Visible to Intel only — GUID: GUID-44966531-B356-4F3E-A214-6FC24F2EA6F2
Run a ROS 2 Sample Application in the Docker* Container
Run an Intel® RealSense™ ROS 2 Sample Application in Docker* Container
Run a GStreamer* Video Pipeline using GStreamer* Plugins in Docker* Container
Run a GStreamer* Audio Pipeline using GStreamer* Plugins in Docker* Container
Run a GStreamer* Video Pipeline using libv4l2 in Docker* Container
Run a GStreamer* Video Pipeline using the Intel® RealSense™ Plugin in the Docker* Container
Optional (Only for Sony* IMX390 Setups): Run a GStreamer* Video Pipeline using Sony’s IMX390 MIPI Sensor in Docker* Container
Run OpenVINO™ Sample Applications in Docker* Container
Run the Sample Application
Troubleshooting
Run ROS 2 OpenVINO™ Toolkit Sample Applications in Docker* Container
Run Intel® oneAPI Base Toolkit Sample Applications in Docker* Container
Run Profiling Application in Docker* Container with VTune™ Profiler
Run OpenVINO™ Benchmarking Tool
Run the ADBSCAN Algorithm
Launch Wandering Application with Gazebo* Simulation
Launch Wandering Application on AAEON* Robot Kit
Launch Cartographer with 2D LIDAR
Run FastMapping Algorithm
Run ROS 2 Navigation Sample Applications Using the ITS Path Planner Plugin in a Docker* Container
ITS Path Planner Plugin Customization
Run the Edge Insights for Autonomous Mobile Robots Container in KVM Guest
Run a Collaborative SLAM System
Build New and Custom Docker* Images from the Edge Insights for Autonomous Mobile Robots SDK
Troubleshooting for Robot Tutorials
Visible to Intel only — GUID: GUID-44966531-B356-4F3E-A214-6FC24F2EA6F2
Run OpenVINO™ Sample Applications in Docker* Container
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
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/05_tutorials/config/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.demo.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.demo.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.demo.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.demo.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.demo.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.demo.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
For general robot issues, go to: Troubleshooting for Robot Tutorials.