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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

9. AprilTag Machine Code Tracking

AprilTag Machine Code Tracking (No-PTZ)

1. Experiment Objective

The camera tracks an AprilTag marker, and the car rotates to keep the Tag at the center of the frame. It supports Tracking mode (rotation only) and Follow mode (rotation + distance keeping).

2. Experiment Principle

1. Keyword Explanation

KeywordDescription
AprilTagA visual fiducial marker system based on QR-like codes that supports 6-DoF pose estimation and is commonly used in robot localization and navigation
tag36h11One of the AprilTag marker families, using 36-bit encoding with 11-bit error detection to provide highly robust 2D marker recognition
3D Pose EstimationComputes the 6-DoF pose of the Tag relative to the camera from the coordinates of the Tag's 4 corner points in the image using the PnP algorithm
quad_decimateThe image downsampling factor; increasing it improves detection speed but reduces long-distance detection capability
PD ControlProportional-derivative controller that computes the body angular velocity based on the Tag center deviation and its rate of change
QoSbest_effort (image input + cmd_vel downlink), depth=1

2. Technical Architecture

/camera/image_raw (BEST_EFFORT)
  → april_tag_tracker_node
    ├── Image processing (15Hz): image conversion → flip → apriltag.Detector.detect() → Tag corners + pose
    └── Body control (10Hz): pixel error → PD angular velocity + Tag depth Z-value distance following → /cmd_vel (BEST_EFFORT)

Follow mode feature: distance following is based not on pixel size but on the Z value of the Tag 3D pose (actual depth, in meters), providing higher precision.

3. Core Topic Quick Reference

Firmware Downlink

TopicMessage TypeQoSFieldRangeDirection
/cmd_velgeometry_msgs/Twistbest_effort + keep_last(1)linear.x-0.30~0.30 m/s+= forward, −= backward
   angular.z-0.60~0.60 rad/s+= turn left, −= turn right

3. Experiment Steps

1. Hardware Preparation

HardwareRequirement
PTZ version❌
No-PTZ version✅
Required peripheralESP32 camera (bracket-mounted), and a printed AprilTag marker (tag36h11 family)

2. Start the Agent + Camera + Joint Model (Terminal 1)

xxxxxxxxxx
ros2 launch microros_v2_bringup car_base.launch.py

image-20260728161615714

3. Start AprilTag Tracking (Terminal 2)

Tracking mode (rotation tracking only):

xxxxxxxxxx
ros2 launch microros_v2_no_ptz_visual_control_pkg april_tag_tracker.launch.py

image-20260728164308032

5. Operation Instructions

KeyFunction
SpaceToggle tracking/standby state
q / Q / ESCExit the program

 

4. Experiment Phenomenon

  • When the AprilTag moves left or right in the frame, the car automatically rotates to track it, keeping the Tag at the center of the frame
  • The terminal outputs the Tag's 3D pose information in real time (translation x/y/z and rotation angles)
  • The Tag border and center point are visualized
  • Follow mode: keeps a constant distance based on the Tag depth Z value
  • After the Tag moves out of the frame, the car stops; when it reappears, tracking resumes automatically

5. Code Explanation

Source path:

  • Launch (track): ~/microros_ws/src/microros_v2_no_ptz_visual_control_pkg/launch/april_tag_tracker.launch.py
  • Launch (follow): ~/microros_ws/src/microros_v2_no_ptz_visual_control_pkg/launch/april_tag_follow.launch.py
  • Node source: ~/microros_ws/src/microros_v2_no_ptz_visual_control_pkg/microros_v2_no_ptz_visual_control_pkg/april_tag_tracker_node.py

1. Core Node Analysis

april_tag_tracker_node uses the apriltag Python library to detect AprilTag markers. Core workflow:

  • Image processing (15Hz): raw Image frame conversion → grayscale → detector.detect(gray) → extract Tag corners + pose
  • 3D pose estimation: uses the PnP algorithm with the known physical Tag size (tag_size) and camera intrinsics to compute the 6-DoF pose
  • Body control (10Hz): deviation between the Tag center x-coordinate and the image center → PD angular velocity; Tag depth Z-value deviation → PD linear velocity
  • quad_decimate: lowers the detection resolution to speed up processing (the larger the value, the faster, but small Tags at long distance may be missed)

Constructor — create the AprilTag detector:

​x
# Source file: ~/microros_ws/src/microros_v2_no_ptz_visual_control_pkg/microros_v2_no_ptz_visual_control_pkg/april_tag_tracker_node.py
​
def __init__(self) -> None:
    super().__init__(DEFAULT_NODE_NAME)
    self._tgt_lock = threading.Lock()
    self._state = STATE_IDLE
    self._has_target = False
    self._lost_frames = 0
    self._target_x: Optional[float] = None
    self._target_z: Optional[float] = None  # Tag depth Z value (used for distance following)
​
    self._declare_params()
    self._load_params()
​
    # Create the AprilTag detector
    self._detector = apriltag.Detector(
        apriltag.DetectorOptions(
            families=self._tag_family,
            quad_decimate=float(self._quad_decimate),
        )
    )
​
    best_effort = QoSProfile(
        history=QoSHistoryPolicy.KEEP_LAST, depth=1,
        reliability=QoSReliabilityPolicy.BEST_EFFORT,
        durability=QoSDurabilityPolicy.VOLATILE,
    )
    self._img_sub = self.create_subscription(
        Image, self._img_topic, self._on_image, best_effort)
    self._cmd_pub = self.create_publisher(Twist, self._cmd_topic, best_effort)
​
    self._img_timer = self.create_timer(
        1.0 / max(float(self._img_rate), MINIMUM_TIMER_RATE_HZ), self._process_image)
    self._body_timer = self.create_timer(
        1.0 / max(float(self._body_rate), MINIMUM_TIMER_RATE_HZ), self._body_tick)

Body control callback — 3D Z-value distance following:

xxxxxxxxxx
# Source file: ~/microros_ws/src/microros_v2_no_ptz_visual_control_pkg/microros_v2_no_ptz_visual_control_pkg/april_tag_tracker_node.py
​
def _body_tick(self) -> None:
    """Body control timer (10Hz)."""
    if self._state != STATE_TRACKING or self._shutdown:
        self._pub_zero()
        return
​
    with self._tgt_lock:
        has = self._has_target
        tx = self._target_x
        tz = self._target_z
​
    if not has or tx is None:
        self._pub_zero()
        return
​
    # Horizontal pixel error → PD angular velocity
    cx = self._img_w * 0.5
    err_x = tx - cx
    if abs(err_x) <= self._deadband_x:
        az = 0.0
    else:
        az = self._angular_kp * err_x
        az = clamp(az, -self._max_az, self._max_az)
        if self._reverse_body:
            az = -az
​
    cmd = Twist()
    cmd.angular.z = az
​
    # Distance following: based on the 3D depth Z value (not pixel size)
    if self._dist_follow and tz is not None and tz > 0:
        err_z = self._target_z_m - tz  # _target_z_m unit: meters
        if abs(err_z) > self._dist_tol_z:
            lx = self._dist_kp * err_z
            lx = clamp(lx, -self._max_lx, self._max_lx)
            if self._reverse_linear:
                lx = -lx
            cmd.linear.x = lx
​
    self._cmd_pub.publish(cmd)

Key logic: Unlike the pixel-size-based distance following in color/face tracking, AprilTag distance following directly uses the 3D pose Z value (actual depth), so the actual size of the target does not need to be calibrated. The tag_size parameter must match the actual physical side length of the printed Tag (default 0.048 m); otherwise the pose estimation will be inaccurate.

2. Key Parameters

Parameter definition file:

  • Launch: ~/microros_ws/src/microros_v2_no_ptz_visual_control_pkg/launch/april_tag_tracker.launch.py
  • Node: ~/microros_ws/src/microros_v2_no_ptz_visual_control_pkg/microros_v2_no_ptz_visual_control_pkg/april_tag_tracker_node.py
ParameterTypeDefaultDescription
tag_familystrtag36h11AprilTag marker family
tag_sizefloat0.048 mPhysical side length of the Tag (meters), affects the 3D pose computation accuracy
quad_decimatefloat2.0Image downsampling factor
tag_select_strategystrlargestMulti-Tag selection strategy
deadband_x_pixelfloat20.0 pxHorizontal deadband
angular_kpfloat1.8Body angle PD proportional coefficient
max_angular_zfloat0.6 rad/sMaximum body steering speed
enable_distance_followboolfalseWhether to enable distance following
target_tag_pose_zfloat0.25 mTarget Tag depth for distance following (3D Z value)
distance_tolerance_pose_zfloat0.02 mDistance-following deadband
distance_kpfloat1.5Distance PD proportional coefficient
max_linear_xfloat0.30 m/sMaximum forward/backward speed

3. Node Description

NodePackageDescription
april_tag_tracker_nodemicroros_v2_no_ptz_visual_control_pkgAprilTag 6-DoF tracking with 3D pose + PD body control

6. Common Problems

ProblemCauseSolution
Tag is not detectedTag too small, reflective, or quad_decimate too largeEnlarge the printed Tag size and lower quad_decimate
Slow steering trackingPD parameters too conservativeIncrease angular_kp and reduce deadband_x_pixel
Pose jitterThe physical Tag size tag_size is inaccuratePrecisely measure and set the actual side length of the Tag (meters)
Slow Follow mode responseDistance PD parameters are unreasonableTune distance_kp / distance_kd
KCF Object Tracking
Color Follow

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