深度学习方法生成抓取位姿与6D姿态估计的完整实现

如何将GraspNet等深度学习模型与6D姿态估计集成到ROS2和MoveIt中，实现高精度的机器人抓取系统。

1. 系统架构

text

[RGB-D传感器] → [物体检测与6D姿态估计] → [GraspNet抓取位姿生成] → [MoveIt运动规划] → [执行抓取]

2. 环境配置

2.1 安装依赖

bash

# 安装PyTorch (根据CUDA版本选择)
pip3 install torch torchvision torchaudio

# 安装其他依赖
pip3 install open3d scipy transforms3d tensorboardx

# 安装ROS2相关包
sudo apt install ros-${ROS_DISTRO}-vision-msgs ros-${ROS_DISTRO}-tf2-geometry-msgs

2.2 下载GraspNet模型

bash

mkdir -p ~/graspnet_ws/src
cd ~/graspnet_ws/src
git clone https://github.com/jsll/pytorch-graspnet.git
cd pytorch-graspnet
pip install -e .

3. 6D姿态估计实现

3.1 创建姿态估计节点

src/pose_estimation.py:

python

#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Image, CameraInfo, PointCloud2
from vision_msgs.msg import Detection3DArray, BoundingBox3D
from geometry_msgs.msg import Pose
from cv_bridge import CvBridge
import cv2
import numpy as np
import torch
from third_party.DenseFusion.lib.network import PoseNet, PoseRefineNet
from third_party.DenseFusion.lib.utils import *

class PoseEstimator(Node):
    def __init__(self):
        super().__init__('pose_estimator')
        
        # 加载预训练模型
        self.model = PoseNet(num_points=1000, num_obj=10)
        self.model.load_state_dict(torch.load('path/to/pose_model.pth'))
        self.model.cuda()
        
        self.refiner = PoseRefineNet(num_points=1000, num_obj=10)
        self.refiner.load_state_dict(torch.load('path/to/pose_refine_model.pth'))
        self.refiner.cuda()
        
        # 订阅RGB-D数据
        self.sub_rgb = self.create_subscription(
            Image, '/camera/color/image_raw', self.rgb_callback, 10)
        self.sub_depth = self.create_subscription(
            Image, '/camera/depth/image_raw', self.depth_callback, 10)
        self.sub_camera_info = self.create_subscription(
            CameraInfo, '/camera/color/camera_info', self.camera_info_callback, 10)
        
        # 发布检测结果
        self.pub_detections = self.create_publisher(
            Detection3DArray, '/object_detections_3d', 10)
        
        self.bridge = CvBridge()
        self.camera_matrix = None
        self.dist_coeffs = None
        self.current_rgb = None
        self.current_depth = None
        
    def camera_info_callback(self, msg):
        # 获取相机内参
        self.camera_matrix = np.array(msg.k).reshape(3, 3)
        self.dist_coeffs = np.array(msg.d)
        
    def rgb_callback(self, msg):
        self.current_rgb = self.bridge.imgmsg_to_cv2(msg, 'bgr8')
        
    def depth_callback(self, msg):
        sel