奥比中光双目摄像头实现物品抓取的机器人系统

最新推荐文章于 2025-08-19 21:56:19 发布

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最新推荐文章于 2025-08-19 21:56:19 发布 · 1k 阅读

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#机器人 #unity #游戏引擎 #python #c++ #github

一个完整的方案，介绍如何使用奥比中光(Orbbec)双目摄像头获取物品位置并实现机器人抓取功能。

1. 系统架构概述

text

[奥比中光摄像头] → [点云数据处理] → [物品识别定位] → [运动规划] → [机械臂控制]

2. 硬件准备

奥比中光 Astra/Astra Pro 或 Femto 系列深度摄像头
ROS2兼容的机械臂(如UR, Franka, xArm等)
计算平台(如NVIDIA Jetson或高性能PC)

3. ROS2软件配置

3.1 安装奥比中光ROS2驱动

bash

sudo apt install ros-${ROS_DISTRO}-libuvc-camera
sudo apt install ros-${ROS_DISTRO}-camera-calibration

# 从源码安装Orbbec ROS2驱动
mkdir -p ~/orbbec_ws/src
cd ~/orbbec_ws/src
git clone https://github.com/orbbec/ros2_astra_camera.git
cd ..
rosdep install --from-paths src --ignore-src -r -y
colcon build --symlink-install
source install/setup.bash

3.2 启动摄像头节点

bash

ros2 launch astra_camera astra.launch.py

4. 物品识别与定位实现

4.1 创建ROS2包

bash

ros2 pkg create object_grasping --build-type ament_cmake \
--dependencies rclcpp rclcpp_components cv_bridge image_transport \
pcl_conversions pcl_ros tf2 tf2_ros tf2_geometry_msgs moveit_core \
moveit_ros_planning_interface

4.2 物品检测节点实现 (C++)

src/object_detector.cpp:

cpp

#include <rclcpp/rclcpp.hpp>
#include <sensor_msgs/msg/image.hpp>
#include <sensor_msgs/msg/point_cloud2.hpp>
#include <vision_msgs/msg/detection3_d_array.hpp>
#include <cv_bridge/cv_bridge.h>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl_conversions/pcl_conversions.h>
#include <pcl/filters/passthrough.h>
#include <pcl/segmentation/extract_clusters.h>

class ObjectDetector : public rclcpp::Node
{
public:
    ObjectDetector() : Node("object_detector")
    {
        // 订阅深度点云
        pointcloud_sub_ = this->create_subscription<sensor_msgs::msg::PointCloud2>(
            "/camera/depth/points", 10,
            std::bind(&ObjectDetector::pointcloudCallback, this, std::placeholders::_1));
        
        // 发布检测结果
        detection_pub_ = this->create_publisher<vision_msgs::msg::Detection3DArray>(
            "/detected_objects", 10);
        
        // 参数声明
        this->declare_parameter("cluster_tolerance", 0.02);
        this->declare_parameter("min_cluster_size", 100);
        this->declare_parameter("max_clus