My colleague Lentin Joseph just launched a 10-day live bootcamp that teaches you how to build AI-based robots from scratch using ROS 2 and NVIDIA Isaac. He’s the author of 11 books on ROS and one of the top experts in the field.
If you’ve been following my tutorials here at AutomaticAddison, you know I focus on practical, hands-on robotics education. This bootcamp aligns perfectly with that philosophy: daily live sessions, real projects, and no fluff.
Check out the complete bootcamp details at robocademy.com.
Bootcamp Details
Duration: 10 consecutive days, 2-3 hours per day
Schedule: 9:00 PM – 12:00 AM IST
Starts: February 20, 2026
Format: Fully online, live instructor-led sessions
Language: English
What You’ll Learn
The bootcamp takes you from zero to building intelligent robotic systems:
Day 1 – Introduction to Robotics & AI: What robotics and AI actually are, types of robots (mobile, industrial, humanoid, drones), where ROS 2 and NVIDIA Isaac are used in the real world, and how robots sense, think, and act.
Day 2 – Prerequisites for ROS 2 and NVIDIA Isaac: Linux basics, essential terminal commands, installing ROS 2 and NVIDIA Isaac Sim, and Python fundamentals for ROS 2.
Day 3 – ROS 2 Core Concepts: Nodes, topics, messages, publishers and subscribers, ROS 2 CLI tools, and writing your first ROS 2 Python node.
Day 4 – URDF, Simulation, and TF: Understanding URDF, links and joints, visual and collision models, Robot State Publisher, and TF frames and transforms.
Day 5 – NVIDIA Isaac Sim Basics: Interface overview, creating simulation scenes, adding robots, physics and lighting, camera and LiDAR sensors.
Days 6-10 continue building on these foundations with more advanced topics and a hands-on mini project.
Why This Bootcamp Works
Live interactive sessions with real-time Q&A and doubt clearing
Hands-on, project-based learning where you actually build things
No physical robot required, everything runs in simulation
All sessions recorded so you can revisit or catch up if you miss a day
Certificate of completion after finishing the bootcamp and mini project
Post-bootcamp support including community access and continued guidance
Lifetime access to all course content and recordings
Hardware Requirements
You’ll need a laptop or desktop with an NVIDIA RTX GPU (6 GB+ VRAM). But here’s the good news: if you don’t have an RTX GPU, they’ll help you set up a cloud-based VM to run NVIDIA Isaac tools in your browser. The estimated VM cost for the entire bootcamp is around $10.
Why ROS 2 + NVIDIA Isaac Matters
The combination of ROS 2 and NVIDIA Isaac isn’t arbitrary. ROS 2 has become the de facto standard for robotics middleware, while Isaac provides the GPU-accelerated simulation capabilities that modern robotics development demands. If you’re building robots that need to operate in complex environments, whether that’s warehouses, hospitals, or manufacturing floors, you need both.
I’ve spent years working with ROS, and the transition to ROS 2 combined with proper simulation tools like Isaac is where the industry is headed. This bootcamp addresses that reality.
Your Instructor
Lentin Joseph brings 15+ years of industry experience in robotics software development and professional training. He’s the author of “Mastering ROS 2 for Robotics Programming” and 10+ other ROS books. His teaching style emphasizes practical skills and real-world application over theory.
Satisfaction Guarantee
They offer a 2-day satisfaction guarantee. If you feel the bootcamp isn’t right for you, you can request a full refund within the first 2 days.
Who This Is For
This bootcamp is designed for absolute beginners. No prior robotics or AI experience required. It’s ideal for:
Students who want industry-relevant skills beyond academic theory
Working engineers transitioning into robotics or upgrading from ROS 1
Entrepreneurs who need to understand the technical stack for robotics ventures
Anyone curious about robotics and AI who wants a structured starting point
If you’re already deep into ROS 2 and comfortable with Isaac, you might not need this. But if you’re looking for structured, live instruction with community support, it’s worth considering.
Bottom Line
I don’t promote many external courses, but Lentin’s track record in robotics education speaks for itself. A 10-day live bootcamp with daily hands-on sessions, lifetime access to recordings, and a satisfaction guarantee is a solid way to get started with ROS 2 and NVIDIA Isaac.
The bootcamp kicks off February 20, 2026. If you’re ready to start building robots, check it out at robocademy.com.
In this tutorial, we will use the MoveIt Task Constructor for ROS 2 to carry out a pick and place task. Here is what you will build by the end of this tutorial:
We’ll use a depth camera to dynamically detect and locate objects in the scene.
By the end of this tutorial, you’ll have created a vision-enhanced pick and place system that demonstrates the power of combining perception with sophisticated motion planning.
If you prefer to learn by video rather than text, follow the video below:
Here is what skills you will learn in this tutorial:
Learn how to integrate a simulated depth camera into Gazebo
Learn how to process point cloud data to detect and locate objects.
A point cloud is a collection of data points in 3D space that represent the surface of an object or environment.
Learn how to dynamically update the MoveIt planning scene based on visual information
Learn how to modify the MoveIt Task Constructor pipeline to use real-time object poses (positions and orientations)
Here is a high-level overview of what our enhanced program will do:
Set up the demo scene with randomly placed objects
Acquire and process point cloud data from the depth camera
Detect and localize objects in the scene
Update the planning scene with the detected objects
Define a pick sequence that includes:
Opening the gripper
Moving to a visually determined pre-grasp position
Approaching the target object
Closing the gripper
Lifting the object
Define a place sequence that includes:
Moving to a designated place location
Lowering the object
Opening the gripper
Retreating from the placed object
Plan the entire pick and place task using the updated scene information
Optionally execute the planned task
Provide detailed feedback on each stage of the process, including visual perception results
All the code is here on my GitHub repository. Note that I am working with ROS 2 Jazzy, so the steps might be slightly different for other versions of ROS 2.
Create a Package
Let’s create a package to store our code.
cd ~/ros2_ws/src/mycobot_ros2/
ros2 pkg create \
--build-type ament_cmake \
--dependencies \
generate_parameter_library \
libpcl-all-dev \
moveit_common \
moveit_core \
moveit_ros_planning \
moveit_ros_planning_interface \
moveit_task_constructor_core \
moveit_task_constructor_msgs \
pcl_conversions \
pcl_ros \
rclcpp \
sensor_msgs \
shape_msgs \
tf2_eigen \
tf2_geometry_msgs \
--license BSD-3-Clause \
--maintainer-name ubuntu \
--maintainer-email automaticaddison@todo.com \
--description "Pick and place demo using the MoveIt Task Constructor for motion planning and the Point Cloud Library for perception." \
mycobot_mtc_pick_place_demo
Type in your password, and install any missing dependencies.
Now build.
colcon build && source ~/.bashrc
Don’t worry about any build errors at this stage. We will fix those later.
Create a Launch File
Now let’s create some launch files. Here are all the launch files. You will add the code for each launch file below.
cd ~/ros2_ws/src/mycobot_ros2/mycobot_mtc_pick_place_demo/
Create a folder named launch.
mkdir launch && cd launch
Add your first launch file:
touch pick_place_demo.launch.py
This launch file launches the MoveIt Task Constructor node responsible for pick and place with perception.
touch point_cloud_viewer.launch.py
This launch file is created for viewing point cloud data (.pcd files) in RViz. It starts a node to convert PCD files to point clouds, allows configuration of file paths and publishing intervals, and launches RViz with a specific configuration for visualizing the point cloud data.
Save the file, and close it.
touch get_planning_scene_server.launch.py
This launch file starts the GetPlanningSceneServer node (we’ll go over this service in detail later in this tutorial), which is responsible for providing the current planning scene. It loads configuration parameters from a YAML file.
cd ~/ros2_ws/src/mycobot_ros2/mycobot_mtc_pick_place_demo/
mkdir config && cd config
touch mtc_node_params.yaml
Add the code, and save.
This YAML file contains configuration parameters for the MoveIt Task Constructor node. It includes settings for robot control, object manipulation, motion planning, and various timeout and scaling factors. These parameters define the behavior of the pick and place task, including grasp generation, approach distances, and cartesian motion settings.
Save the file, and close it.
touch get_planning_scene_server.yaml
This configuration file sets parameters for the GetPlanningSceneServer node. It includes settings for point cloud processing, plane and object segmentation, support surface detection, and various thresholds for filtering and clustering. These parameters are for processing sensor data and creating an accurate representation of the planning scene.
Save the file, and close it.
Add the Source Code
Below I will guide you through how I set up the source code. If you want to learn more details about the implementation of each piece of code, go to the Appendix.
cd ~/ros2_ws/src/mycobot_ros2/mycobot_mtc_pick_place_demo/src
touch mtc_node.cpp
Add the code.
This file implements the main MoveIt Task Constructor (MTC) node for the pick and place task. It sets up the planning scene, creates the MTC task with various stages such as move to pick, grasp, lift, and place. The file also handles the execution of the task, coordinating the robot’s movements to complete the pick and place operation.
Save the file, and close it.
touch cluster_extraction.cpp
This file contains functions for extracting clusters from a point cloud using a region growing algorithm. It helps in separating different objects in the scene by grouping points that likely belong to the same object. The extracted clusters can then be processed individually for object recognition or manipulation tasks.
Save the file, and close it.
touch get_planning_scene_client.cpp
This file implements a test client for the GetPlanningScene service. It’s responsible for requesting the current planning scene, which includes information about objects in the environment.
Save the file, and close it.
touch get_planning_scene_server.cpp
This file implements the server for the GetPlanningScene service. It processes point cloud and RGB image data to generate CollisionObjects for the MoveIt planning scene. These CollisionObjects represent the obstacles and objects in the robot’s environment, allowing for accurate motion planning and object manipulation.
Save the file, and close it.
touch normals_curvature_and_rsd_estimation.cpp
This file contains functions to estimate normal vectors, curvature values, and Radius-based Surface Descriptor (RSD) values for each point in a point cloud. These geometric features help in understanding the shape and orientation of surfaces in the scene. The estimated features can be used for tasks such as object recognition, segmentation, and grasp planning.
Save the file, and close it.
touch object_segmentation.cpp
This file does most of the heavy lifting. It implements object segmentation for the input 3D point cloud. It includes functions for fitting geometric primitives (cylinders and boxes) to point cloud data, which is used to identify and represent objects in the scene.
Save the file, and close it.
touch plane_segmentation.cpp
This file contains functions to segment the support plane and objects from a given point cloud. It identifies the surface on which objects are placed, separating it from the objects themselves. This segmentation is important for tasks such as determining where objects can be placed.
cd ~/ros2_ws/src/mycobot_ros2/mycobot_mtc_pick_place_demo/include/mycobot_mtc_pick_place_demo/
touch cluster_extraction.h
Save the file, and close it.
touch get_planning_scene_client.h
Save the file, and close it.
touch normals_curvature_and_rsd_estimation.h
Save the file, and close it.
touch object_segmentation.h
Save the file, and close it.
gedit plane_segmentation.h
Save the file, and close it.
These header files define the interfaces for the corresponding source files we created earlier. They contain function declarations, class definitions, and necessary include statements.
cd ~/ros2_ws/src/mycobot_ros2/mycobot_mtc_pick_place_demo/
mkdir scripts && cd scripts
touch pointcloud.sh
Save the file, and close it.
touch robot.sh
Save the file, and close it.
chmod +x pointcloud.sh
chmod +x robot.sh
These scripts will help you launch the necessary components for viewing point clouds and running the robot simulation with Gazebo, RViz, and MoveIt 2.
The pointcloud.sh script is designed to launch the robot in Gazebo and then view a specific point cloud file.
The robot.sh script launches the full setup including the robot in Gazebo, RViz for visualization, and sets up MoveIt 2 for motion planning. The chmod commands at the end make both scripts executable, allowing you to run them directly from the terminal.
Add the RViz Configuration File
Now let’s add the RViz configuration file.
cd ~/ros2_ws/src/mycobot_ros2/mycobot_mtc_pick_place_demo/
We haven’t built our package yet (using “colcon build”), but it is helpful to know how to test point cloud data in Gazebo once everything has been built.
To confirm the point cloud data is being generated successfully in Gazebo, you would run these commands:
Create a ROS 2 Service Interface for Point Cloud Processing for MoveIt Planning Scene Generation
Now let’s create a ROS 2 service that processes point cloud data to generate CollisionObjects for a MoveIt planning scene. This service will segment the input point cloud, fit primitive shapes to the segments, and create corresponding CollisionObjects. The service will also provide the necessary data for subsequent grasp generation should you decide to use a grasp generation strategy other than the one we will implement in this tutorial.
Here is a description of the service on a high level:
moveit_msgs::msg::PlanningSceneWorld: Contains CollisionObjects for all detected objects
sensor_msgs::msg::PointCloud2: Full scene point cloud
sensor_msgs::msg::Image: RGB image of the scene
std::string: ID of the target object in the PlanningSceneWorld
std::string: ID of the support surface in the PlanningSceneWorld
bool: Success flag
Create a Package
Let’s create a new package called mycobot_interfaces to store our custom service definition. This package will be used across your mycobot projects for custom message and service definitions.
After creating our custom service interface, it’s important to verify that it has been created correctly. Follow these steps to confirm the interface creation:
Open a new terminal.
Navigate to your workspace:
cd ~/ros2_ws
Use the ros2 interface show command to display the content of our newly created service:
ros2 interface show mycobot_interfaces/srv/GetPlanningScene
Remember, whenever you make changes to your interfaces, you need to rebuild the package and source your workspace again for the changes to take effect.
Now you have created a custom service interface for planning scene generation. This service will take a target shape and dimensions as input, and return a planning scene world, full point cloud, RGB image, target object ID, support surface ID (e.g. a table), and a success flag.
To use this service in other packages, add mycobot_interfaces as a dependency in the package.xml of the mycobot_mtc_pick_place_demo package where you want to use this service:
<depend>mycobot_interfaces</depend>
In your C++ code for the get_planning_scene_server, you would include the generated header:
You can then create service clients or servers using this interface.
This custom service interface provides a clear contract for communication between your point cloud processing node and other nodes in your system that need planning scene information.
Edit CMakeLists.txt
cd ~/ros2_ws/src/mycobot_ros2/mycobot_mtc_pick_place_demo/
Let’s build the code now. We will do this in stages due to all the developer warnings I encountered when trying to build the mycobot_mtc_pick_place_demo package the first time I did this.
First build all packages other then the mycobot_mtc_pick_place_demo package.
(OR source ~/ros2_ws/install/setup.bash if you haven’t set up your bashrc file to source your ROS distribution automatically with “source ~/ros2_ws/install/setup.bash”)
Now build the mycobot_mtc_pick_place_demo package:
Without the Wno-dev flag, you would see this warning:
CMake Warning (dev) at /usr/lib/x86_64-linux-gnu/cmake/pcl/Modules/FindFLANN.cmake:45 (find_package):
Policy CMP0144 is not set: find_package uses upper-case <PACKAGENAME>_ROOT
variables. Run "cmake --help-policy CMP0144" for policy details. Use the
cmake_policy command to set the policy and suppress this warning.
CMake variable FLANN_ROOT is set to:
/usr
For compatibility, find_package is ignoring the variable, but code in a
.cmake module might still use it.
Call Stack (most recent call first):
/usr/lib/x86_64-linux-gnu/cmake/pcl/PCLConfig.cmake:261 (find_package)
/usr/lib/x86_64-linux-gnu/cmake/pcl/PCLConfig.cmake:306 (find_flann)
/usr/lib/x86_64-linux-gnu/cmake/pcl/PCLConfig.cmake:570 (find_external_library)
CMakeLists.txt:55 (find_package)
This warning is for project developers. Use -Wno-dev to suppress it.
Just ignore it. Build the workspace again.
source ~/.bashrc
colcon build
source ~/.bashrc
Open a terminal window, and type the following command:
sudo sed -i 's/^\(\s*\)PCL_ERROR ("\[pcl::SampleConsensusModelPlane::isSampleGood\] Sample points too similar or collinear!\\n");/\1\/\/ PCL_ERROR ("[pcl::SampleConsensusModelPlane::isSampleGood] Sample points too similar or collinear!\\n");/' $(find /usr/include/pcl* -path "*/sample_consensus/impl/sac_model_plane.hpp")
This command comments out logging by the PCL library that happens during RANSAC. It silences this annoying warning:
[pcl::SampleConsensusModelPlane::isSampleGood] Sample points too similar or collinear!
Build one more time.
colcon build && source ~/.bashrc
Launch the Code
Finally…the moment you have been waiting for. Time to launch the code.
Let’s add two aliases. Open a terminal, and type these commands:
[Err] [Physics.cc:1773] Attempting to create a mimic constraint for joint [gripper_base_to_gripper_left2] but the chosen physics engine does not support mimic constraints, so no constraint will be created.
You can also ignore this warning:
Warning: class_loader.impl: SEVERE WARNING!!! A namespace collision has occurred with plugin factory for class rviz_default_plugins::displays::InteractiveMarkerDisplay. New factory will OVERWRITE existing one. This situation occurs when libraries containing plugins are directly linked against an executable (the one running right now generating this message). Please separate plugins out into their own library or just don't link against the library and use either class_loader::ClassLoader/MultiLibraryClassLoader to open.
Here is what you would see (I disabled the Marker Array in the Displays panel):
Camera title angle makes a big difference in the quality of the MoveIt planning scene. You can experiment with different values by modifying your URDF for the Intel RealSense camera.
Also, if you attempt to execute the plan, and your robot stalls as it tries to pick up the cylinder, experiment with different physics engine plugins for the SDF file for the Gazebo world.
If you want to visualize the raw 3D point cloud data, you can run the pointcloud.sh script. Make sure the files are in the appropriate location.
pointcloud
That’s it!
Deep Learning Alternatives for MoveIt Planning Scene Generation
In the service we developed to generate the collision objects for the planning scene, we fit primitive shapes to the 3D point cloud generated by our RGBD camera. We could have also generated the MoveIt planning scene using modern deep learning methods (I won’t go through this in this tutorial).
For example, you could use a package like isaac_ros_foundationpose to create 3D bounding boxes around objects in the scene and then add those boxes as collision objects. The advantage of this technique is that you have the pose of the object as well as the class information (e.g. mug, plate, bowl, etc.)
Here is what a Detection3D.msg message would look like if you were to type ‘ros2 topic echo <name_of_detection3d_topic>’:
Appendix: ROS 2 Service: Generating a MoveIt Planning Scene from a 3D Point Cloud
Overview
The method I used for generating the planning scene was inspired by the following paper:
Goron, Lucian Cosmin, et al. “Robustly segmenting cylindrical and box-like objects in cluttered scenes using depth cameras.” ROBOTIK 2012; 7th German Conference on Robotics. VDE, 2012.
If you get a chance, I highly recommend you read this entire paper. It provides a robust methodology for generating object primitives (i.e. boxes and cylinders) from a 3D point cloud scene even if the depth camera can only see the objects partially (i.e. from one side).
Image Source: Goron, Lucian Cosmin, et al. “Robustly segmenting cylindrical and box-like objects in cluttered scenes using depth cameras.” ROBOTIK 2012; 7th German Conference on Robotics. VDE, 2012.
Let’s go through the technical details of the ROS 2 service we created. The purpose of the service is to process point cloud and RGB image data to generate CollisionObjects for a MoveIt planning scene. I will cover how we segmented the input point cloud, fit primitive shapes to the segments, created corresponding CollisionObjects, and provided the necessary data for subsequent grasp generation.
moveit_msgs::msg::PlanningSceneWorld: Contains CollisionObjects for all detected objects
sensor_msgs::msg::PointCloud2: Full scene point cloud
sensor_msgs::msg::Image: RGB image of the scene
std::string: ID of the target object in the PlanningSceneWorld
std::string: ID of the support surface in the PlanningSceneWorld
bool: Success flag
Implementation Details
Point Cloud Preprocessing
Estimate the support plane for the objects in the scene and extract the points in the point cloud that are above the support plane (plane_segmentation.cpp)
The first step of the algorithm identifies the points that make up the flat surface (like a table) that objects are sitting on.
Input
Point cloud (pcl::PointCloud<pcl::PointXYZRGB>)
Output
Point cloud for the support plane
Point cloud for the points above the detected support plane.
Process
Estimate surface normals
A normal is a vector perpendicular to the surface at a given point. It provides information about the local orientation of the surface
For each point in the cloud, estimate the normal by fitting a plane to its k-nearest neighbors.
Store the computed normals, keeping them aligned with the original point cloud.
Identify potential support surfaces
Use the computed surface normals to find approximately horizontal surfaces.
Group points whose normals are approximately parallel to the world Z-axis (vertical). These points likely belong to horizontal surfaces like tables.
Perform Euclidean clustering on these points to get support surface candidate clusters.
Store the support surface candidate clusters
For each support surface candidate cluster:
Use RANSAC to fit a plane model
Validate the plane model based on the robot’s workspace limits
If cropping is enabled:
Check if the plane is within the cropped area:
If cropping is disabled:
Skip the position check
Check if the plane is close to z=0:
Define z_tolerance (e.g., 0.05 meters)
Ensure the absolute value of plane_center.z is less than z_tolerance
Verify the plane is approximately horizontal:
Define up_vector as (0, 0, 1)
Calculate dot_product between plane_normal and up_vector
Define angle_tolerance based on acceptable tilt (e.g., cos of 2.5 degrees)
Ensure dot_product is greater than angle_tolerance
If all applicable conditions are met, consider the plane model valid; otherwise, reject it
Select the best fitting plane as the support surface
From the set of validated plane models, choose the best candidate based on the following criteria:
Inlier count:
Define inlier_count for each plane model as the number of points that fit the model within a specified distance threshold
Prefer plane models with higher inlier_count, as they represent surfaces with more supporting points
Plane size:
Calculate the area of each plane model by finding the 2D bounding box of inlier points projected onto the plane
Prefer larger planes, as they are more likely to represent the main support surface
Distance to z=0:
Calculate z_distance as the absolute distance of the plane’s center to z=0
Prefer planes with smaller z_distance
Orientation accuracy:
Calculate orientation_score as the dot product between the plane’s normal and the up vector (0, 0, 1)
Prefer planes with higher orientation_score (closer to being perfectly horizontal)
Combine these factors using a weighted scoring system:
Define weights for each factor (e.g., w_inliers, w_size, w_distance, w_orientation)
Calculate a total_score for each plane model
Select the plane model with the highest total_score as the best fitting plane
Store the selected best_plane_model for further use in object segmentation
Return the results:
Create support_plane_cloud:
Extract all inlier points from the original point cloud that belong to the best_plane_model
Store these points in support_plane_cloud
Create objects_cloud:
For each point in the original point cloud:
If the point is above the best_plane_model (use the plane equation to check)
And if cropping is enabled, the point is within the crop boundaries
Add the point to objects_cloud
Return both support_plane_cloud and objects_cloud
References:
R. Mario and V. Markus, “Grasping of Unknown Objects from a Table Top,” in Workshop on Vision in Action: Efficient strategies for cognitive agents in complex environments, 2008.
R. B. Rusu, N. Blodow, Z. C. Marton, and M. Beetz, “Close-range Scene Segmentation and Reconstruction of 3D Point Cloud Maps for Mobile Manipulation in Human Environments,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), St. Louis, MO, USA, October 2009.
Estimate normal vectors, curvature values, and radius descriptor values for each point in the point cloud (normals_curvature_and_rsd_estimation.cpp)
Input
Point cloud (of type pcl::PointCloudpcl::PointXYZRGB)
Output
Point Cloud with Normal Vectors, Curvature Values, and Radius Descriptor Values
Each point has an associated 3D vector representing its normal vector (a 3D vector indicating the direction the surface is facing at that point)
Each point also has an additional value representing how curved the surface is at that point.
Each point has Radius-based Surface Descriptor (RSD) values (the minimum and maximum surface radius that can be fitted to the point’s neighborhood). This step determines whether a point belongs to a linear or circular surface.
Process
This approach provides a method for handling boundary points and refining normal estimation using MaximumLikelihoodSampleConsensus (MLESAC). It estimates normals, curvature, and RSD values for every point, using a more conservative approach for points identified as potentially being on a boundary.
For each point p in the cloud:
Find neighbors:
Identify the k nearest neighboring points around p (k_neighbors as input parameter)
These k points form the “neighborhood” of p.
Check if the point is a boundary point by seeing if it has fewer than k_neighbors neighbors.
For boundary points, adjust the neighborhood size to use a smaller number of neighbors (up to min_boundary_neighbors or whatever is available).
Estimate the normal vector:
Perform initial Principal Component Analysis (PCA) on the neighborhood:
This captures how the neighborhood points are spread out around point p.
The eigenvector corresponding to the smallest eigenvalue is an initial normal estimate.
Use Maximum Likelihood Estimation SAmple Consensus (MLESAC) to refine the local plane estimate:
MLESAC aims to refine the plane fitting by robustly estimating the best plane model and discarding outliers.
It uses the initial normal estimate as a starting point.
If MLESAC fails, fall back to the initial normal estimate.
Compute a weighted covariance matrix:
Assign weights to neighbor points based on their distance to the estimated local plane
Points closer to the plane and inliers from MLESAC get higher weights
This step helps to reduce the influence of outliers
Perform PCA on this weighted covariance matrix:
Compute eigenvectors and eigenvalues
The eigenvector corresponding to the smallest eigenvalue is the final normal estimate
Compute curvature values:
Use the eigenvalues from PCA on the weighted covariance matrix to calculate the curvature values according to the following formula: curvature = λ₀ / (λ₀ + λ₁ + λ₂) where λ₀ ≤ λ₁ ≤ λ₂
Use pcl::RSDEstimation to compute the minimum and maximum surface radius that can be fitted to the point’s neighborhood.
This step determines whether a point belongs to a linear or circular surface.
Output creation: Point cloud that includes:
The original XYZ coordinates
The RGB color information
The estimated normal vector for each point
The estimated curvature value for each point
The RSD values (r_min and r_max) for each point
Key Parameters
k_neighbors: Number of nearest neighbors to consider
max_plane_error: Maximum allowed error for plane fitting in MLESAC
max_iterations: Maximum number of iterations for MLESAC
min_boundary_neighbors: Minimum number of neighbors to consider for boundary points
rsd_radius: Radius to use for RSD estimation
References
R. B. Rusu, Z. C. Marton, N. Blodow, M. Dolha, and M. Beetz, “Towards 3D Point Cloud Based Object Maps for Household Environments,” Robotics and Autonomous Systems Journal (Special Issue on Semantic Knowledge in Robotics), vol. 56, no. 11, pp. 927–941, 30 November 2008.
Z. C. Marton, D. Pangercic, N. Blodow, and M. Beetz, “Combined 2D-3D Categorization and Classification for Multimodal Perception Systems,” International Journal of Robotics Research, 2011.
Cluster the point cloud into connected components using region growing based on nearest neighbors – cluster_extraction.cpp
Input
Point cloud (of type pcl::PointCloud<PointXYZRGBNormalRSD>::Ptr) – Point cloud with XYZ, RGB, normal vectors, curvature values, and Radius-based Surface Descriptor (RSD) values
Output
Vector of point cloud clusters, where each cluster is a separate point cloud containing:
Points that are close to each other in 3D space.
Each point retains its original attributes (xyz, RGB, normal, curvature, RSD values)
Process
Create a KdTree object for efficient nearest neighbor searches
Extract normals from the input cloud into a separate pcl::PointCloudpcl::Normal object
Create a RegionGrowing object and set its parameters:
Minimum and maximum cluster size
Number of nearest neighbors to consider
Smoothness threshold (angle threshold for neighboring normals)
Curvature threshold
Apply the region growing algorithm to extract clusters
For each extracted cluster:
Create a new point cloud
Copy points from the input cloud to the new cluster cloud based on the extracted indices
Set the cluster’s width, height, and is_dense properties
Key Points
Purpose: Reduce the search space for subsequent segmentation by grouping nearby points
Note: In cluttered scenes, objects that are touching or very close may end up in the same cluster
This step does not perform final object segmentation, but prepares data for later segmentation steps
The algorithm uses both geometric properties (normals) and curvature information for clustering
Smoothness threshold is converted from degrees to radians in the implementation
Parameters
min_cluster_size: Minimum number of points that a cluster needs to contain
max_cluster_size: Maximum number of points that a cluster can contain
smoothness_threshold: Maximum angle difference between normals (in degrees, converted to radians internally)
curvature_threshold: Maximum difference in curvature between neighboring points
nearest_neighbors: Number of nearest neighbors to consider for region growing
Additional Features
The implementation calculates and logs various statistics for each cluster:
Average RSD (Radius-based Surface Descriptor) values
References
R. B. Rusu, N. Blodow, Z. C. Marton, and M. Beetz, “Close-range Scene Segmentation and Reconstruction of 3D Point Cloud Maps for Mobile Manipulation in Human Environments,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), St. Louis, MO, USA, October 2009.
Segmentation of Clutter – object_segmentation.cpp
The input into this step is the vector of point cloud clusters that was generated during the previous step.
Input
Vector of point cloud clusters, where each cluster is a separate point cloud containing:
Points that are close to each other in 3D space.
Each point retains its original attributes (xyz, RGB, normal, curvature, RSD values)
Output
moveit_msgs::CollisionObject[] collision_objects
Outer Loop
The entire process below occurs for each point cloud cluster in the input vector.
Process
Project the Point Cloud Cluster Onto the Surface Plane
For each point (x,y,z,RGB,normal, curvature, RSDmin and RSDmax) in the 3D cluster:
Project the point onto the surface plane (assumed to be z=0). This creates a point (x, y).
Maintain a mapping between each 2D projected point and its original 3D point
Initialize Two Separate Parameter Spaces
Create a vector to store line models for the Hough transform
Each line model stores rho, theta, votes, and inlier count
Create a 3D Hough parameter space for circle models
Dimensions: center_x, center_y, radius
Understanding Hough Space
Hough space is a parameter space used for detecting specific shapes or models. Each point in Hough space represents a possible instance of the shape you’re looking for (in this case, circles or lines).
For lines:
Instead of y = mx + b, a rho-theta parameterization of the line is used
rho is the perpendicular distance from the line to the origin
This parameterization allows for vertical lines and avoids infinite slopes
For circles:
The Hough space is 3D: (center_x, center_y, radius)
Each point in this space represents a possible circle in the original image
The voting process in Hough space helps find shapes even if they’re partially hidden or broken in the original image.
Inner Loop (repeated num_iterations times)
RANSAC Model Fitting
Line Fitting
Use RANSAC to fit a 2D line to the projected points. This is done to identify potential box-like objects.
Circle Fitting
Use RANSAC to fit a 2D circle to the projected points. This is done to identify cylindrical-like objects.
Filter Inliers
For the fitted models from the RANSAC Model Fitting step, apply a series of filters to refine the corresponding set of inlier points.
Circle Filtering
Euclidean Clustering
Use pcl::EuclideanClusterExtraction to group inliers into clusters.
Accept models with a maximum of two clusters (representing complete circles or two visible arcs of the same cylinder).
Reject models with more than the specified maximum number of clusters.
Height Consistency (for two clusters only)
For models with exactly two clusters, check if the height difference between clusters is within the specified tolerance.
This ensures that the fitted circle represents a cross-section of a single, upright cylindrical object.
Curvature Filtering
Keep inlier points with high curvature (above the specified threshold).
Remove inlier points with low curvature.
Radius-based Surface Descriptor (RSD) Filtering
Compare the minimum surface radius (r_min) of each inlier point to the radius of the fitted circle.
Keep points where the difference is within the specified tolerance.
Surface Normal Filtering
Calculate the angle between the point’s normal (projected onto the xy-plane) and the vector from the circle center to the point.
Keep points where this angle is within the specified threshold.
Line Filtering
Euclidean Clustering
Use pcl::EuclideanClusterExtraction to group inliers into clusters.
Accept models with only one cluster.
Reject models with more than the specified maximum number of clusters.
Curvature Filtering
Keep inlier points with low curvature (below the specified threshold).
Remove inlier points with high curvature.
Model Validation
For both circle and line models, check how many inlier points remain after filtering.
If the number of remaining inliers for the model exceeds the threshold:
The model is considered valid.
Add the model to the appropriate Hough parameter space.
If the number of remaining inliers is below the threshold:
The model is rejected.
An additional validation step compares inlier counts between circle and line models, keeping only the model type with more inliers.
Add Model to the Hough Space
If a model is valid:
For circles:
Add a vote to the 3D Hough space (center_x, center_y, radius bins)
For lines:
Add the line model (rho, theta, votes, inlier count) to the line models vector
rho is the perpendicular distance from the origin (0, 0) to the line
theta is the angle formed between the x-axis and this perpendicular vector (positive theta is counter-clockwise measured from x-axis)
Remove inliers and continue
Remove the inliers of valid models from the working point cloud and continue the inner loop until insufficient points remain or no valid models are found.
Cluster Parameter Spaces
After all iterations on a point cloud cluster:
Cluster line models based on similarity in rho and theta.
Cluster circle models in the 3D Hough space.
Select Model with Most Votes
Compare the top line cluster with the top circle cluster. Select the model type (line or circle) with the highest vote count.
Estimate 3D Shape
Using the parameters from the highest-vote cluster, fit the selected solid geometric primitive model type (box or cylinder) to the original 3D point cloud data.
Cylinder Fitting
Use the 2D circle fit for radius and (x,y) center position.
Set cylinder bottom at z=0.
Set top height to the highest point in the cluster.
Calculate dimensions and position of the cylinder.
Box Fitting
Compute box orientation from the line angle.
Project points onto the line direction and perpendicular direction to determine length and width.
Use z-values of points to determine height.
Calculate dimensions and position of the box.
Add Shape as Collision Object
The box or cylinder is added as a collision object (moveit_msgs::CollisionObject) to the planning scene with a unique id (e.g. box_0, box_1, cylinder_0, cylinder_1, etc).
Move to Next Cluster
Proceed to the next point cloud cluster in the vector (i.e., move to the next iteration of the Outer Loop).
Key Parameters
num_iterations: Number of RANSAC iterations per cluster
inlier_threshold: Minimum number of inliers for a model to be considered valid
ransac_distance_threshold: Maximum distance for a point to be considered an inlier in RANSAC
ransac_max_iterations: Maximum number of iterations for RANSAC
circle_min_cluster_size, line_min_cluster_size: Minimum size for Euclidean clusters
circle_max_clusters, line_max_clusters: Maximum number of allowed clusters
circle_height_tolerance: Maximum allowed height difference between two circle clusters
circle_curvature_threshold, line_curvature_threshold: Curvature thresholds for filtering
circle_radius_tolerance: Tolerance for RSD filtering
circle_normal_angle_threshold: Maximum angle between normal and radial vector for circles
circle_cluster_tolerance, line_cluster_tolerance: Distance threshold for Euclidean clustering
line_rho_threshold, line_theta_threshold: Thresholds for clustering line models in the parameter space
Get Planning Scene Server (get_planning_scene_server.cpp)
This code brings together all the previously developed components into a single, unified ROS 2 service. It integrates functions for point cloud processing, plane segmentation, object clustering, and shape fitting into a cohesive workflow. The service processes point cloud and RGB image data to generate a MoveIt planning scene, which can be called by a node using the MoveIt Task Constructor to obtain environment information for manipulation tasks. The core functionality is encapsulated in the handleService method of the GetPlanningSceneServer class.
handleService Method Walkthrough
Initialize Response: The method starts by setting the success flag of the response to false.
Check Data Availability: It verifies that both point cloud and RGB image data are available. If either is missing, it logs an error and returns.
Validate Input Parameters and Prepare Point Cloud:
Checks if target_shape and target_dimensions are valid
Transforms the point cloud to the target frame
Applies optional cropping to the point cloud
Convert PointCloud2 to PCL Point Cloud: Converts the ROS PointCloud2 message to a PCL point cloud for further processing.
Segment Support Plane and Objects: Uses the segmentPlaneAndObjects function to separate the support surface from objects in the scene.
Create CollisionObject for Support Surface: Generates a CollisionObject representing the support surface and adds it to the planning scene.
Estimate Normals, Curvature, and RSD: Calls estimateNormalsCurvatureAndRSD to calculate geometric features for each point in the object cloud.
Extract Clusters: Uses extractClusters to identify distinct object clusters in the point cloud.
Get Collision Objects: Calls segmentObjects to convert point cloud clusters into collision objects for the planning scene.
Identify Target Object: Searches through the collision objects to find one matching the requested target shape and dimensions.
Assemble PlanningSceneWorld: Combines all collision objects into a complete PlanningSceneWorld structure.
Fill the Response:
Sets the full point cloud, RGB image, target object ID, and support surface ID in the response
Sets the success flag to true if all critical steps were successful
Logs detailed information about the response contents
This method transforms raw sensor data into a structured planning scene that can be used by the MoveIt Task Constructor for motion planning and manipulation tasks.
Congratulations on reaching the end! Keep building!
In this tutorial, we will explore how to use the ros2 doctor command to identify and diagnose issues in your ROS 2 system.
The purpose of ros2 doctor is to check the health and configuration of your ROS 2 setup. It analyzes various aspects such as environment variables, network configuration, and running systems to identify potential issues or warnings.
Real-World Applications
ros2 doctor is useful when:
Setting up a new ROS 2 environment to ensure proper configuration
Troubleshooting issues or errors in your ROS 2 system
Collaborating on ROS 2 projects to ensure consistent setups across team members
If ros2 doctor finds no warnings, you’ll see “All <n> checks passed”.
You don’t have to worry about UserWarnings. UserWarnings indicate non-ideal configurations, but the setup is still usable.
What you do need to pay attention to are errors. If any errors are found, you will see “UserWarning: ERROR:”. In this case, the check is considered failed, and you should address the errors.
Check a System
Let’s use ros2 doctor to analyze an actual system.
We will launch the robot and use ros2 doctor to analyze the running system.
