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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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OpenVINO™ Sample Application
This tutorial tells you how to:
Run inference engine object detection on a pretrained network using the SSD method.
Run the detection demo application for a CPU and GPU.
Use a model optimizer to convert a TensorFlow* neural network model.
After conversion, run the neural network with inference engine for a CPU and GPU.
Run the Sample Application
Check if your installation has the eiforamr-openvino-sdk Docker* image.
docker images |grep eiforamr-openvino-sdk #if you have it installed, the result is: eiforamr-openvino-sdk
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 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_<version>/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=22
Run inference engine object detection on a pre-trained network using the Single-Shot multibox Detection (SSD) method. Run the detection demo application for a CPU:
CHOOSE_USER=root docker-compose -f 01_docker_sdk_env/docker_compose/05_tutorials/openvino_CPU.tutorial.yml up
Expected output: A video in a loop with cars being detected and labeled by the Neural Network using a CPU
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 docker-compose -f 01_docker_sdk_env/docker_compose/05_tutorials/openvino_CPU.tutorial.yml down
For an explanation of what happened, open the yml file. The file is well documented. To use your own files, place them in your home directory, and change the respective lines in the yml files to target them.
Run the detection demo application for the GPU:
CHOOSE_USER=root docker-compose -f 01_docker_sdk_env/docker_compose/05_tutorials/openvino_GPU.tutorial.yml up
Expected output: A video in a loop with cars being detected and labeled by the Neural Network using a GPU
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 docker-compose -f 01_docker_sdk_env/docker_compose/05_tutorials/openvino_GPU.tutorial.yml down
For an explanation of what happened, open the yml file. The file is well documented. To use your own files, place them in your home directory, and change the respective lines in the yml files to target them.
For system with an Intel® Movidius™ Myriad™ X accelerator, run the detection demo application on the Intel® Movidius™ Myriad™ X accelerator:
NOTE:Only execute this command on systems with an Intel® Movidius™ Myriad™ X accelerator.Check your system:
lsusb
Look for Intel Movidius MyriadX in the output.
CHOOSE_USER=root docker-compose -f 01_docker_sdk_env/docker_compose/05_tutorials/openvino_MYRIAD.tutorial.yml up
Expected output: A video in a loop with cars being detected and labeled by the Neural Network using the Intel® Movidius™ Myriad™ X accelerator.
NOTE:There is a known issue that if you choose to run the object_detection_demo using the –d MYRIAD option, a core dump error is thrown when the demo ends.If errors occur, remove the following file and try again:
rm -rf /tmp/mvnc.mutex
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 docker-compose -f 01_docker_sdk_env/docker_compose/05_tutorials/openvino_MYRIAD.tutorial.yml down
For an explanation of what happened, open the yml file. The file is well documented. To use your own files, place them in your home directory, and change the respective lines in the yml files to target them.
Troubleshooting
If running the yml file gets stuck at downloading:
gedit 01_docker_sdk_env/docker_compose/05_tutorials/openvino_CPU.tutorial.yml # In the same way open any other yml you want to test behind a proxy.
Add the following lines after echo echo “*** Set up the OpenVINO environment ***”, replacing http://<http_proxy>:port with your actual environment http_proxy.
export http_proxy="http://<http_proxy>:port" export https_proxy="http://<https_proxy>:port"
For general robot issues, go to: Troubleshooting for Robot Tutorials.
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