Ubuntu 22.04 与 ROS2 Humble 下 Intel RealSense D435i 相机配置指南 | 极客日志

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Ubuntu 22.04 与 ROS2 Humble 下 Intel RealSense D435i 相机配置指南

本文介绍了在 Ubuntu 22.04 和 ROS2 Humble 环境下配置 Intel RealSense D435i 相机的完整流程。内容包括安装 RealSense SDK 及 ROS2 驱动（二进制或源码），创建自定义 ROS2 功能包，编写并发布订阅图像、深度及 IMU 数据的 C++ 节点，配置启动文件以集成 RViz 可视化，以及常用命令和故障排除方法。通过该指南可实现相机的数据采集与基础处理。

DataScient发布于 2026/3/260 浏览

Ubuntu 22.04/ROS2 Humble 下使用 Intel RealSense D435i 相机

1. 安装 RealSense SDK 和 ROS2 驱动

1.1 安装依赖

sudo apt update
sudo apt install -y git cmake libssl-dev libusb-1.0-0-dev pkg-config libgtk-3-dev
 apt install -y libglfw3-dev libgl1-mesa-dev libglu1-mesa-dev
 apt install -y python3-dev python3-pip

sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-key F6E65AC044F831AC80A06380C8B3A55A6F3EFCDE || sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-key F6E65AC044F831AC80A06380C8B3A55A6F3EFCDE

sudo add-apt-repository "deb https://librealsense.intel.com/Debian/apt-repo $(lsb_release -cs) main" -u

sudo apt-get install librealsense2-dkms librealsense2-utils librealsense2-dev librealsense2-dbg

mkdir -p ~/realsense_ws
cd ~/realsense_ws

git clone https://github.com/IntelRealSense/librealsense.git
cd librealsense

./scripts/setup_udev_rules.sh
./scripts/patch-realsense-ubuntu-lts.sh

mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DBUILD_EXAMPLES=true
make -j$(nproc)
sudo make install

sudo apt install ros-humble-realsense2-*

mkdir -p ~/ros2_ws/src
cd ~/ros2_ws/src

git clone https://github.com/IntelRealSense/realsense-ros.git -b humble

cd ~/ros2_ws
rosdep install -i --from-path src --rosdistro humble -y

colcon build
source install/setup.bash

cd ~/ros2_ws/src
ros2 pkg create d435i_camera --build-type ament_cmake --dependencies rclcpp sensor_msgs cv_bridge image_transport geometry_msgs tf2 tf2_ros

d435i_camera/
├── CMakeLists.txt
├── package.xml
├── include/
├── src/
│   ├── d435i_publisher.cpp
│   └── d435i_subscriber.cpp
└── launch/
    ├── d435i_launch.py
    └── rviz_config.rviz

<?xml version="1.0"?>
<?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
<package format="3">
  <name>d435i_camera</name>
  <version>0.1.0</version>
  <description>D435i Camera ROS2 Package</description>
  <maintainer email="[email protected]">Your Name</maintainer>
  <license>Apache-2.0</license>
  <depend>rclcpp</depend>
  <depend>sensor_msgs</depend>
  <depend>cv_bridge</depend>
  <depend>image_transport</depend>
  <depend>geometry_msgs</depend>
  <depend>tf2</depend>
  <depend>tf2_ros</depend>
  <depend>realsense2_camera</depend>
  <depend>vision_msgs</depend>
  <depend>std_msgs</depend>
  <exec_depend>rviz2</exec_depend>
  <exec_depend>ros2launch</exec_depend>
  <export>
    <build_type>ament_cmake</build_type>
  </export>
</package>

cmake_minimum_required(VERSION 3.8)
project(d435i_camera)

if(CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
  add_compile_options(-Wall -Wextra -Wpedantic)
endif()

# 查找依赖
find_package(ament_cmake REQUIRED)
find_package(rclcpp REQUIRED)
find_package(sensor_msgs REQUIRED)
find_package(cv_bridge REQUIRED)
find_package(image_transport REQUIRED)
find_package(geometry_msgs REQUIRED)
find_package(tf2 REQUIRED)
find_package(tf2_ros REQUIRED)
find_package(vision_msgs REQUIRED)
find_package(std_msgs REQUIRED)
find_package(OpenCV REQUIRED)

# 添加可执行文件
add_executable(d435i_publisher src/d435i_publisher.cpp)
ament_target_dependencies(d435i_publisher rclcpp sensor_msgs cv_bridge image_transport geometry_msgs tf2 tf2_ros vision_msgs std_msgs)

add_executable(d435i_subscriber src/d435i_subscriber.cpp)
ament_target_dependencies(d435i_subscriber rclcpp sensor_msgs cv_bridge std_msgs)

# 安装目标
install(TARGETS d435i_publisher d435i_subscriber DESTINATION lib/${PROJECT_NAME})

# 安装启动文件
install(DIRECTORY launch DESTINATION share/${PROJECT_NAME})

# 安装 rviz 配置
install(FILES launch/rviz_config.rviz DESTINATION share/${PROJECT_NAME}/launch)

ament_package()

#include<rclcpp/rclcpp.hpp>
#include<sensor_msgs/msg/image.hpp>
#include<sensor_msgs/msg/camera_info.hpp>
#include<sensor_msgs/msg/imu.hpp>
#include<cv_bridge/cv_bridge.h>
#include<image_transport/image_transport.hpp>
#include<opencv2/opencv.hpp>
#include<tf2_ros/transform_broadcaster.h>
#include<geometry_msgs/msg/transform_stamped.hpp>
#include<vision_msgs/msg/detection2_d_array.hpp>

class D435iPublisher : public rclcpp::Node {
public:
  D435iPublisher() : Node("d435i_publisher") {
    // 创建发布者
    color_pub_ = image_transport::create_publisher(this, "camera/color/image_raw");
    depth_pub_ = image_transport::create_publisher(this, "camera/depth/image_raw");
    imu_pub_ = create_publisher<sensor_msgs::msg::Imu>("camera/imu", 10);
    
    // 创建相机信息发布者
    color_info_pub_ = create_publisher<sensor_msgs::msg::CameraInfo>("camera/color/camera_info", 10);
    depth_info_pub_ = create_publisher<sensor_msgs::msg::CameraInfo>("camera/depth/camera_info", 10);
    
    // 创建 TF 广播器
    tf_broadcaster_ = std::make_unique<tf2_ros::TransformBroadcaster>(this);
    
    // 创建定时器（模拟相机数据）
    timer_ = create_wall_timer(
      std::chrono::milliseconds(33), // 30Hz
      std::bind(&D435iPublisher::timer_callback, this));
    
    // 创建检测结果发布者
    detection_pub_ = create_publisher<vision_msgs::msg::Detection2DArray>("detections", 10);
    RCLCPP_INFO(this->get_logger(), "D435i Publisher started");
  }

private:
  void timer_callback() {
    publish_color_image();
    publish_depth_image();
    publish_imu_data();
    publish_camera_info();
    publish_tf();
    publish_detections();
  }

  void publish_color_image() {
    // 创建模拟彩色图像（640x480）
    cv::Mat color_image(480, 640, CV_8UC3, cv::Scalar(100, 100, 255));
    // 添加一些视觉特征
    cv::circle(color_image, cv::Point(320, 240), 50, cv::Scalar(0, 255, 0), 3);
    cv::rectangle(color_image, cv::Rect(100, 100, 200, 200), cv::Scalar(255, 0, 0), 2);
    
    // 转换为 ROS 消息
    auto msg = cv_bridge::CvImage(std_msgs::msg::Header(), "bgr8", color_image).toImageMsg();
    msg->header.stamp = now();
    msg->header.frame_id = "camera_color_optical_frame";
    color_pub_.publish(msg);
  }

  void publish_depth_image() {
    // 创建模拟深度图像（640x480）
    cv::Mat depth_image(480, 640, CV_16UC1);
    // 模拟深度数据
    for (int i = 0; i < depth_image.rows; i++) {
      for (int j = 0; j < depth_image.cols; j++) {
        // 模拟距离（毫米）
        int distance = 1000 + (i * 640 + j) % 500;
        depth_image.at<uint16_t>(i, j) = distance;
      }
    }
    
    // 转换为 ROS 消息
    auto msg = cv_bridge::CvImage(std_msgs::msg::Header(), "16UC1", depth_image).toImageMsg();
    msg->header.stamp = now();
    msg->header.frame_id = "camera_depth_optical_frame";
    depth_pub_.publish(msg);
  }

  void publish_imu_data() {
    auto imu_msg = std::make_unique<sensor_msgs::msg::Imu>();
    imu_msg->header.stamp = now();
    imu_msg->header.frame_id = "camera_imu_optical_frame";
    // 模拟 IMU 数据
    imu_msg->linear_acceleration.x = 0.0;
    imu_msg->linear_acceleration.y = 0.0;
    imu_msg->linear_acceleration.z = 9.81;
    imu_msg->angular_velocity.x = 0.01;
    imu_msg->angular_velocity.y = 0.02;
    imu_msg->angular_velocity.z = 0.03;
    imu_pub_->publish(std::move(imu_msg));
  }

  void publish_camera_info() {
    // 彩色相机参数
    auto color_info = std::make_unique<sensor_msgs::msg::CameraInfo>();
    color_info->header.stamp = now();
    color_info->header.frame_id = "camera_color_optical_frame";
    color_info->height = 480;
    color_info->width = 640;
    color_info->distortion_model = "plumb_bob";
    color_info->d = {0.0, 0.0, 0.0, 0.0, 0.0};
    color_info->k = {615.0, 0.0, 320.0, 0.0, 615.0, 240.0, 0.0, 0.0, 1.0};
    color_info->r = {1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0};
    color_info->p = {615.0, 0.0, 320.0, 0.0, 0.0, 615.0, 240.0, 0.0, 0.0, 0.0, 1.0, 0.0};
    
    // 深度相机参数
    auto depth_info = std::make_unique<sensor_msgs::msg::CameraInfo>();
    *depth_info = *color_info;
    depth_info->header.frame_id = "camera_depth_optical_frame";
    
    color_info_pub_->publish(std::move(color_info));
    depth_info_pub_->publish(std::move(depth_info));
  }

  void publish_tf() {
    geometry_msgs::msg::TransformStamped transform;
    transform.header.stamp = now();
    transform.header.frame_id = "camera_link";
    transform.child_frame_id = "camera_color_optical_frame";
    transform.transform.translation.x = 0.0;
    transform.transform.translation.y = 0.0;
    transform.transform.translation.z = 0.0;
    transform.transform.rotation.x = 0.0;
    transform.transform.rotation.y = 0.0;
    transform.transform.rotation.z = 0.0;
    transform.transform.rotation.w = 1.0;
    tf_broadcaster_->sendTransform(transform);
  }

  void publish_detections() {
    auto detection_msg = std::make_unique<vision_msgs::msg::Detection2DArray>();
    detection_msg->header.stamp = now();
    detection_msg->header.frame_id = "camera_color_optical_frame";
    
    // 添加模拟检测结果
    vision_msgs::msg::Detection2D detection;
    detection.bbox.center.position.x = 320.0;
    detection.bbox.center.position.y = 240.0;
    detection.bbox.size_x = 100.0;
    detection.bbox.size_y = 100.0;
    detection_msg->detections.push_back(detection);
    
    detection_pub_->publish(std::move(detection_msg));
  }

  // 发布者
  image_transport::Publisher color_pub_;
  image_transport::Publisher depth_pub_;
  rclcpp::Publisher<sensor_msgs::msg::Imu>::SharedPtr imu_pub_;
  rclcpp::Publisher<sensor_msgs::msg::CameraInfo>::SharedPtr color_info_pub_;
  rclcpp::Publisher<sensor_msgs::msg::CameraInfo>::SharedPtr depth_info_pub_;
  rclcpp::Publisher<vision_msgs::msg::Detection2DArray>::SharedPtr detection_pub_;
  
  // TF 广播器
  std::unique_ptr<tf2_ros::TransformBroadcaster> tf_broadcaster_;
  
  // 定时器
  rclcpp::TimerBase::SharedPtr timer_;
};

int main(int argc, char* argv[]) {
  rclcpp::init(argc, argv);
  auto node = std::make_shared<D435iPublisher>();
  rclcpp::spin(node);
  rclcpp::shutdown();
  return 0;
}

#include<rclcpp/rclcpp.hpp>
#include<sensor_msgs/msg/image.hpp>
#include<sensor_msgs/msg/imu.hpp>
#include<cv_bridge/cv_bridge.h>
#include<opencv2/opencv.hpp>
#include<memory>

class D435iSubscriber : public rclcpp::Node {
public:
  D435iSubscriber() : Node("d435i_subscriber") {
    // 创建订阅者
    color_sub_ = create_subscription<sensor_msgs::msg::Image>("camera/color/image_raw", 10,
      std::bind(&D435iSubscriber::color_callback, this, std::placeholders::_1));
    depth_sub_ = create_subscription<sensor_msgs::msg::Image>("camera/depth/image_raw", 10,
      std::bind(&D435iSubscriber::depth_callback, this, std::placeholders::_1));
    imu_sub_ = create_subscription<sensor_msgs::msg::Imu>("camera/imu", 10,
      std::bind(&D435iSubscriber::imu_callback, this, std::placeholders::_1));
    RCLCPP_INFO(this->get_logger(), "D435i Subscriber started");
  }

private:
  void color_callback(const sensor_msgs::msg::Image::SharedPtr msg) {
    try {
      // 转换 ROS 图像消息到 OpenCV 格式
      cv_bridge::CvImagePtr cv_ptr = cv_bridge::toCvCopy(msg, "bgr8");
      cv::Mat image = cv_ptr->image;
      
      // 显示图像信息
      RCLCPP_INFO_ONCE(this->get_logger(), "Color Image: %dx%d, Encoding: %s", image.cols, image.rows, msg->encoding.c_str());
      
      // 处理图像（示例：转换为灰度图）
      cv::Mat gray_image;
      cv::cvtColor(image, gray_image, cv::COLOR_BGR2GRAY);
      
      // 显示图像（可选）
      if (show_images_) {
        cv::imshow("Color Image", image);
        cv::imshow("Gray Image", gray_image);
        cv::waitKey(1);
      }
    } catch (cv_bridge::Exception& e) {
      RCLCPP_ERROR(this->get_logger(), "cv_bridge exception: %s", e.what());
    }
  }

  void depth_callback(const sensor_msgs::msg::Image::SharedPtr msg) {
    try {
      cv_bridge::CvImagePtr cv_ptr = cv_bridge::toCvCopy(msg);
      cv::Mat depth_image = cv_ptr->image;
      
      // 计算深度统计信息
      double min_val, max_val;
      cv::minMaxLoc(depth_image, &min_val, &max_val);
      RCLCPP_INFO_THROTTLE(this->get_logger(), *this->get_clock(), 1000, "Depth Image: Min=%.2fmm, Max=%.2fmm", min_val, max_val);
      
      // 可视化深度图（归一化到 0-255）
      if (show_images_ && depth_image.type() == CV_16UC1) {
        cv::Mat depth_visual;
        depth_image.convertTo(depth_visual, CV_8U, 255.0 / max_val);
        cv::applyColorMap(depth_visual, depth_visual, cv::COLORMAP_JET);
        cv::imshow("Depth Image", depth_visual);
        cv::waitKey(1);
      }
    } catch (cv_bridge::Exception& e) {
      RCLCPP_ERROR(this->get_logger(), "cv_bridge exception: %s", e.what());
    }
  }

  void imu_callback(const sensor_msgs::msg::Imu::SharedPtr msg) {
    // 提取 IMU 数据
    double accel_x = msg->linear_acceleration.x;
    double accel_y = msg->linear_acceleration.y;
    double accel_z = msg->linear_acceleration.z;
    double gyro_x = msg->angular_velocity.x;
    double gyro_y = msg->angular_velocity.y;
    double gyro_z = msg->angular_velocity.z;
    
    // 打印 IMU 数据（限制频率）
    RCLCPP_INFO_THROTTLE(this->get_logger(), *this->get_clock(), 1000, "IMU - Accel: [%.3f, %.3f, %.3f], Gyro: [%.3f, %.3f, %.3f]", accel_x, accel_y, accel_z, gyro_x, gyro_y, gyro_z);
  }

  // 订阅者
  rclcpp::Subscription<sensor_msgs::msg::Image>::SharedPtr color_sub_;
  rclcpp::Subscription<sensor_msgs::msg::Image>::SharedPtr depth_sub_;
  rclcpp::Subscription<sensor_msgs::msg::Imu>::SharedPtr imu_sub_;
  bool show_images_ = true; // 设置为 false 如果不希望显示图像
};

int main(int argc, char* argv[]) {
  rclcpp::init(argc, argv);
  auto node = std::make_shared<D435iSubscriber>();
  rclcpp::spin(node);
  rclcpp::shutdown();
  return 0;
}

#!/usr/bin/env python3
import os
from launch import LaunchDescription
from launch_ros.actions import Node
from ament_index_python.packages import get_package_share_directory

def generate_launch_description():
  # 获取包目录
  pkg_dir = get_package_share_directory('d435i_camera')
  
  # 配置 RealSense 相机节点
  realsense_node = Node(
    package='realsense2_camera',
    executable='realsense2_camera_node',
    namespace='camera',
    name='realsense2_camera',
    parameters=[{
      'rgb_camera.profile': '640x480x30',
      'depth_module.profile': '640x480x30',
      'enable_color': True,
      'enable_depth': True,
      'enable_imu': True,
      'align_depth': True,
      'enable_pointcloud': True,
      'pointcloud_texture_stream': 'RS2_STREAM_COLOR',
      'initial_reset': True,
      'unite_imu_method': 'linear_interpolation',
    }],
    output='screen'
  )
  
  # 自定义发布者节点
  publisher_node = Node(
    package='d435i_camera',
    executable='d435i_publisher',
    name='d435i_publisher',
    output='screen'
  )
  
  # 自定义订阅者节点
  subscriber_node = Node(
    package='d435i_camera',
    executable='d435i_subscriber',
    name='d435i_subscriber',
    output='screen'
  )
  
  # RViz2 节点
  rviz_config_file = os.path.join(pkg_dir, 'launch', 'rviz_config.rviz')
  rviz_node = Node(
    package='rviz2',
    executable='rviz2',
    name='rviz2',
    arguments=['-d', rviz_config_file],
    output='screen'
  )
  
  # TF 静态发布器
  static_tf_node = Node(
    package='tf2_ros',
    executable='static_transform_publisher',
    arguments=['0', '0', '0', '0', '0', '0', 'camera_link', 'camera_color_optical_frame']
  )
  
  return LaunchDescription([
    realsense_node,
    publisher_node,
    subscriber_node,
    static_tf_node,
    rviz_node
  ])

cd ~/ros2_ws
colcon build --packages-select d435i_camera
source install/setup.bash

source ~/ros2_ws/install/setup.bash
ros2 launch realsense2_camera rs_launch.py

ros2 topic list

ros2 topic echo /camera/color/image_raw --once

source install/setup.bash
ros2 launch d435i_camera d435i_launch.py

source install/setup.bash
ros2 run d435i_camera d435i_subscriber

source install/setup.bash
ros2 run d435i_camera d435i_publisher

ros2 topic list
ros2 topic echo /camera/color/image_raw --once
ros2 topic echo /camera/imu --once

ros2 topic list

ros2 topic info /camera/color/image_raw
ros2 topic info /camera/depth/image_raw

ros2 topic echo /camera/imu

ros2 run tf2_tools view_frames.py

sudo apt install ros-humble-rqt*

rqt_image_view

rqt

权限问题

sudo usermod -a -G dialout $USER
sudo usermod -a -G video $USER

UDEV 规则问题

sudo cp ~/realsense_ws/librealsense/config/99-realsense-libusb.rules /etc/udev/rules.d/
sudo udevadm control --reload-rules && sudo udevadm trigger

重新插拔相机
```
sudo systemctl restart udev
```
检查相机连接
```
rs-enumerate-devices
```
测试深度流
```
realsense-viewer
```

printenv | grep ROS

echo $AMENT_PREFIX_PATH

ros2 topic pub /test std_msgs/msg/String "data: 'Hello ROS2'" -1

parameters=[{
  'depth_fps': 30.0,
  'color_fps': 30.0,
  'gyro_fps': 200.0,
  'accel_fps': 63.0,
}]

parameters=[{
  'enable_pointcloud': True,
  'pointcloud_texture_stream': 'RS2_STREAM_COLOR',
}]

Ubuntu 22.04 与 ROS2 Humble 下 Intel RealSense D435i 相机配置指南

Ubuntu 22.04/ROS2 Humble 下使用 Intel RealSense D435i 相机

1. 安装 RealSense SDK 和 ROS2 驱动

1.1 安装依赖

1.2 安装 Intel RealSense SDK

方法一：二进制安装（推荐）

方法二：源码编译安装（不推荐）

1.3 安装 ROS2 RealSense 包

方法一：二进制安装（推荐）

方法二：源码编译安装（不推荐）

2. 创建自定义 ROS2 包

2.1 创建包

2.2 项目结构

3. 配置文件

3.1 package.xml

3.2 CMakeLists.txt

4. 源代码文件

4.1 src/d435i_publisher.cpp

4.2 src/d435i_subscriber.cpp

5. 启动文件

5.1 launch/d435i_launch.py

6. 编译和运行

6.1 编译包

6.2 测试 RealSense 驱动

6.3 运行自定义节点

方法 1：使用启动文件

方法 2：单独运行节点

7. 常用命令和工具

7.1 查看相机信息

7.2 使用 rqt 工具

8. 故障排除

8.1 常见问题解决

8.2 验证安装

9. 高级功能配置

9.1 修改发布频率

9.2 启用点云

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Ubuntu 22.04 与 ROS2 Humble 下 Intel RealSense D435i 相机配置指南

Ubuntu 22.04/ROS2 Humble 下使用 Intel RealSense D435i 相机

1. 安装 RealSense SDK 和 ROS2 驱动

1.1 安装依赖

1.2 安装 Intel RealSense SDK

方法一：二进制安装（推荐）

方法二：源码编译安装（不推荐）

1.3 安装 ROS2 RealSense 包

方法一：二进制安装（推荐）

方法二：源码编译安装（不推荐）

2. 创建自定义 ROS2 包

2.1 创建包

2.2 项目结构

3. 配置文件

3.1 package.xml

3.2 CMakeLists.txt

4. 源代码文件

4.1 src/d435i_publisher.cpp

4.2 src/d435i_subscriber.cpp

5. 启动文件

5.1 launch/d435i_launch.py

6. 编译和运行

6.1 编译包

6.2 测试 RealSense 驱动

6.3 运行自定义节点

方法 1：使用启动文件

方法 2：单独运行节点

7. 常用命令和工具

7.1 查看相机信息

7.2 使用 rqt 工具

8. 故障排除

8.1 常见问题解决

8.2 验证安装

9. 高级功能配置

9.1 修改发布频率

9.2 启用点云

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