调了几天遇到很多坑啊！第一次玩Ros的痛！！ 1、首先看usb_cam： 直接下载代码： 进入创建好的工作空间：
cd ~/catkin_ws/src git clone https://github.com/bosch-ros-pkg/usb_cam.git然后退回到工作空间，编译代码：
cd ~/catkin_ws catkin_make编译好之后，添加编译好的文件到环境变量：
source devel/setup.bash然后接下来测试usb_cam： 先运行usb_cam节点：
rosrun usb_cam usb_cam_node运行上面命令发现没有显示图像，只看到摄像头打开了。这是因为ros发布的topic是/usb_cam/image_raw。新打开一个终端，可以通过如下命令查看：
rostopic list结果如下：
/rosout /rosout_agg /usb_cam/camera_info /usb_cam/image_raw /usb_cam/image_raw/compressed /usb_cam/image_raw/compressed/parameter_descriptions /usb_cam/image_raw/compressed/parameter_updates /usb_cam/image_raw/compressedDepth /usb_cam/image_raw/compressedDepth/parameter_descriptions /usb_cam/image_raw/compressedDepth/parameter_updates /usb_cam/image_raw/theora /usb_cam/image_raw/theora/parameter_descriptions /usb_cam/image_raw/theora/parameter_updates所以我们需要运行如下命令才可以看到图像：
rosrun image_view image_view image:=/usb_cam/image_raw或者直接写launch文件，这样就不用一个终端运行node，一个终端看图像。新建usb_cam_test.launch：
<launch> <node name="usb_cam" pkg="usb_cam" type="usb_cam_node" output="screen" > <param name="video_device" value="/dev/video0" /> <param name="image_width" value="640" /> <param name="image_height" value="480" /> <param name="pixel_format" value="yuyv" /> <param name="camera_frame_id" value="usb_cam" /> <param name="io_method" value="mmap"/> </node> <node name="image_view" pkg="image_view" type="image_view" respawn="false" output="screen"> <remap from="image" to="/usb_cam/image_raw"/> <param name="autosize" value="true" /> </node> </launch>其中字段的意义可按照字面理解，这里不再解释。 然后终端直接运行：
roslaunch usb_cam usb_cam_test.launch2、接下来，我们看usb_cam采集的图像怎么让opencv处理。 ros提供了一个cv_bridge用以转换Ros采集到图像到opencv能处理的图像。 这里主要参考官网对于cv_bridge的解释及其使用： http://wiki.ros.org/cv_bridge/Tutorials/UsingCvBridgeToConvertBetweenROSImagesAndOpenCVImages 包创建：http://wiki.ros.org/ROS/Tutorials/CreatingPackage 这里需要新建opecv的测试工作空间，opencv测试依赖于：
sensor_msgs cv_bridge roscpp std_msgs image_transport进入～/catkin_ws/src,创建测试包：
catkin_create_pkg opencvtest sensor_msgs cv_bridge roscpp std_msgs image_transport rospy roscpp创建好包之后，进入~/catkin_ws/src/opencvtest/src，将官网的代码保存到此处，命名为opencv_testcam.cpp：
#include <ros/ros.h> #include<image_transport/image_transport.h> #include<cv_bridge/cv_bridge.h> #include<sensor_msgs/image_encodings.h> #include<opencv2/imgproc/imgproc.hpp> #include<opencv2/highgui/highgui.hpp> static const std::string OPENCV_WINDOW = "Image window"; class ImageConverter { ros::NodeHandle nh_; image_transport::ImageTransport it_; image_transport::Subscriber image_sub_; image_transport::Publisher image_pub_; public: ImageConverter() : it_(nh_) { // Subscrive to input video feed and publish output video feed image_sub_ = it_.subscribe("/usb_cam/image_raw", 1, &ImageConverter::imageCb, this); image_pub_ = it_.advertise("/image_converter/output_video", 1); cv::namedWindow(OPENCV_WINDOW); } ~ImageConverter() { cv::destroyWindow(OPENCV_WINDOW); } void imageCb(const sensor_msgs::ImageConstPtr& msg) { cv_bridge::CvImagePtr cv_ptr; try { cv_ptr = cv_bridge::toCvCopy(msg, sensor_msgs::image_encodings::BGR8); } catch (cv_bridge::Exception& e) { ROS_ERROR("cv_bridge exception: %s", e.what()); return; } // Draw an example circle on the video stream if (cv_ptr->image.rows > 60 && cv_ptr->image.cols > 60) cv::circle(cv_ptr->image, cv::Point(50, 50), 10, CV_RGB(255,0,0)); // Update GUI Window cv::imshow(OPENCV_WINDOW, cv_ptr->image); cv::waitKey(3); // Output modified video stream image_pub_.publish(cv_ptr->toImageMsg()); } }; int main(int argc, char** argv) { ros::init(argc, argv, "image_converter"); ImageConverter ic; ros::spin(); return 0; }以上代码不解释了，可以查看官网的解释。这里需要注意的是代码：
image_sub_ = it_.subscribe("/usb_cam/image_raw", 1, &ImageConverter::imageCb, this);这里是usb摄像头的topic，官网默认是/camera/image_raw，这里修改为usb摄像头。 保存退出后，进入上个目录，修改CmakeList.txt，在文件最后添加：
find_package(OpenCV) include_directories(${OpenCV_INCLUDE_DIRS}) add_executable(opencv_testcam src/opencv_testcam.cpp) target_link_libraries(opencv_testcam ${catkin_LIBRARIES} ${OpenCV_LIBRARIES})然后返回工作空间~/catkin_ws执行catkin_make编译工程。 编译完成后，执行
source devel/setup.bash先打开一个终端运行roscore，用以节点之间的通信交互。 再打开一个终端运行rosrun usbcam usbcam_node 再打开一个终端运行rosrun opencvtest opencv_testcam 之后即可看到opencv处理后摄像头的图像。
