您的当前位置：首页正文

Ros 消息结构1

2024-11-09 来源：个人技术集锦

1、ROS的消息头信息

#Standard metadata for higher-level flow data types
#sequence ID: consecutively increasing ID 
uint32 seq
 
#Two-integer timestamp that is expressed as:
# * stamp.secs: seconds (stamp_secs) since epoch
# * stamp.nsecs: nanoseconds since stamp_secs
# time-handling sugar is provided by the client library
time stamp
 
#Frame this data is associated with
# 0: no frame
# 1: global frame
string frame_id

以上是标准头信息的主要部分。seq是消息的顺序标识，不需要手动设置，发布节点在发布消息时，会自动累加。stamp 是消息中与数据相关联的时间戳，例如激光数据中，时间戳对应激光数据的采集时间点。frame_id 是消息中与数据相关联的参考系id，例如在在激光数据中，frame_id对应激光数据采集的参考系。

2.1、激光消息的结构

针对激光雷达，ROS在包中定义了专用了数据结构来存储激光消息的相关信息，成为LaserScan。LaserScan消息的格式化定义，为虚拟的激光雷达数据采集提供了方便，在我们讨论如何使用他之前，来看看该消息的结构是什么样的：

#
# Laser scans angles are measured counter clockwise, with 0 facing forward
# (along the x-axis) of the device frame
#
 
Header header
float32 angle_min        # start angle of the scan [rad]
float32 angle_max        # end angle of the scan [rad]
float32 angle_increment  # angular distance between measurements [rad]
float32 time_increment   # time between measurements [seconds]
float32 scan_time        # time between scans [seconds]
float32 range_min        # minimum range value [m]
float32 range_max        # maximum range value [m]
float32[] ranges         # range data [m] (Note: values < range_min or > range_max should be discarded)
float32[] intensities    # intensity data [device-specific units]

备注中已经详细介绍了每个参数的意义。

2.2、通过代码发布LaserScan消息

使用ROS发布LaserScan格式的激光消息非常简洁，下边是一个简单的例程：

#include <ros/ros.h>
#include <sensor_msgs/LaserScan.h>
 
int main(int argc, char** argv){
  ros::init(argc, argv, "laser_scan_publisher");
 
  ros::NodeHandle n;
  ros::Publisher scan_pub = n.advertise<sensor_msgs::LaserScan>("scan", 50);
 
  unsigned int num_readings = 100;
  double laser_frequency = 40;
  double ranges[num_readings];
  double intensities[num_readings];
 
  int count = 0;
  ros::Rate r(1.0);
  while(n.ok()){
    //generate some fake data for our laser scan
    for(unsigned int i = 0; i < num_readings; ++i){
      ranges[i] = count;
      intensities[i] = 100 + count;
    }
    ros::Time scan_time = ros::Time::now();
 
    //populate the LaserScan message
    sensor_msgs::LaserScan scan;
    scan.header.stamp = scan_time;
    scan.header.frame_id = "laser_frame";
    scan.angle_min = -1.57;
    scan.angle_max = 1.57;
    scan.angle_increment = 3.14 / num_readings;
    scan.time_increment = (1 / laser_frequency) / (num_readings);
    scan.range_min = 0.0;
    scan.range_max = 100.0;
 
    scan.ranges.resize(num_readings);
    scan.intensities.resize(num_readings);
    for(unsigned int i = 0; i < num_readings; ++i){
      scan.ranges[i] = ranges[i];
      scan.intensities[i] = intensities[i];
    }
 
    scan_pub.publish(scan);
    ++count;
    r.sleep();
  }
}

我们将代码分解以便于分析：

#include <sensor_msgs/LaserScan.h>

首先我们需要先包含Laserscan的数据结构。

ros::Publisher scan_pub = n.advertise<sensor_msgs::LaserScan>("scan", 50);

创建一个发布者，以便于后边发布针对scan主题的Laserscan消息。

  unsigned int num_readings = 100;
  double laser_frequency = 40;
  double ranges[num_readings];
  double intensities[num_readings];
 
  int count = 0;
  ros::Rate r(1.0);
  while(n.ok()){
    //generate some fake data for our laser scan
    for(unsigned int i = 0; i < num_readings; ++i){
      ranges[i] = count;
      intensities[i] = 100 + count;
    }
    ros::Time scan_time = ros::Time::now();

这里的例程中我们虚拟一些激光雷达的数据，如果使用真是的激光雷达，这部分数据需要从驱动中获取。

    //populate the LaserScan message
    sensor_msgs::LaserScan scan;
    scan.header.stamp = scan_time;
    scan.header.frame_id = "laser_frame";
    scan.angle_min = -1.57;
    scan.angle_max = 1.57;
    scan.angle_increment = 3.14 / num_readings;
    scan.time_increment = (1 / laser_frequency) / (num_readings);
    scan.range_min = 0.0;
    scan.range_max = 100.0;
 
    scan.ranges.resize(num_readings);
    scan.intensities.resize(num_readings);
    for(unsigned int i = 0; i < num_readings; ++i){
      scan.ranges[i] = ranges[i];
      scan.intensities[i] = intensities[i];
    }

创建scan_msgs::LaserScan数据类型的变量scan，把我们之前伪造的数据填入格式化的消息结构中。

scan_pub.publish(scan);

数据填充完毕后，通过前边定义的发布者，将数据发布。

3.1、点云消息的结构

#This message holds a collection of 3d points, plus optional additional information about each point.
 
#Each Point32 should be interpreted as a 3d point in the frame given in the header
 
  
Header header
 
geometry_msgs/Point32[] points  #Array of 3d points
 
ChannelFloat32[] channels       #Each channel should have the same number of elements as points array, and the data in each channel should correspond 1:1 with each point

如上所示，点云消息的结构支持存储三维环境的点阵列，而且channels参数中，可以设置这些点云相关的数据，例如可以设置一个强度通道，存储每个点的数据强度，还可以设置一个系数通道，存储每个点的反射系数，等等。

3.2、通过代码发布点云数据

ROS发布点云数据同样简洁：

#include <ros/ros.h>
#include <sensor_msgs/PointCloud.h>
 
int main(int argc, char** argv){
  ros::init(argc, argv, "point_cloud_publisher");
 
  ros::NodeHandle n;
  ros::Publisher cloud_pub = n.advertise<sensor_msgs::PointCloud>("cloud", 50);
 
  unsigned int num_points = 100;
 
  int count = 0;
  ros::Rate r(1.0);
  while(n.ok()){
    sensor_msgs::PointCloud cloud;
    cloud.header.stamp = ros::Time::now();
    cloud.header.frame_id = "sensor_frame";
 
    cloud.points.resize(num_points);
 
    //we'll also add an intensity channel to the cloud
    cloud.channels.resize(1);
    cloud.channels[0].name = "intensities";
    cloud.channels[0].values.resize(num_points);
 
    //generate some fake data for our point cloud
    for(unsigned int i = 0; i < num_points; ++i){
      cloud.points[i].x = 1 + count;
      cloud.points[i].y = 2 + count;
      cloud.points[i].z = 3 + count;
      cloud.channels[0].values[i] = 100 + count;
    }
 
    cloud_pub.publish(cloud);
    ++count;
    r.sleep();
  }
}

分解代码来分析：

#include <sensor_msgs/PointCloud.h>

首先也是要包含sensor_msgs/PointCloud消息结构。

ros::Publisher cloud_pub = n.advertise<sensor_msgs::PointCloud>("cloud", 50);

定义一个发布点云消息的发布者。

sensor_msgs::PointCloud cloud;
cloud.header.stamp = ros::Time::now();
cloud.header.frame_id = "sensor_frame";

为点云消息填充头信息，包括时间戳和相关的参考系id。

cloud.points.resize(num_points);

设置存储点云数据的空间大小。

 //we'll also add an intensity channel to the cloud
cloud.channels.resize(1);
cloud.channels[0].name = "intensities";
cloud.channels[0].values.resize(num_points);

设置一个名为“intensity“的强度通道，并且设置存储每个点强度信息的空间大小。

 //generate some fake data for our point cloud
    for(unsigned int i = 0; i < num_points; ++i){
      cloud.points[i].x = 1 + count;
      cloud.points[i].y = 2 + count;
      cloud.points[i].z = 3 + count;
      cloud.channels[0].values[i] = 100 + count;
    }

将我们伪造的数据填充到点云消息结构当中。

cloud_pub.publish(cloud);

最后，发布点云数据。