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MicroROS V2 Robot Car📖
01.Introduction
  • 0.Unboxing Video
  • 1. Readme Learning Route
  • 2. Must Read Before Use
  • 3. Introduction to MicroROS V2 Control Board
  • 4. Introduction to LiDAR
  • 5. Introduction to ESP32 Camera Module
  • 6. FAQ
  • 7. About Charging
02.Assembly course
  • Assembly Video
03.Preparation
  • 1. Quickly Start
  • 2. Write Firmware
  • 3. Install VM and Start System
  • 4. Configure VM and Connect Robot
  • 5. Connect to MicroROS Agent
04.Quick start control course
  • 1. VM Keyboard Remote Control
  • 2. VM Handle Remote Control
  • 3. USB Handle Control
05.Robot control course
  • 1. Robot Information Release
  • 2. Robot Keyboard Control
  • 3. Robot Handle Control
  • 4. Robot State Estimation
  • 5. Linear Speed Calibration
  • 6. Angular Velocity Calibration
  • 7. Robot URDF Model
06.Lidar course
  • 1. Precautions for LiDAR Mapping and Navigation
  • 2. Lidar Avoid
  • 3. Lidar Following
  • 4. Lidar Guard
  • 5. Lidar Patrol
  • 6. Gmapping Mapping
  • 7. Cartographer Mapping
  • 8. Cartographer Quickly Repositioning Navigation
  • 9. Slam Toolx Mapping
  • 10. Navigation2 Single Point Navigation
  • 11. Navigation2 Multi Point Navigation
  • 12. Ros Robot App Mapping And Navigation
07.Road network planning
  • 1. Introduction to Road Network Planning and Quick Start
  • 2. Build a Pose Map
  • 3. Road Network Annotation
  • 4. Planning and Navigation Between Road Networks
  • 5. Road Network Navigation in Any Pose
  • 6. Path Replanning
08.Vision basic course
OpenCV Image Processing
  • 1. Introduction to OpenCV Source CV
  • 2. Open Source CV Geometric Transformation
  • 3. Open Source CV Image Processing and Drawing Text Line Segments
  • 4. Open Source CV Image Beautification
09.Visual General Course (For Standard&Superior version)
4. Yolov11&26 Visual Recognition
  • 1. YOLO Environment Setup
  • 2. CLI Usage
  • 3. Object Detection
  • 4. Instance Segmentation
  • 5. Pose Estimation
  • 6. Image Classification
  • 7. Oriented Bounding Boxes Object Detection
  • 8. Model Prediction
1. ESP32 Visual Recognition Module Configuration
  • 1. Serial Port Configuration WiFi Instructions
  • 2. Use ESP32 Camera Module in Robot Car
  • 3. Servo PTZ Calibration
2. OpenCV Visual Recognition
  • 1. Human Pose Estimation
  • 2. Object Detection
  • 3. AR Vision
  • 4. QR Code
  • 5. Color Recognition
  • 6. Facial Recognition
  • 7. AprilTag Machine Code Recognition
3. Mediapipe Visual Recognition
  • 1. Hand Detection
  • 2. Pose Detection
  • 3. Holistic Detection
  • 4. Facial Detection
  • 5. Facial Recognition
  • 6. Facial Special Effects
  • 7. 3D Object Recognition
  • 8. Brush
  • 9. Gesture Recognition
  • 10. Palm Target Localization
  • 11. Fingertip Trajectory Recognition
10.Visual Advanced Course (Just for Standard version)
1. Visual Control
  • 1. QR Code Recognition Control Action
  • 2. AprilTag Control Car Action Group
  • 3. Gesture Control Car Action Group
  • 4. Visual Line Tracking
2. Visual Tracking
  • 1. Color Tracking
  • 2. Facial Tracking
  • 3. QR Code Tracking
  • 4. Object Recognition And Tracking
  • 5. Mediapipe Palm Tracking
  • 6. Mediapipe Gesture Tracking
  • 7. Mediapipe Human Pose Tracking
  • 8. KCF Object Tracking
  • 9. AprilTag Machine Code Tracking
3. Visual Following
  • 1. Color Follow
  • 2. Face Follow
  • 3. QR Code Follow
  • 4. Mediapipe Palm Follow
  • 5. Mediapipe Gesture Follow
  • 6. Mediapipe Human Posture Follow
  • 7. AprilTag Machine Code Follow
  • 8. Object Follow
11.Visual Advanced Course (Just for Superior version)
1. Visual Control
  • 1. QR Code Recognition Control Action
  • 2. AprilTag Control Car Action Group
  • 3. Gesture Control Car Action Group
  • 4. Visual Line Tracking
2. Visual Tracking
  • 1. Color Tracking
  • 2. Facial Tracking
  • 3. QR Code Tracking
  • 4. YOLO Object Recognition and Tracking
  • 5. Mediapipe Palm Tracking
  • 6. Mediapipe Gesture Tracking
  • 7. Mediapipe Human Pose Tracking
  • 8. KCF Object Tracking
  • 9. AprilTag Machine Code Tracking
3. Visual Follow
  • 1. Color Follow
  • 2. Face Follow
  • 3. QR Code Follow
  • 4. Mediapipe Palm Follow
  • 5. Mediapipe Gesture Follow
  • 6. Mediapipe Human Pose Follow
  • 7. AprilTag Machine Code Follow
  • 8. Object Follow
12.AI Large Model Basic and Applications Course
1. AI Large Model Preparation
  • 1.AI Large Model Preparation
2. AI Large Model Basic Course
  • 1. AI Large Model Types and Principles
  • 2. RAG Retrieval Augmented and Training Samples
  • 3. Embodied Intelligent System Architecture
  • 4. Embodied Intelligence Core Source Code
3. AI Large Model Development
  • 1. Register Model Service Account
  • 2. Configure API KEY
  • 3. Dify Introduction
  • 4. Dify Basic Features
  • 5. RAG Knowledge Base Deployment
  • 6. AI Application Development Chatbot
  • 7. RAG Knowledge Base and Chatbot
  • 8. AI Agent Workflow
4. AI Large Model (Text Version)
  • 1. Semantic Understanding and Instruction Following
  • 2. Multimodal Visual Understanding
  • 3. Multimodal Visual Understanding + Basic Control
  • 4. Multimodal Visual Understanding + Object Following
  • 5. Multimodal Visual Understanding + Visual Line Following
  • 6. Multimodal Visual Understanding + SLAM Navigation
  • 7. Multimodal Visual Understanding + SLAM Navigation + Visual Line Following
  • 8. Multimodal Visual Understanding + SLAM Navigation + Object Following
  • 9. Intent Inference + Multimodal Visual Understanding + SLAM Navigation
13.Openclaw Basics Course
  • 1. Openclaw Introduction
  • 2. Openclaw Core Concepts
  • 3. Openclaw Api-Key Configuration
  • 4. Openclaw Switching Models
  • 5. Openclaw System Prompt
  • 6. Openclaw Common CLI Command
14. Openclaw Interaction Course
  • 1. Openclaw Webchat Interaction
  • 2. Openclaw Tui Interaction
  • 3. Openclaw Whatsapp Interaction
15.Openclaw Development Course
  • 1. Openclaw Robot Mcp Service Integration
  • 2. Robot CLI Command Tools
  • 3. Robot-Control Plugin Tools
  • 4. Openclaw Skills Development
  • 5. Openclaw Scheduled Tasks
  • 6. Openclaw Multi Agent System
  • 7. Openclaw Proactive Interaction
  • 8. Openclaw Vector Memory
16.Openclaw Embodied Intelligence Course
  • 1. OpenClaw Natural Interaction + Basic Control
  • 2. OpenClaw Natural Interaction + Slam Navigation
  • 3. OpenClaw Natural Interaction + Multimodal Visual Understand
  • 4. OpenClaw Natural Interaction + Multimodal Visual Understand + Slam Navigation + Visual Follow
  • 5. OpenClaw Natural Interaction + Multimodal Visual Understand + Slam Navigation + Visual Track
17.Multi-machine Course
  • 1. Multi Machine Handle Control
  • 2. Multi Machine Keyboard Control
  • 3. Multi Machine Navigation
18.MicroROS-V2 Control Board Development Environment
  • 1. Set Up the ESP32-IDF Development Environment
  • 2. ESP32-IDF Configuration Tool
  • 3. ESP Flash Tool Flashing Firmware
  • 4. Install ESP32 Microros Components
  • 5. Install And Start Microros Agent
19.MicroROS-V2 Control Board ESP32 Basic Course
  • 1. Led Light Up
  • 2. Button Function
  • 3. Drive Buzzer
  • 4. Oled
  • 5. Usb Host Function
  • 6. Serial Communication
  • 7. Battery Voltage Detection
  • 8. Drive Pwm Servo
  • 9. Drive Motors
  • 10. Read Motor Encoder Data
  • 11. Pid Motor Speed Control
  • 12. Robot Kinematics Analysis Theory
  • 13. Read Imu Data
  • 14. Read Lidar Data
  • 15. Flash Data Storage
  • 16. Custom Partition Table
  • 17. Bluetooth Communication
  • 18. Wifi Connection
  • 19. ESP32 Visual Recognition Module
20.MicroROS-V2 Control Board Micro_ros Basic Course
  • 1. Publish Topics
  • 2. Subscribe To Topics
  • 3. Multi Topic Subscription And Publishing
  • 4. Subscribe To Buzzer Topic
  • 5. Subscribe To Pwm Servo Topic
  • 6. Subscribe To Speed Control Topic
  • 7. Publish Odometry Topic
  • 8. Publish Imu Data Topic
  • 9. Publish Lidar Data Topic
21. 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
22.Docker Course
  • 1.Docker overview and docker installation
  • 2.Common commands for docker image containers
  • 3.Docker images deeply understand and publish images
23.ROS 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)
  • Code
  • Software_Tools
  • VM_System_File
  • 3D_Model_File
  • ESP32_Firmware
  • Dify LLM Base
  • Instruction_Manual
  • VMware
Download ZIP

Welcome to MicroROS V2 Robot Car repository

7. Robot URDF Model

Robot URDF Model

Robot URDF Model1. Course Content2. DemonstrationOperating Instructions3. Source Code Analysis3.1 model_display.py3.2 Model File

1. Course Content

  • Start the 3D state estimation layer and view the car's URDF model in real time in RViz2 using the Orbit 3D view. The model pose is driven in real time by the IMU data of the ESP32 control board — pick up the car and flip or rotate it freely, and the 3D model in RViz2 follows the motion, giving you an intuitive understanding of the coordinate systems of each sensor and the joint structure.

2. Demonstration

ros2 launch microros_v2_bringup display_model.launch.py

image-20260727150232935

 

Operating Instructions

After startup, RViz2 displays the car's 3D model. Since the IMU drives it in real time, pick up the car and tilt or flip it in your hand, and the model in RViz2 follows the motion immediately:

  • Rotate the car: The model flips synchronously in RViz2 (driven by the full IMU roll/pitch/yaw pose)

    image-20260727150422029

 

  • Adjust the gimbal servo angles through the GUI sliders: the cam1/cam2 joints update the model display effect in real time

image-20260727150454697

 

3. Source Code Analysis

3.1 model_display.py

  • Source code path
xxxxxxxxxx
$HOME/microros_ws/src/common_demo/common_demo/model_display/model_display.py

model_display.py converts the IMU real-time pose data into TF transforms, so that the URDF model in RViz2 flips and rotates together with the real car.

  1. Node initialization (__init__): Creates a TransformBroadcaster for publishing TF transforms; subscribes to the /imu topic with the qos_profile_sensor_data QoS (IMU usually uses best-effort QoS), with the callback function imu_callback.

  2. IMU callback (imu_callback): After receiving an IMU message, constructs a TransformStamped:

    • header.frame_id = 'odom', child_frame_id = 'base_footprint', establishing the odom → base_footprint transform chain
    • The translation is fixed at (0, 0, 0.03), only to keep the model floating at the origin for easy observation of the pose; no position estimation is performed
    • The rotation quaternion is taken directly from msg.orientation (i.e., the roll/pitch/yaw obtained from IMU fusion), achieving real-time full-pose driving
    • Calls sendTransform to publish; after receiving the TF, robot_state_publisher automatically computes the poses of all child links (base_link, gimbal, camera, etc.) based on the joint relationships in the URDF
  3. Cooperation with joint_state_publisher: The IMU only drives the overall pose of base_footprint; the cam1/cam2 gimbal joint angles are published separately by joint_state_publisher subscribing to /ptz_status. When the two are combined, the model in RViz2 can simultaneously present the complete effect of the body tilting + the gimbal rotating.

​x
class ModelDisplayNode(Node):
    def __init__(self):
        super().__init__('model_display')
        self.tf_broadcaster = TransformBroadcaster(self)
        # IMU data is usually published with best-effort QoS
        self.imu_sub = self.create_subscription(
            Imu, '/imu', self.imu_callback, qos_profile_sensor_data)
        self.get_logger().info('model_display started: /imu -> tf(odom->base_link)')
​
    def imu_callback(self, msg: Imu):
        t = TransformStamped()
        t.header.stamp = self.get_clock().now().to_msg()
        t.header.frame_id = 'odom'
        t.child_frame_id = 'base_footprint'
        # Translation stays at origin, only attitude is visualized
        t.transform.translation.x = 0.0
        t.transform.translation.y = 0.0
        t.transform.translation.z = 0.03
        t.transform.rotation = msg.orientation
        self.tf_broadcaster.sendTransform(t)

 

3.2 Model File

  • Model file path
xxxxxxxxxx
$HOME/microros_ws/src/robot_description/urdf/microros_v2.urdf.xacro

 

xxxxxxxxxx
<robot xmlns:xacro="http://www.ros.org/wiki/xacro"
  name="microros_v2">
​
  <xacro:include filename="$(find robot_description)/urdf/camera.urdf.xacro"/>
​
  <!-- camera_type: ptz | no_ptz | basic_car -->
  <xacro:arg name="camera_type" default="ptz"/>
​
  <!-- base_link mesh: no_ptz uses base_link_noptz.STL, ptz/basic_car use base_link.STL -->
  <xacro:property name="base_link_mesh"
    value="${'base_link_noptz.STL' if '$(arg camera_type)' == 'no_ptz' else 'base_link.STL'}"/>
​
  <!-- base_footprint -->
  <link
    name="base_footprint">
  </link>
  <joint
    name="basetofootprint"
    type="fixed">
    <origin
      xyz="0.0 0.0 0.02"
      rpy="0 0 0" />
    <parent
      link="base_footprint" />
    <child
      link="base_link" />
  </joint>
​
  <!-- car base -->
  <link
    name="base_link">
    <inertial>
      <origin
        xyz="0.0125358449468065 6.2338964078765E-07 0.0329029674944082"
        rpy="0 0 0" />
      <mass
        value="0.549545262878645" />
      <inertia
        ixx="0.000494117990279825"
        ixy="7.47435050338857E-08"
        ixz="-5.82265935748093E-08"
        iyy="0.00056957019186456"
        iyz="-1.73647671262012E-07"
        izz="0.00100869032893883" />
    </inertial>
    <visual>
      <origin
        xyz="0 0 0"
        rpy="0 0 0" />
      <geometry>
        <mesh
          filename="package://robot_description/meshes/${base_link_mesh}" />
      </geometry>
      <material
        name="">
        <color
          rgba="0.752941176470588 0.752941176470588 0.752941176470588 1" />
      </material>
    </visual>
    <collision>
      <origin
        xyz="0 0 0"
        rpy="0 0 0" />
      <geometry>
        <mesh
          filename="package://robot_description/meshes/${base_link_mesh}" />
      </geometry>
    </collision>
  </link>
.....

 

  • URDF, whose full name is Unified Robot Description Format, is a robot model file described in XML format

Tag Introduction

  • link, a link, which can be thought of as a human arm

  • joint, a joint, which can be thought of as a human elbow joint

  • The relationship between link and joint: two links are connected through a joint. Imagine an arm where the forearm (link) and the upper arm (link) are connected through the elbow joint (joint).

  • In the URDF description language, link is used to describe physical characteristics:

  • Describes the visual display, the <visual> tag.

  • Describes the collision properties, the <collision> tag.

  • Describes the physical inertia, the <inertial> tag (not commonly used).

  • origin

    Describes the pose information; the xyz attribute describes the coordinate position in the global environment, and the rpy attribute describes its own orientation.

  • mass

    Describes the mass of the link.

  • inertia

    Inertial reference frame. Due to the symmetry of the rotational inertia matrix, only the 6 upper-triangular elements ixx, ixy, ixz, iyy, iyz, izz are needed as attributes.

  • geometry

    The tag describes the shape; the mesh attribute is mainly used to load texture files, and the filename attribute is the file path of the texture.

  • material

    The tag describes the material; the name attribute is required, can be empty, and can be repeated. The rgba attribute in the <color> tag represents red, green, blue, and transparency, separated by spaces. The color range is [0-1].

 

joint describes the relationship between two links, and the motion position and velocity limits, as well as the kinematic and dynamic properties.

Joint types:

  • fixed: Fixed joint. Motion is not allowed; it serves to connect.
  • continuous: Continuous joint. Can rotate continuously, without rotation angle limits.
  • revolute: Revolute joint. Similar to continuous, but with rotation angle limits.
  • prismatic: Prismatic joint. Moves along an axis, with position limits.
  • floating: Floating joint. Has six degrees of freedom, 3T3R.
  • planar: Planar joint. Allows translation or rotation orthogonal to a plane.
  • In the <joint> tag, the name attribute is required, describing the name of the joint, and it must be unique.
  • In the <joint> tag, the type attribute corresponds to the six joint types.
Angular Velocity Calibration
Precautions for LiDAR Mapping and Navigation

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