Intel® Edge Software Device Qualification (Intel® Edge Software Device Qualification (Intel® ESDQ)) for Autonomous Mobile Robot

Overview

Intel® Edge Software Device Qualification (Intel® Edge Software Device Qualification (Intel® ESDQ)) for Autonomous Mobile Robot provides customers with the capability to run an Intel-provided test suite at the target system, with the goal of enabling partners to determine their platform’s compatibility with the Autonomous Mobile Robot.

The target of this self certification suite is the Autonomous Mobile Robot compute systems. These platforms are the brain of the Robot Kit. They are responsible to get input from sensors, analyze them, and give instructions to the motors and wheels to move the Autonomous Mobile Robot.

How It Works

The Autonomous Mobile Robot Test Modules interact with the Intel® Edge Software Device Qualification (Intel® ESDQ) Command Line Interface (CLI) through a common test module interface (TMI) layer which is part of the Intel® Edge Software Device Qualification (Intel® ESDQ) binary. Intel® Edge Software Device Qualification (Intel® ESDQ) generates a complete test report in HTML format, along with detailed logs packaged as one zip file, which you can manually choose to email to: edge.software.device.qualification@intel.com. More detailed information is available at Intel® Edge Software Device Qualification (Intel® ESDQ) Overview.

Note

Each test and its pass/fail criteria is described below. To jump to the installation process, go to Download and Install Intel® Edge Software Device Qualification (Intel® ESDQ) for Autonomous Mobile Robot.

Intel® Edge Software Device Qualification (Intel® ESDQ) for Autonomous Mobile Robot contains the following test modules.

Intel® RealSense™ Camera This module verifies the capabilities of the Intel® RealSense™ technology on the target platform. For more information, go to the Intel® RealSense™ website. The tests within this module verify that the following features are installed properly on the target platform and that Autonomous Mobile Robot and Intel® RealSense™ camera are functioning properly. The tests are considered PASS if: The Intel® RealSense™ SDK 2.0 libraries are installed on the target system. A simple C++ file can be compiled using the g++ compiler and the -lrealsense2 compilation flag. Intel® RealSense™ camera topics are listed and published. The number of FPS (Frames Per Second) are as expected.

Intel® VTune™ Profiler Profiler This module runs the Intel® VTune™ Profiler Profiler on the target system. For more information, go to the Intel® VTune™ Profiler website. The test is considered PASS if: VTune™ Profiler Profiler runs without errors. VTune™ Profiler Profiler collects Platform information.

rviz2 and FastMapping This module runs the FastMapping application (the version of octomap optimized for Intel® platforms) on the target system and uses rviz2 to verify that it works as expected. For more information, go to the rviz wiki. The test is considered PASS if: FastMapping is able to create a map out of a pre-recorded ROS 2 bag.

Intel® oneAPI Base Toolkit This module verifies some basic capabilities of Intel® oneAPI Base Toolkit on the target platform. For more information, go to the Intel® oneAPI Base Toolkit website. The tests within this module verify that the DPC++ compiler features are functioning properly on the target platform. This test is considered PASS if: A simple C++ file can be compiled using the DPC++ compiled and it runs as expected.

OpenVINO™ Toolkit This module verifies two core features of the OpenVINO™ Toolkit: OpenVINO™ model optimizer Object detection using TensorFlow model The test is considered PASS if: The OpenVINO™ model optimizer is capable to transform a TensorFlow model to an Intermediate Representation (IR) of the network, which can be inferred with the Inference Engine.

OpenVINO™ Query for inferencing devices This module executes the Hello Query Device C++ sample application of the OpenVINO™ toolkit. This application identifies all available devices that can be used for inferencing. The test is considered PASS if: The OpenVINO™ Hello Query Device sample application can identify the inferencing devices CPU and GPU. On Intel® Core™ Ultra Processors, in addition the NPU must be be identified as an inferencing device.

GStreamer Video This module verifies if a GStreamer Video Pipeline using GStreamer Plugins runs on the target system. The test is considered PASS if: The Video Pipeline was opened on the host without errors.

GStreamer Audio This module verifies if a GStreamer Audio Pipeline using GStreamer Plugins runs on the target system. The test is considered PASS if: The Audio Pipeline was opened on the host without errors.

GStreamer Autovideosink Plugin - Display This module verifies if a stream from a camera compatible with libv4l2 can be opened and displayed using GStreamer. The test is considered PASS if: No Error messages are displayed while running the gst-launch command. This test may Fail, or it may be skipped if the target system does not have a Web Camera connected.

ADBSCAN This module verifies if the ADBSCAN algorithm works on the target system. The test is considered PASS if: The ADBSCAN algorithm works on the target system.

Collaborative Visual SLAM This module verifies if the collaborative visual SLAM algorithm works on the target system. The test is considered PASS if: The collaborative visual SLAM algorithm works on the target system.

Get Started

This tutorial takes you through the installation and execution of the Intel® Edge Software Device Qualification (Intel® ESDQ) CLI tool. Configure your target system to satisfy the necessary Prerequisites before you proceed with the Download and Install Intel® Edge Software Device Qualification (Intel® ESDQ) for Autonomous Mobile Robot. Execute your self-certification process by selecting from the three available certification types:

• Run the Self-Certification Application for Compute Systems for certifying Intel®-based compute systems with the Autonomous Mobile Robot software

• Run the Self-Certification Application for RGB Cameras for certifying RGB cameras with the Autonomous Mobile Robot software

• Run the Self-Certification Application for Depth Cameras for certifying depth cameras with the Autonomous Mobile Robot software

Refer to How It Works for more detailed information about the test modules.

Prerequisites

Satisfy the Intel® Edge Software Device Qualification (Intel® ESDQ) prerequisites by:

• Installing OpenVINO™ Development Tools and specifying tensorflow as the extras parameter of the described “Step 4. Install the Package” instructions:

  pip install openvino-dev[tensorflow]
  

• Installing the intel-basekit Deb package by following the Intel® oneAPI Base Toolkit Installation Guide for Linux OS instructions.

• Installing GStreamer by following the “Install GStreamer on Canonical Ubuntu OS or Debian OS” instructions.

• Installing the pre-built Intel® RealSense™ SDK 2.0 packages librealsense2-utils, librealsense2-dev and librealsense2-dbg by following the “Installing the packages” instructions.

• Configuring your VTune™ Profiler installation as described in the “Additional System setup for CPU and GPU profiling” section of VTune™ Profiler for CPU and GPU profiling.

• Installing the OpenVINO™ Runtime by executing these steps:

  1. Add the OpenVINO™ APT package sources as described in section “OpenVINO™ Installation Steps” on page Install OpenVINO™ Packages.

  2. Make sure that your file /etc/apt/preferences.d/intel-openvino pins the OpenVINO™ version of all components to 2024.2.0* or above. Consider that earlier OpenVINO™ versions do not support the NPU of Intel® Core™ Ultra Processors.

  3. Install the OpenVINO™ Runtime by using:

     sudo apt-get install openvino
     

  Additional information can be found in the OpenVINO™ documentation.

• Installing the Intel® NPU Driver as described on page Install the Intel® NPU Driver on Intel® Core™ Ultra Processors. Don’t execute this step if your system does not have an Intel® Core™ Ultra Processor.

Note

Make sure that Git is installed on your target system.

Download and Install Intel® Edge Software Device Qualification (Intel® ESDQ) for Autonomous Mobile Robot

Complete the following two installation steps in order to properly configure your test setup:

1. Download and Install Intel® Edge Software Device Qualification (Intel® ESDQ) CLI

2. Download and Install the Test Modules

Download and Install Intel® Edge Software Device Qualification (Intel® ESDQ) CLI

Download the Intel® Edge Software Device Qualification (Intel® ESDQ) CLI to your device from here: edge-software-device-qualification-11.0.0.zip

Set the ESDQ_INSTALLATION variable to point to the desired installation location. For example, if you want to install the the Intel® Edge Software Device Qualification (Intel® ESDQ) CLI under the ~/esdq directory, just set the this variable as follows:

export ESDQ_INSTALLATION=~/esdq
mkdir $ESDQ_INSTALLATION

Directly from the download directory, unzip the downloaded file into the installation location.

unzip edge-software-device-qualification-11.0.0.zip -d $ESDQ_INSTALLATION

Set the convenient ROBOTICS_SDK variable that is going to be used in the next installation steps.

export ROBOTICS_SDK=$ESDQ_INSTALLATION/edge-software-device-qualification-11.0.0/

Install the Intel® Edge Software Device Qualification (Intel® ESDQ) CLI executing the following commands:

cd $ROBOTICS_SDK
./setup.sh -i
export PATH=$PATH:$HOME/.local/bin

Check the successful installation of the Intel® Edge Software Device Qualification (Intel® ESDQ) CLI verifying that the execution of the following command prints Version: 11.0.0 on the terminal:

esdq --version

Download and Install the Test Modules

To download and install the Autonomous Mobile Robot test modules on your target device follow the steps below:

1. Install the ros-humble-amr-esdq Deb package from Intel® Autonomous Mobile Robot APT repository.

   sudo apt update
   sudo apt install ros-humble-amr-esdq
   

2. The tests are conducted from the directory pointed by the previously set ROBOTICS_SDK variable. Copy the installed test suite into the directory.

   cp -r /opt/ros/humble/share/amr-esdq/AMR_Test_Module/ $ROBOTICS_SDK/modules/
   

3. Verify the appropriate permissions for the test modules directory by executing the following command:

   cd $ROBOTICS_SDK
   chmod -R +xw  modules/AMR_Test_Module
   

4. Check that the Autonomous Mobile Robot test module is correctly installed by verifying that the output of the following command lists the Robotics_SDK module.

   esdq module list
   

5. Download the necessary assets required by the test suite.

   esdq --verbose module run Robotics_SDK --arg download
   

Run the Self-Certification Application for Compute Systems

1. Use the groups command to verify whether the current user belongs to the render, video, and dialout groups. If the user does not belong to these groups, add the group membership:

   sudo usermod -a -G render,video,dialout $USER
   

   Log out and log in again.

2. If you have just installed the ros-humble-amr-esdq Deb package as described in the Download and Install Intel® Edge Software Device Qualification (Intel® ESDQ) for Autonomous Mobile Robot section, reboot your system.

   Otherwise, there is a possibility that the tests that depend on the GPU ORB Extractor encounter issues accessing the GPU.

3. Make sure that the environment variable ROBOTICS_SDK is initialized as shown in Download and Install Intel® Edge Software Device Qualification (Intel® ESDQ) for Autonomous Mobile Robot and change the working directory:

   echo $ROBOTICS_SDK
   cd $ROBOTICS_SDK
   

4. If your system uses a Linux Kernel 6.7.5 or later, read the section GPU device is not detected with Linux Kernel 6.7.5 or later. If your system is impacted by this issue, export the following debug variables as a workaround:

   export NEOReadDebugKeys=1
   export OverrideGpuAddressSpace=48
   

5. Run the Intel® Edge Software Device Qualification (Intel® ESDQ) test, and generate the report:

   export ROS_DOMAIN_ID=19
   esdq --verbose module run Robotics_SDK
   

6. Visualize the report by opening the reports/report.html file in your browser.

   Expected output (These results are for illustration purposes only.)

   

   Note

   All the tests are expected to pass. The VTune™ Profiler test failure and the Intel® RealSense™ camera test skip above are shown for demonstration purposes only. For example, the Intel® RealSense™ camera test is skipped if no Intel® RealSense™ camera is connected to the target system.

   If individual test cases do not pass, you can check the detailed log files in folder $ROBOTICS_SDK/modules/AMR_Test_Module/output/.

Run the Self-Certification Application for RGB Cameras

This self-certification test expects the camera stream to be on the /camera/color/image_raw topic. This topic must be visible in rviz2 using the camera_color_frame fixed frame. If your camera ROS 2 node does not stream to that topic by default, use ROS 2 remapping to publish to that topic.

Note

The following steps use the Intel® RealSense™ camera’s ROS 2 node as an example. You must change the node to your actual camera’s ROS 2 node.

You can check your current configuration by:

1. Running the RGB camera node in a ROS 2 environment after setting the ROS_DOMAIN_ID.

   source /opt/ros/humble/setup.bash
   # set a unique id here that is used in all terminals
   export ROS_DOMAIN_ID=19
   ros2 launch realsense2_camera rs_launch.py camera_namespace:=/ &
   

2. Verifying the presence of the topic in the topic list.

   ros2 topic list
   

3. Once your configuration is set, you can proceed to run the Intel® Edge Software Device Qualification (Intel® ESDQ) test and generate the report.

   cd $ROBOTICS_SDK
   export ROS_DOMAIN_ID=19
   esdq --verbose module run Robotics_SDK --arg sensors_rgb
   

Run the Self-Certification Application for Depth Cameras

This self-certification test expects the camera stream to be on the /camera/depth/color/points and on the /camera/depth/image_rect_raw topics. These topics must be visible in rviz2 using the camera_link fixed frame. If your camera ROS 2 node does not stream to that topic by default, use ROS 2 remapping to publish to that topic.

Note

The following steps use the Intel® RealSense™ camera’s ROS 2 node as an example. You must change the node to your actual camera’s ROS 2 node.

You can check your current configuration by:

1. Running the depth camera node in a ROS 2 environment after setting the ROS_DOMAIN_ID.

   source /opt/ros/humble/setup.bash
   # set a unique id here that is used in all terminals
   export ROS_DOMAIN_ID=19
   ros2 launch realsense2_camera rs_launch.py pointcloud.enable:=true camera_namespace:=/ &
   

2. Verifying the presence of the topic in the topic list.

   ros2 topic list
   

3. Once your configuration is set, you can proceed to run the Intel® Edge Software Device Qualification (Intel® ESDQ) test and generate the report.

   cd $ROBOTICS_SDK
   export ROS_DOMAIN_ID=19
   esdq --verbose module run Robotics_SDK --arg sensors_depth
   

Send Results to Intel

Once the automated and manual tests are executed successfully, you can submit your test results and get your devices listed on the Intel® Edge Software Recommended Hardware site.

Send the zip file that is created after running Intel® Edge Software Device Qualification (Intel® ESDQ) tests to: edge.software.device.qualification@intel.com.

For example, after one of our local runs the following files were generated in the $ROBOTICS_SDK/reports/ directory: report.html and report.zip.

Troubleshooting

For issues, go to: Troubleshooting for Autonomous Mobile Robot Tutorials .

Support Forum

If you’re unable to resolve your issues, contact the Support Forum.