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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. Publish Odometry Topic

Publishing the Odometry Topic

1. Experiment Objective

In this experiment, you will learn how to publish the ROS2 standard nav_msgs/msg/Odometry odometry message through micro-ROS, while also fusing a cmd_vel speed subscription to implement a complete chassis control and state feedback node. Through this example, you will master motor encoder data acquisition, Dead Reckoning, ROS time synchronization, and the combined use of publishers and subscribers. This example publishes the topic /odom_raw at 10 Hz and simultaneously subscribes to /cmd_vel to control the motion of the car.

2. Experiment Principle

1. Keyword Explanation

ConceptDescription
OdometryThe ROS2 standard odometry message type, defined in nav_msgs/msg/Odometry, containing position, orientation, linear and angular velocities, as well as their respective covariance matrices. It is the fundamental data source for robot localization and navigation
Dead ReckoningA method of estimating the robot's position and orientation by integrating sensor data such as encoders. Starting from a known initial position, the current position is continuously estimated by integrating velocity and angular velocity over time, but the error accumulates over time
frame_idA string in ROS2 used to identify coordinate frames. The frame_id in the message header indicates the coordinate frame in which the data is expressed. In this example, odom_frame is the world coordinate frame and base_footprint is the robot body coordinate frame
Covariance matrixA mathematical tool representing the uncertainty of an estimate. In the Odometry message, pose.covariance describes the uncertainty of position and orientation, and twist.covariance describes the uncertainty of velocity. The smaller the value, the higher the confidence
EncoderAn angle sensor mounted on the motor shaft, which measures the rotation angle and direction of the motor through pulse counting. Combined with the wheel diameter, it can estimate the distance and speed of wheel movement, and is the core input device for Dead Reckoning
ROS time synchronizationThe micro-ROS node synchronizes its clock with the ROS2 Agent through rmw_uros_sync_session(), obtaining a unified ROS timestamp and ensuring that data from different nodes is consistent and comparable on the time axis

2. Technical Architecture

This example implements a complete chassis control and state feedback node, serving as both a publisher and a subscriber.

Configuration ItemValueDescription
Node nameodom_publisherROS2 node name
Published topicodom_rawTopic for publishing odometry data
Message typenav_msgs/msg/OdometryROS2 standard odometry message
Publish frequency10 HzPeriod 100 ms
Subscribed topiccmd_velSpeed control topic
cmd_vel timeout2000 msAutomatically stops the car if no cmd_vel message is received for more than 2 seconds (different from the 1500 ms in Lesson 6)
QoS modeUplink best_effort (odometry), downlink reliable (cmd_vel)The odometry uses best-effort transmission, cmd_vel uses reliable transmission

In terms of software architecture, the program creates 1 timer (publishes odometry at 10 Hz) and 1 subscriber (cmd_vel) simultaneously in micro_ros_task, uniformly scheduled by the executor (EXECUTOR_HANDLE_COUNT=2), ensuring the coordinated operation of communication and control.

3. Core Topic Quick Reference

Firmware Uplink (Sensor Data)

TopicMessage TypeQoSDescription
/odom_rawnav_msgs/msg/Odometrybest_effortOdometry data (position + orientation + velocity), published at 10 Hz

Firmware Downlink (Control Commands)

TopicMessage TypeQoSFieldRangeDirection
/cmd_velgeometry_msgs/msg/TwistReliablelinear.x (linear velocity m/s)[-1.0, 1.0]+= forward, −= backward
   angular.z (angular velocity rad/s)[-6.0, 6.0]+= turn left, −= turn right

Timeout safety: If no new cmd_vel is received within 2000 ms, the car stops automatically.

Usage Example:

# View the odometry data
ros2 topic echo /odom_raw
# Control the car to move forward
ros2 topic pub /cmd_vel geometry_msgs/msg/Twist "{linear: {x: 0.3}, angular: {z: 0.0}}"

4. Middleware Configuration

micro-ROS uses a static memory model (RCUTILS_AVOID_DYNAMIC_ALLOCATION=ON), all entity memory is pre-allocated at compile time, and runtime dynamic creation is not supported.

Configuration file: ~/esp/extra_components/micro_ros_espidf_component/colcon.meta

| RMW_UXRCE_TRANSPORT | udp | UDP transport (Wi-Fi communication) | | RMW_UXRCE_MAX_PUBLISHERS | 6 | Maximum number of publishers | | RMW_UXRCE_MAX_SUBSCRIPTIONS | 6 | Maximum number of subscribers | | RMW_UXRCE_MAX_NODES | 1 | Maximum number of nodes | | RMW_UXRCE_MAX_SERVICES | 0 | Services not supported | | RMW_UXRCE_MAX_CLIENTS | 0 | Clients not supported | | RMW_UXRCE_MAX_HISTORY | 1 | History depth = 1, no message queuing | | UCLIENT_UDP_TRANSPORT_MTU | 4096 | Maximum UDP packet size 4 KB | | UCLIENT_MIN_HEARTBEAT_TIME_INTERVAL | 1 | Agent heartbeat interval 1 ms |

Key Constraints:

  • MAX_HISTORY=1 means there is no message buffer queue, so uplink topics (firmware → host) uniformly use best_effort QoS

  • MAX_SERVICES=0, MAX_CLIENTS=0 means only topic communication is supported, not Service/Client

  • After modifying colcon.meta, the micro-ROS library must be recompiled for the changes to take effect: run idf.py clean-microros in the project directory to clean and rebuild the micro-ROS library, then idf.py build to compile the firmware

QoS selection analysis of this experiment:

DirectionTopicQoSReason
Uplink (firmware → host)/odom_rawbest_effortMAX_HISTORY=1 has no buffering, so reliable retransmission is meaningless; odometry updates at high frequency (10 Hz), losing one frame has little impact
Downlink (host → firmware)/cmd_velreliablerclc_subscription_init_default(); control commands must be reliably delivered, loss may lead to loss of control

The downlink cmd_vel uses the default Reliable QoS, relying on RMW_UXRCE_STREAM_HISTORY=2 (2 MTU buffers) and RMW_UXRCE_PUBLISH_RELIABLE_TIMEOUT=5 (5 ms timeout) to ensure reliability.

3. Experiment Steps

1. Hardware Preparation

HardwareRequirement
MicroROS-V2 control board✅ Required
USB Type-C cable✅ Required
Wi-Fi network (2.4 GHz)✅ Required
DC geared motor (with encoder) ×2✅ Required
Motor driver board✅ Required

2. Prerequisites

  1. The ESP32-IDF development environment has been set up

  2. The MicroROS-V2 control board is connected to the computer via USB, and the motors (including encoders) are correctly connected

  3. Configure the Wi-Fi SSID, password, Agent IP and port via idf.py menuconfig (the micro_ros menu item)

    xxxxxxxxxx
    cd ~/esp/microROS_samples/odom_publisher
    source ~/esp/esp-idf/export.sh
    idf.py menuconfig

    image-20260723154206694

    1. Select example-app setting to set the domain ID to be consistent with the virtual machine

    image-20260811195030534

    After configuration, press "s" to save and press "q" to exit the configuration interface.

3. Start micro_ros_agent (Terminal 1)

Start the micro-ROS Agent on the ROS2 host:

xxxxxxxxxx
cd ~/uros_ws
source install/local_setup.bash
ros2 run micro_ros_agent micro_ros_agent udp4 --port 8090 -v4

4. Build and Flash (Terminal 2)

xxxxxxxxxx
cd ~/esp/microROS_samples/odom_publisher
source ~/esp/esp-idf/export.sh
idf.py build flash monitor

5. How to Stop

  1. First send a stop command: ros2 topic pub /cmd_vel geometry_msgs/msg/Twist "{linear: {x: 0.0}, angular: {z: 0.0}}" -1

  2. Press Ctrl+] in Terminal 2 to exit the serial monitor (stop firmware execution)

  3. Press Ctrl+C in Terminal 1 to stop micro_ros_agent

4. Experiment Result

After the build and flash succeed, on the ROS2 host:

View the odometry data:

xxxxxxxxxx
ros2 topic echo /odom_raw

Expected output (updated at 10 Hz):

xxxxxxxxxx
header:
  stamp:
    sec: xxx
    nanosec: xxx
  frame_id: odom_frame
child_frame_id: base_footprint
pose:
  pose:
    position:
      x: 0.0
      y: 0.0
      z: 0.0
    orientation:
      x: 0.0
      y: 0.0
      z: 0.0
      w: 1.0
  covariance: [...]
twist:
  twist:
    linear:
      x: 0.0
      ...
    angular:
      z: 0.0
      ...
  covariance: [...]

Control the car and observe the odometry changes:

​x
# Send a forward command
ros2 topic pub /cmd_vel geometry_msgs/msg/Twist "{linear: {x: 0.3}, angular: {z: 0.0}}"

# Observe the odometry: pose.pose.position.x should keep increasing
ros2 topic echo /odom_raw | grep "x:"

Timeout stop protection: If no new cmd_vel message is received within 2000 ms (2 seconds), the node automatically sets the motor speed to 0 and stops motion to prevent loss of control. This is the same mechanism as Lesson 6 (twist_subscriber, timeout 1500 ms), but with a different timeout threshold.

Control board serial port output:

xxxxxxxxxx
I (xxx) MAIN: Agent reachable: 192.168.x.x:8888
I (xxx) MAIN: free heap before support init: xxx
I (xxx) MAIN: stack watermark before support init: xxx
I (xxx) MAIN: free heap after support init: xxx
I (xxx) MAIN: stack watermark after support init: xxx
I (xxx) MAIN: time synced, offset=xxx ms
I (xxx) MAIN: micro-ROS odom + cmd_vel init done
I (xxx) MAIN: cmd_vel: vx=0.300 wz=0.000

5. Program Analysis

Source code paths:

  • Firmware source code: ~/esp/microROS_samples/odom_publisher/main/main.c

  • Encoder driver: ~/esp/microROS_samples/odom_publisher/components/encoder/encoder.h, encoder.c

1. Main Program Logic

main/main.c is a fully functional chassis control and odometry node, and the flow is as follows:

  1. In app_main():

    • Calls Motor_Init() to initialize the motors and encoders

    • Creates the motion queue and the motion_worker_task task

    • Initializes the Wi-Fi network

    • Creates micro_ros_task

  2. In micro_ros_task:

    • Creates the node odom_publisher

    • Initializes static fields of the odometry message such as frame_id and covariance matrices (odom_ros_init)

    • Publishes the topic odom_raw in best_effort mode, with message type nav_msgs/msg/Odometry

    • Subscribes to the topic cmd_vel in Reliable mode, with message type geometry_msgs/msg/Twist

    • Creates a 100 ms period timer bound to the odometry update callback

    • The executor manages 1 timer and 1 subscriber simultaneously (EXECUTOR_HANDLE_COUNT=2)

    • Synchronizes the ROS time at startup

2. Key Components

Setting the frame_id of the odometry message:

Source file: ~/esp/microROS_samples/odom_publisher/main/main.c

xxxxxxxxxx
static void odom_ros_init(void)
{
    const char *content_frame_id = "odom_frame";
    const char *content_child_frame_id = "base_footprint";

    msg_odom.header.frame_id =
        micro_ros_string_utilities_set(msg_odom.header.frame_id, frame_id);
    msg_odom.child_frame_id =
        micro_ros_string_utilities_set(msg_odom.child_frame_id, child_frame_id);

    // Covariance matrix settings
    msg_odom.pose.covariance[0] = 0.001;   // x position covariance
    msg_odom.pose.covariance[7] = 0.001;   // y position covariance
    msg_odom.pose.covariance[35] = 0.001;  // yaw angle covariance
    msg_odom.twist.covariance[0] = 0.0001; // x velocity covariance
    msg_odom.twist.covariance[7] = 0.0001; // y velocity covariance
    msg_odom.twist.covariance[35] = 0.0001;// angular velocity covariance
}

Dead Reckoning algorithm:

Source file: ~/esp/microROS_samples/odom_publisher/main/main.c

xxxxxxxxxx
static void odom_update(float vel_dt, float linear_vel_x,
                         float linear_vel_y, float angular_vel_z)
{
    float delta_heading = angular_vel_z * vel_dt;
    float cos_h = cosf(heading_);
    float sin_h = sinf(heading_);

    // Convert body-frame velocities into world-frame displacement
    float delta_x = (linear_vel_x * cos_h - linear_vel_y * sin_h) * vel_dt;
    float delta_y = (linear_vel_x * sin_h + linear_vel_y * cos_h) * vel_dt;

    x_pos_ += delta_x;
    y_pos_ += delta_y;
    heading_ = normalize_angle(heading_ + delta_heading);

    // Convert Euler angles to a quaternion to fill the orientation
    float q[4];
    odom_euler_to_quat(0.0f, 0.0f, heading_, q);
    msg_odom.pose.pose.orientation.x = (double)q[1];
    msg_odom.pose.pose.orientation.y = (double)q[2];
    msg_odom.pose.pose.orientation.z = (double)q[3];
    msg_odom.pose.pose.orientation.w = (double)q[0];

    // Fill the velocities
    msg_odom.twist.twist.linear.x = linear_vel_x;
    msg_odom.twist.twist.angular.z = angular_vel_z;
}

components/encoder/encoder.h — Encoder interface:

Parameter definition file: ~/esp/microROS_samples/odom_publisher/components/encoder/encoder.h

Pin/ParameterValueDescription
ENCODER_GPIO_H1A5Phase A of the M1 motor encoder
ENCODER_GPIO_H1B4Phase B of the M1 motor encoder
ENCODER_GPIO_H2A7Phase A of the M2 motor encoder
ENCODER_GPIO_H2B6Phase B of the M2 motor encoder
ENCODER_PCNT_HIGH_LIMIT1000Pulse counter upper limit
ENCODER_PCNT_LOW_LIMIT-1000Pulse counter lower limit
FunctionDescription
Encoder_Init()Initializes the PCNT peripherals of the two encoders
Encoder_Get_Count_M1()Gets the count value of the M1 motor encoder
Encoder_Get_Count_M2()Gets the count value of the M2 motor encoder

ROS time synchronization mechanism:

Source file: ~/esp/microROS_samples/odom_publisher/main/main.c

xxxxxxxxxx
static void sync_time(void)
{
    if (RMW_RET_OK == rmw_uros_sync_session(1000)) {
        uint64_t now_ms = get_millisecond();
        uint64_t ros_time_ms = rmw_uros_epoch_millis();
        time_offset_ms = ros_time_ms - now_ms;
        ESP_LOGI(TAG, "time synced, offset=%llu ms",
                 (unsigned long long)time_offset_ms);
    } else {
        time_offset_ms = 0;
        ESP_LOGW(TAG, "time sync failed, use local uptime");
    }
}

static struct timespec get_timespec(void)
{
    struct timespec tp = {0};
    uint64_t now_ms = get_millisecond() + time_offset_ms;
    tp.tv_sec = (time_t)(now_ms / 1000ULL);
    tp.tv_nsec = (long)((now_ms % 1000ULL) * 1000000ULL);
    return tp;
}

Timer callback timer_odom_callback:

Source file: ~/esp/microROS_samples/odom_publisher/main/main.c

xxxxxxxxxx
static void timer_odom_callback(rcl_timer_t *timer, int64_t last_call_time)
{
    RCLC_UNUSED(last_call_time);

    if (timer == NULL) {
        return;
    }

    uint64_t now_ms = get_millisecond();
    if (prev_odom_update_ms == 0) {
        prev_odom_update_ms = now_ms;
    }

    // 1. Calculate the time interval
    float vel_dt = (float)(now_ms - prev_odom_update_ms) / 1000.0f;
    prev_odom_update_ms = now_ms;

    // 2. Get the actual speed estimated from the encoders
    Motion_Get_Speed(&car_motion);

    // 3. Dead Reckoning update
    odom_update(
        vel_dt,
        car_motion.Vx,
        car_motion.Vy,
        car_motion.Wz);

    // 4. Fill the ROS timestamp
    struct timespec time_stamp = get_timespec();
    msg_odom.header.stamp.sec = (int32_t)time_stamp.tv_sec;
    msg_odom.header.stamp.nanosec = (uint32_t)time_stamp.tv_nsec;

    // 5. Publish the odometry message
    rcl_ret_t rc = rcl_publish(&publisher_odom, &msg_odom, NULL);
    if (rc != RCL_RET_OK) {
        odom_pub_fail_cnt++;
        if ((odom_pub_fail_cnt % 20U) == 1U) {
            print_rcl_error("rcl_publish(&publisher_odom, &msg_odom, NULL)",
                            __LINE__, rc);
        }
    }
}

6. Common Problems

ProblemCauseSolution
Odometry position data does not updateThe encoders are not read correctly or the motors are not rotatingCheck the encoder wiring and confirm the motors are actually rotating
Severe odometry data driftDead Reckoning error accumulationThis is an inherent limitation of the open-loop node; fuse IMU or external localization to correct it
Inaccurate timestampsROS time synchronization failedCheck whether the Agent time synchronization function is normal; it will automatically attempt synchronization at startup
frame_id mismatchInconsistent coordinate frame conventions with other ROS2 nodesThe default is odom_frame → base_footprint; modify as needed
Inaccurate encoder countsPulse count overflow or incorrect direction judgmentAdjust the ENCODER_PCNT_HIGH_LIMIT and MOTOR_ENCODER_SIGN macros
Subscribe To Speed Control Topic
Publish Imu Data Topic

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