking and Profiling
EI for AMR Container on a Virtual Machine
Fibocom’s FM350 5G Module Integration
Change Existing and Add New Docker* Images to the EI for AMR SDK
Troubleshooting for Robot Tutorials
Spatial Partitioning and Search Operations with Octrees
Constructing a Convex Hull Polygon for a 3D Point Cloud
Detecting Specific Models and Their Parameters in 3D Point Clouds
Plane Model Segmentation
Surface Reconstruction with Intel® oneAPI Base Toolkit's Moving Least Squares (MLS)
Intel® oneAPI Base Toolkit's Iterative Closest Point (ICP)
KdTree Search Using Intel® oneAPI Base Toolkit's KdTree FLANN
Downsampling 3D Point Clouds with a Voxelized Grid
Filtering Point Clouds with a Passthrough Filter
PCL Optimizations Outside of Docker* Images
Optimized PCL Known Limitation
Optimized PCL Troubleshooting
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ROS 2 OpenVINO™ Toolkit Sample Application
This tutorial tells you how to run the segmentation demo application on both a static image and on a video stream received from a Intel® RealSense™ camera.
Run the Sample Application
Check if your installation has the amr-ros2-openvino Docker* image.
docker images |grep amr-ros2-openvino #if you have it installed, the result is: amr-ros2-openvino
NOTE:If the image is not installed, continuing with these steps triggers a build that takes longer than an hour (sometimes, a lot longer depending on the system resources and internet connection).If the image is not installed, Intel recommends installing the Robot Base Kit or Robot Complete Kit with the Get Started Guide for Robots.
Go to the AMR_containers folder:
cd <edge_insights_for_amr_path>/Edge_Insights_for_Autonomous_Mobile_Robots_*/AMR_containers
Prepare the environment setup:
source ./01_docker_sdk_env/docker_compose/common/docker_compose.source export CONTAINER_BASE_PATH=`pwd` export ROS_DOMAIN_ID=16
Launch the automated execution of the ROS 2 OpenVINO™ toolkit sample applications:
CHOOSE_USER=root docker-compose -f 01_docker_sdk_env/docker_compose/05_tutorials/ros2_openvino.tutorial.yml up
Expected output:
Execution of the object segmentation sample code input from the image: This takes one minute, and you can see the semantic segmentation being applied to the image.
Original image
Image with semantic object segmentation
Execution of the object segmentation sample code input from the Intel® RealSense™ camera topic: This requires a Intel® RealSense™ camera connected to the testing target. It takes one minute, and you can see the semantic segmentation being applied to the video stream received from a Intel® RealSense™ camera.
To close this, do one of the following:
Type Ctrl-c in the terminal where you did the up command.
Run this command in another terminal:
CHOOSE_USER=eiforamr 01_docker_sdk_env/docker_compose/05_tutorials/ros2_openvino.tutorial.yml down
How it Works
All of the commands required to run this tutorial are documented in:
01_docker_sdk_env/docker_compose/05_tutorials/ros2_openvino.tutorial.yml
To use your own image to run semantic segmentation:
Copy your image into the AMR_containers folder at:
cp <path_to_image>/my_image.jpg 01_docker_sdk_env/docker_compose/05_tutorial/param/
Edit 01_docker_sdk_env/docker_compose/05_tutorials/ros2_openvino.tutorial.yml, at line 34, adding the following command:
cp ${CONTAINER_BASE_PATH}/01_docker_sdk_env/docker_compose/05_tutorials/param/my_image.jpg ../ros2_ws/src/ros2_openvino_toolkit/data/images/Edit 01_docker_sdk_env/docker_compose/05_tutorials/param/pipeline_segmentation_image.yaml to change the input_path:, line 4:
input_path: /home/eiforamr/ros2_ws/src/ros2_openvino_toolkit/data/images/my_image.jpg
Run the automated yml:
CHOOSE_USER=root docker-compose -f 01_docker_sdk_env/docker_compose/05_tutorials/ros2_openvino.tutorial.yml up
Expected result: Execution of semantic segmentation on the image you selected
Troubleshooting
For general robot issues, go to: Troubleshooting for Robot Tutorials.
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