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ROSMASTER M3 PRO📖
0.Configuration and Operation Guide
  • 1. Quick start handle to control the car
  • 2. Log in to the car and view the code
  • 3. Robotic Arm Calibration
  • 4. Enter the Docker (Jetson-Nano and Raspberry Pi 5 users, see here)
  • 5. Assembly video
  • 6. Modify the firmware parameters of the car control board
  • 7. Frequently Asked Questions and Precautions
  • 8. Precautions for DIY Robotic Arm
1.Chassis Control Course
  • 1.ROS control
  • 2.Handle control
  • 3.Keyboard control
  • 4.Angular velocity calibration
  • 5.Line velocity calibration
  • 6.Line patrol automatic driving
  • 7.Patrol
2.Lidar Course
  • 1.Lidar introduction and use
  • 2.Dual Lidar fusion
  • 3.Lidar obstacle avoidance
  • 4.Lidar tracking
  • 5.Lidar guard
  • 6.Gmapping-SLAM mapping
  • 7.Cartographer-SLAM mapping
  • 8.slam_toolbox mapping
  • 9.Navigation2 single-point navigation avoid
  • 10.Navigation2 multi-point navigation avoid
  • 11.Repositioning navigation
  • 12.RTAB-Map mapping
  • 13.RTAB-Map navigation
  • 14.APP mapping navigation
  • 15.Machine code handling
  • 16.Color block transport
  • 17.Navigation via Pose-Constrained Visual Relocalizationnew
3.Depth Camera Course
  • 1.Dabai_DCW2 camera introduction
  • 2.Depth pseudo-color image
  • 3.Depth camera distance measurement
  • 4.Wood block volume measurement
  • 5.Mediapipe gesture recognition
  • 6.Edge detection
  • 7.YOLOv8 object detection
  • 8.Deep learning object tracking
  • 9. Transformer Model Tracking Algorithmnew
4.Robotic Arm and 3D Space Gripping Course
  • 1.Robotic arm solution
  • 2.Robotic arm chassis linkage control
  • 3.Machine code ID sorting
  • 4.Sorting height abnormality machine code
  • 5.Tracking and gripping machine code
  • 6.Color block color sorting
  • 7.Sorting height abnormality color block
  • 8.Tracking and gripping color block
  • 9.Wood block shape sorting
  • 10.KCF tracking and gripping objects
  • 11.Medipipe gesture ID sorting machine code
  • 12.Mediapipe gesture height sorting machine code
  • 13.Desktop tracking and gripping machine code
  • 14.3D tracking machine code
  • 15.Line patrol and obstacle removal
5.Road network planning and navigationnew
  • 1. Introduction To Road Network Planning
  • 2. Build A Pose Map
  • 3. Road Network Annotation
  • 4. Planning And Navigation Between Waypoints
  • 5. Road Network Navigation In Any Pose
  • 6. Path Re-planning
6.OpenClaw Basic Coursenew
  • 1. Introduction to OpenClaw
  • 2. OpenClaw Core Concepts
  • 3. OpenClaw API-KEY Configuration
  • 4. OpenClaw Switch Models
  • 5. OpenClaw System Prompt Words
  • 6. OpenClaw Common CLI Commands
7.OpenClaw Interaction Methodnew
  • 1. OpenClaw Webchat Interaction
  • 2. OpenClaw Tui Interaction
  • 3. OpenClaw Voice Dialogue Interaction
  • 4. OpenClaw WhatsApp Interaction
8.OpenClaw Developmentnew
  • 1. OpenClaw connects to the MCP interface of the robot
  • 2. Robot CLI Command Tool
  • 3. Robot control plugin tool
  • 4. Reply bridge plugin tool
  • 5. OpenClaw multimodal vision
  • 6. OpenClaw chassis control
  • 7. OpenClaw robotic arm control
  • 8. OpenClaw skills development
  • 9. OpenClaw timed tasks
  • 10. OpenClaw multi-agent intelligent agent
  • 11. OpenClaw active interaction
  • 12. OpenClaw vector memory
9.OpenClaw Embodied intelligent applicationnew
  • 1. OpenClaw target tracking
  • 2. OpenClaw robotic arm tracking and grasping
  • 3. OpenClaw robotic arm fixed-point placement
  • 4. OpenClaw garbage sorting
  • 5. OpenClaw slam mapping and navigation
  • 6. OpenClaw navigation transfer
  • 7. OpenClaw machine code clip
  • 8. OpenClaw color line patrol
  • 9. OpenClaw visual distance measurement
10.OpenClaw scenario sandboxnew
  • 1. Map description and prop placement
  • 2. Build sand table map
  • 3. Visual relocation markers
  • 4. Map mapping markers
  • 5. OpenClaw scenario sandbox comprehensive case
11.AI Model Basics
  • 1.AI large model types and principles
  • 2.RAG retrieval enhancement and model training samples
  • 3.Embodied intelligent robot system architecture
  • 4.Embodied intelligent functions core source code
12.AI Model Development
  • 01.Register a model service provider account
  • 02. Configuring API-KEY
  • 03. Introduction to Dify
  • 04. Basic Dify Features
  • 05. AI Large Model Development - chatbot
  • 06. Deploy the RAG knowledge base
  • 07. RAG knowledge base + chatbot
  • 08. AI agent workflow
  • 09. Custom wake-up response
  • 10. Core module testing tools
13.AI Large Model (Text Version)
  • 1.Semantic understand instruction following
  • 2.Multimodal visual understand
  • 3.Multimodal visual understand+robotic arm grab
  • 4.Multimodal visual understand+SLAM navigation
  • 5.Robotic arm grab+Multimodal visual understand+SLAM navigation
  • 6.Intention estimation
14.AI Large Model(Voice Version)
  • 1.Semantic understand instruction following
  • 2.Multimodal visual understand
  • 3.Multimodal visual understand+robotic arm grab
  • 4.Multimodal visual understand+SLAM navigation
  • 5.Robotic arm grab+Multimodal visual understand+SLAM navigation
  • 6.Intention estimation
15.Mediapipe Visual Course
  • 1.Hand detection
  • 2.Posture detection
  • 3.Overall detection
  • 4.Facial Landmark Detection
  • 5.Face detection
  • 6.Face special effects
  • 7.3D object recognition
  • 8.Brush
  • 9.Finger control
  • 10.Palm target positioning
  • 11.Fingertip trajectory recognition
  • 12.Fingertip gesture control robotic arm
  • 13.Gesture grabbing and releasing objects
  • 14.Finger control robotic arm
  • 15.Medipipe gesture control robotic arm action group
16.MoveIt2 Simulation Course
  • 1.MovelT2 configuration
  • 2.MovelT2 simulation-reality linkage
  • 3.Random movement
  • 4.Forward kinematics design
  • 5.Inverse kinematics design
  • 6.Cartesian path
  • 7.Trajectory planning
  • 8.Collision detection
  • 9.Scene design
17.Multi-vehicle Course
  • 1.Multi-vehicle chassis control
  • 2.Multi-vehicle robotic arm control
  • 3.Multi-vehicle navigation
  • 4.Multi-vehicle formation
18.Control Board Course
  • 1.Development environment setup
  • 2.Control board STM32 basic routine
  • 3.Control board micro_ros basic routine
19.Docker Course
  • 1、Docker overview and docker installation
  • 2、Common commands for docker image containers
  • 3、Docker images deeply understand and publish images
  • 4、Docker hardware interaction and data processing
  • 5、Enter the bot's docker container
  • 6、Robot development environment construction in Docker
20.Image Processing Basics Course
  • 1. Introduction to Open Source CV
  • 2. Image reading and display
  • 3. Image writing
  • 4. Image quality
  • 5. Pixel Operation
  • 6. Image zoom
  • 7. Image cutting
  • 8. Image translation
  • 9. Image Mirroring
  • 10. Affine Transformation
  • 11. Image Rotation
  • 12. Perspective Transformation
  • 13. Grayscale processing
  • 14. Image Binarization
  • 15. Edge Detection
  • 16. Line drawing
  • 17. Rectangle and circle drawing
  • 18. Drawing text and pictures
21.Main Control Course
Jetson Nano B01
  • 1. Introduction to the Jetson Nano B01 SUB Board
  • 2. Burn the system image
  • 3. Re-read the memory card or USB drive that has been burned
  • 4. Startup system
  • 5. Jetson Nano B01 System and Desktop Introduction
  • 6. Memory card or USB flash drive expansion
  • 7. Network Configuration
  • 8. SSH remote login & file transfer
  • 9. VNC Remote Login
  • 10. Jetson Nano B01 system backup
  • 11. Jetson Nano Swap space increased
  • 12. Installation and use of Jtop
Jetson Orin Nano_NX
  • 1.Jetson Orin board introduction
  • 2.Restore the factory image system
  • 3.Backup SSD system
  • 4.SSD expansion
  • 5.Network configuration
  • 6.SSH remote login
  • 7.VNC remote control
  • 8.Remote file transfer
  • 9.Jtop tool
  • 10.Exchange space expansion
Raspberry Pi
  • 1. Introduction to Raspberry PI 5
  • 2. Raspberry PI system installation and backup
  • 3. Powering the Raspberry PI 5
  • 4. Startup of Raspberry PI 5
  • 5. Update and upgrade operating system
  • 6. Introduction to raspi-config tool
  • 7. config.txt file description
  • 8. Network Configuration
  • 9. remote access
  • 10. Transfer files remotely
  • 11. Set display resolution and rotation
  • 12. Set screen to sleep
  • 13. Play audio and video
  • 14. Using USB camera
  • 15. Using MIPI camera
  • 16. Get real-time temperature of Raspberry Pi
22.Linux System Course
  • 1.Introduction to Linux system
  • 2.Ubuntu file system
  • 3.Ubuntu common commands
  • 4.Ubuntu common editors
  • 5.Ubuntu software operation commands
  • 6.Virtual machine installation
  • 7.SSH remote control
  • 8.VNC remote control
  • 9.Transfer files remotely
  • 10.Driver library and communication
  • 11.Static IP and hotspot mode
  • 12.Bind device ID
  • 13.Capacity expansion and resource allocation
  • 14.Update system software sources
  • 15.Set root user password
  • 16.sudo free password
  • 17.Connect to WiFi network
  • 18.View system version
  • 19.Customized service management
  • 20.Back up system image
23.ROS2 Basic Course
  • 1.Introduction to ROS2
  • 2.ROS2 install Humble
  • 3.ROS2 development environment
  • 4.ROS2 workspace
  • 5.ROS2 function package
  • 6.ROS2 node
  • 7.ROS2 topic communication
  • 8.ROS2 service communication
  • 9.ROS2 action communication
  • 10.ROS2 custom interface message
  • 11.ROS2 parameter service case
  • 12.ROS2 meta-function package
  • 13.ROS2 distributed communication
  • 14.ROS2 DDS
  • 15.ROS2 time related API
  • 16.ROS2 common command tools
  • 17.ROS2 rviz2 use
  • 18.ROS2 rqt toolbox
  • 19.ROS2 Launch startup file configuration
  • 20.ROS2 recording and playback tool
  • 21.ROS2 URDF model
  • 22.ROS2 Gazebo simulation platform
  • 23.ROS2 TF2 coordinate transformation
Download
  • Mapping_APP(iOS_Android)
  • Robot_Image_System_File
  • Code-Firmware
  • Instruction_Manual
  • VM_File
  • Software_Tools
  • Firmware_DriverFile
  • 3D_Model_File
  • ROS2_Basic_VM
  • Other_Annex
Download ZIP

Welcome to ROSMASTER M3 PRO repository

8.slam_toolbox mapping

slam_toolbox mapping

1. Course Content

Learn the SLAM mapping algorithm for robotics using the slam-toolbox.

After running the sample program, control the robot using the keyboard or controller to complete mapping and save the map.

2. Introduction to slam-toolbox

slam-toolbox is an open-source 2D SLAM (Simultaneous Localization and Mapping) algorithm package based on ROS, primarily used for mapping and localizing mobile robots in unknown environments. Based on the Graph Optimization framework, it combines sensor data from laser radar (such as 2D LiDAR) and inertial measurement units (IMUs) to construct a 2D grid map of the environment and update the robot's pose in real time.

2.1 Core Technology Principles

2.1.1 Front-end Processing (Laser Scan Matching)

  • GICP Algorithm: It estimates the robot's relative pose by iteratively calculating the optimal match between the current laser scan and the map. Compared to traditional ICP, GICP introduces a probabilistic model, making it more robust to outliers.
  • Motion Compensation: Uses IMU data to compensate for laser scanning distortion during robot motion, improving matching accuracy in dynamic environments.

2.1.2 Backend Optimization (Graph Optimization)

  • Pose Graph Construction: Constructs a graph structure by combining the robot's pose nodes and the relative pose constraints obtained from scan matching.
  • Global Optimization: Uses a nonlinear optimization algorithm (such as the Ceres Solver) to optimize the pose graph, eliminating accumulated errors and achieving global consistency of the map.

img

GitHub Project Address: https://github.com/SteveMacenski/slam_toolbox

3. Preparation

3.1 Content Description

This lesson uses the Jetson Orin NX as an example. For Raspberry Pi and Jetson Nano boards, you need to open a terminal and enter the command to enter the Docker container. Once inside the Docker container, enter the commands mentioned in this lesson in the terminal. For instructions on entering the Docker container, refer to the product tutorial [Configuration and Operation Guide]--[Enter the Docker (Jetson Nano and Raspberry Pi 5 users, see here)]. For Orin and NX boards, simply open a terminal and enter the commands mentioned in this lesson.

3.2 Start the agent

Note: To test all cases, you must first start the docker agent. If it has already been started, you do not need to restart it

Enter the command in the vehicle terminal:

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sh start_agent.sh

The terminal prints the following message, indicating that the connection is successful

10

4. Run the case

4.1 Map creation process

Note:

  • When building a map, the slower the speed, the better the effect (mainly the slower the rotation speed). If the speed is too fast, the effect will be very poor.
  • For the Jetson Nano and Raspberry Pi series controllers, you must first enter the Docker container (for steps, see the [Docker course chapter - Entering the Robot's Docker Container]).

Command to start mapping on the vehicle terminal:

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ros2 launch slam_mapping slam_toolbox.launch.py

Using the virtual machine configuration as an example, open a terminal and launch the RVIZ visualization interface:

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ros2 launch slam_view slam_view.launch.py

Command to start the RVIZ visualization interface on the vehicle terminal:

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ros2 launch slam_mapping slam_view.launch.py

image-20250613150910238

Open another terminal in the virtual machine and start the keyboard control node (you can also use a controller remote control. To start the controller control node in advance, refer to [5. Chassis Control - 2. Controller Control]):

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ros2 run yahboomcar_ctrl yahboom_keyboard

Click the terminal window with your mouse and press z to reduce the speed.

image-20250613151050343

Click the terminal window with your mouse and press z to reduce the speed. Press I, <, J, and L to control the car's forward, backward, left, and right turns, respectively. Control the car's movement to complete the map building.

image-20250613151616040

4.2 Saving the Map

Open a new terminal on the vehicle and save the map.

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ros2 launch slam_mapping save_map.launch.py

The terminal prompts **Map saved "successful" indicates the map was saved successfully.

image-20250613152219166

The map save path is as follows:

Jetson Orin Nano, Jetson Orin NX:

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/home/jetson/M3Pro_ws/install/M3Pro_navigation/share/M3Pro_navigation/map

Jetson Orin Nano, Raspberry Pi:

You need to enter Docker first.

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/root/M3Pro_ws/install/M3Pro_navigation/share/M3Pro_navigation/map/

One PGM image, one YAML file yahboom_map.yaml

image: yahboom_map.pgm
mode: trinary
resolution: 0.05
origin: [-10, -10, 0]
negate: 0
occupied_thresh: 0.65
free_thresh: 0.25

Parameter Explanation:

  • image: The path to the map file, either absolute or relative.
  • mode: This attribute can be one of trinary, scale, or raw, depending on the selected mode. Trinary is the default.
  • resolution: The map resolution, in meters/pixels.
  • origin: The 2D pose (x, y, yaw) of the lower-left corner of the map. yaw is rotated counterclockwise (yaw=0 means no rotation). Currently, many parts of the system ignore the yaw value.
  • negate: Whether to invert the meaning of white/black and free/occupied (this does not affect the interpretation of the thresholds).
  • occupied_thresh: Pixels with an occupied probability greater than this threshold are considered fully occupied.
  • free_thresh: Pixels with an occupied probability less than this threshold are considered completely free.

5. Node Analysis

5.1 Displaying the Node Computation Graph

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ros2 run rqt_graph rqt_graph

image-20250613152636509

5.2 TF Transformation

Run in the VM terminal:

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ros2 run rqt_tf_tree rqt_tf_tree

Image size is too large; the original image can be viewed in this lesson folder.

frames

5.3 slam-toolbox Node Details

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ros2 node info /slam_toolbox

Enter the above command in the terminal to view the subscription and publication topics related to the gmapping node.

x
/slam_toolbox
  Subscribers:
    /map: nav_msgs/msg/OccupancyGrid
    /parameter_events: rcl_interfaces/msg/ParameterEvent
    /scan: sensor_msgs/msg/LaserScan
    /slam_toolbox/feedback: visualization_msgs/msg/InteractiveMarkerFeedback
  Publishers:
    /map: nav_msgs/msg/OccupancyGrid
    /map_metadata: nav_msgs/msg/MapMetaData
    /parameter_events: rcl_interfaces/msg/ParameterEvent
    /pose: geometry_msgs/msg/PoseWithCovarianceStamped
    /rosout: rcl_interfaces/msg/Log
    /slam_toolbox/graph_visualization: visualization_msgs/msg/MarkerArray
    /slam_toolbox/scan_visualization: sensor_msgs/msg/LaserScan
    /slam_toolbox/update: visualization_msgs/msg/InteractiveMarkerUpdate
    /tf: tf2_msgs/msg/TFMessage
  Service Servers:
    /slam_toolbox/clear_changes: slam_toolbox/srv/Clear
    /slam_toolbox/describe_parameters: rcl_interfaces/srv/DescribeParameters
    /slam_toolbox/deserialize_map: slam_toolbox/srv/DeserializePoseGraph
    /slam_toolbox/dynamic_map: nav_msgs/srv/GetMap
    /slam_toolbox/get_interactive_markers: visualization_msgs/srv/GetInteractiveMarkers
    /slam_toolbox/get_parameter_types: rcl_interfaces/srv/GetParameterTypes
    /slam_toolbox/get_parameters: rcl_interfaces/srv/GetParameters
    /slam_toolbox/list_parameters: rcl_interfaces/srv/ListParameters
    /slam_toolbox/manual_loop_closure: slam_toolbox/srv/LoopClosure
    /slam_toolbox/pause_new_measurements: slam_toolbox/srv/Pause
    /slam_toolbox/save_map: slam_toolbox/srv/SaveMap
    /slam_toolbox/serialize_map: slam_toolbox/srv/SerializePoseGraph
    /slam_toolbox/set_parameters: rcl_interfaces/srv/SetParameters
    /slam_toolbox/set_parameters_atomically: rcl_interfaces/srv/SetParametersAtomically
    /slam_toolbox/toggle_interactive_mode: slam_toolbox/srv/ToggleInteractive
  Service Clients:
​
  Action Servers:
​
  Action Clients:
​
Cartographer-SLAM mapping
Navigation2 single-point navigation avoid

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