Visible to Intel only — GUID: GUID-26F1324C-E23E-4680-9A82-68397A42D87F
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
Collaborative Visual SLAM
Collaborative Visual SLAM with Two Robots
Collaborative Visual SLAM with FastMapping Enabled
Collaborative Visual SLAM with GPU Offloading
Collaborative Visual SLAM on Intel® Atom® Processor-Based Systems
Troubleshooting
Kudan Visual SLAM
FastMapping Algorithm
ADBSCAN Algorithm
ITS Path Planner ROS 2 Navigation Plugin
ITS Path Planner Plugin Customization
Intel® oneAPI Base Toolkit Sample Application
Robot Teleop Using a Gamepad
Robot Teleop Using a Keyboard
Visible to Intel only — GUID: GUID-26F1324C-E23E-4680-9A82-68397A42D87F
Collaborative Visual SLAM
Collaborative visual SLAM is compiled natively for both Intel® Core™ and Intel® Atom® processor-based systems. By default, in this tutorial, the Intel® Core™ processor-based system version is used. If you are running an Intel® Atom® processor-based system, you must make the changes detailed in Collaborative Visual SLAM on Intel® Atom® Processor-Based Systems for collaborative visual SLAM to work.
Collaborative Visual SLAM with Two Robots: uses as input two ROS 2 bags that simulate two robots exploring the same area
The ROS 2 tool rviz2 is used to visualize the two robots, the server, and how the server merges the two local maps of the robots into one common map.
The output includes the estimated pose of the camera and visualization of the internal map.
All input and output are in standard ROS 2 formats.
Collaborative Visual SLAM with FastMapping Enabled: uses as an input a ROS 2 bag that simulates a robot exploring an area
Collaborative visual SLAM has the FastMapping algorithm integrated.
For more information on FastMapping, see How it Works.
The ROS 2 tool rviz2 is used to visualize the robot exploring the area and how FastMapping creates the 2D and 3D maps.
Collaborative Visual SLAM with GPU Offloading
Offloading to the GPU only works on systems with 11th Generation Intel® Core™ processors with Intel® Iris® Xe Integrated Graphics.
Collaborative Visual SLAM also contains mapping and can operate in localization mode.
Collaborative Visual SLAM with Two Robots
Prerequisites:
The main input is a camera, either monocular or stereo or RGB-D.
IMU and odometry data are supported as auxiliary inputs.
Check if your installation has the amr-collab-slam Docker* image.
docker images |grep amr-collab-slam #if you have it installed, the result is: amr-collab-slam
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 re-installing the EI for AMR Robot 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=17
If the bags were not extracted before, do it now:
unzip 01_docker_sdk_env/docker_compose/06_bags.zip -d 01_docker_sdk_env/docker_compose/ sudo chmod 0777 -R 01_docker_sdk_env/docker_compose/06_bags
Run the collaborative visual SLAM algorithm using two bags simulating two robots going through the same area:
CHOOSE_USER=eiforamr docker-compose -f 01_docker_sdk_env/docker_compose/05_tutorials/cslam.tutorial.yml up
Expected result: On the server rviz2, both trackers are seen.
Red indicates the path robot 1 is taking right now.
Blue indicates the path robot 2 took.
Green indicates the points known to the server.
Collaborative Visual SLAM with FastMapping Enabled
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=17
If the bags were not extracted before, do it now:
unzip 01_docker_sdk_env/docker_compose/06_bags.zip -d 01_docker_sdk_env/docker_compose/ sudo chmod 0777 -R 01_docker_sdk_env/docker_compose/06_bags
Run the collaborative visual SLAM algorithm with FastMapping enabled:
docker-compose -f 01_docker_sdk_env/docker_compose/05_tutorials/collab-slam-fastmapping.tutorial.yml up
Expected result: On the opened rviz2, you see the visual SLAM keypoints, the 3D map, and the 2D map.
You can disable the /univloc_tracker_0/local_map, univloc_tracker_0/fused_map, or both topics.
Visible Test: Showing keypoints, the 3D map, and the 2D map
Expected Result:
Visible Test: ``Showing the 3D map
Expected Result:
Visible Test: Map showing the 2D map
Expected Result:
Visible Test: Showing keypoints and the 2D map
Expected Result:
Collaborative Visual SLAM with GPU Offloading
Check if your installation has the amr-collab-slam Docker* image.
docker images |grep amr-collab-slam-gpu #if you have it installed, the result is: amr-collab-slam-gpu
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=17
If the bags were not extracted before, do it now:
unzip 01_docker_sdk_env/docker_compose/06_bags.zip -d 01_docker_sdk_env/docker_compose/ sudo chmod 0777 -R 01_docker_sdk_env/docker_compose/06_bags
Run the collaborative visual SLAM algorithm with GPU offloading:
docker-compose -f 01_docker_sdk_env/docker_compose/05_tutorials/collab-slam-gpu.tutorial.yml up
Expected result: On the opened rviz2, you see the visual SLAM keypoints, the 3D map, and the 2D map
On a different terminal, check how much of the GPU is using intel-gpu-top.
sudo apt-get install intel-gpu-tools sudo intel_gpu_top
To close this execution, close the rviz2 window, and press Ctrl-c in the terminal.
Clean up the Docker* images:
docker-compose -f 01_docker_sdk_env/docker_compose/05_tutorials/collab-slam-gpu.tutorial.yml down --remove-orphans
Collaborative Visual SLAM on Intel® Atom® Processor-Based Systems
Open the collaborative visual SLAM yml file for editing (depending on which tutorial you want to run, replace <collab-slam-tutorial> with cslam, collab-slam-fastmapping, or collab-slam-gpu):
cd <edge_insights_for_amr_path>/Edge_Insights_for_Autonomous_Mobile_Robots_*/AMR_containers gedit 01_docker_sdk_env/docker_compose/05_tutorials/<collab-slam-tutorial>.tutorial.yml
Replace this line:
source /home/eiforamr/workspace/ros_entrypoint.sh
With these lines:
unset CMAKE_PREFIX_PATH unset AMENT_PREFIX_PATH unset LD_LIBRARY_PATH source /home/eiforamr/workspace/CollabSLAM/prebuilt_collab_slam_atom/setup.bash
Troubleshooting
If the tracker (univloc_tracker_ros) fails to start, giving this error:
amr-collab-slam | [ERROR] [univloc_tracker_ros-2]: process has died [pid 140, exit code -4, cmd '/home/eiforamr/workspace/CollabSLAM/prebuilt_collab_slam_core/univloc_tracker/lib/univloc_tracker/univloc_tracker_ros --ros-args -r __node:=univloc_tracker_0 -r __ns:=/ --params-file /tmp/launch_params_zfr70odz -r /tf:=tf -r /tf_static:=tf_static -r /univloc_tracker_0/map:=map'].
See Collaborative Visual SLAM on Intel® Atom® Processor-Based Systems.
The odometry feature use_odom:=true does not work with these bags.
The ROS 2 bags used in this example do not have the necessary topics recorded for the odometry feature of collaborative visual SLAM.
If the use_odom:=true parameter is set, the collab-slam reports errors.
The bags fail to play.
The collab_slam Docker* is started with the local user and needs access to the ROS 2 bags folder.
Make sure that your local user has read and write access to this path: <path to edge_insights_for_amr>//Edge_Insights_for_Autonomous_Mobile_Robots_*/AMR_containers/01_docker_sdk_env/docker_compose/06_bags
The best way to do this is to make your user the owner of the folder. If the EI for AMR bundle was installed with sudo, chown the folder to your local user.
If the following error is encountered:
amr-collab-slam-gpu | [univloc_tracker_ros-2] /workspace/src/gpu/l0_rt_helpers.h:56: L0 error 78000001
The render group might be on a different ID then 109 which is placed in the yaml files used in the examples.
To find what ID the render group has on your system:
getent group render cat /etc/group |grep render
If the result is not render:x:109, change the yml file:
gedit 01_docker_sdk_env/docker_compose/05_tutorials/collab-slam-gpu.tutorial.yml # Change the at the line 26 from 109, to the number you got above.
For general robot issues, go to: Troubleshooting for Robot Tutorials.
Did you find the information on this page useful?
Feedback Message
Characters remaining: