如何使用代码发布导航需要的传感器信息

在导航过程中，传感器的信息至关重要，这些传感器可以是激光雷达、摄像机、声纳、红外线、碰撞开关，但是归根结底，导航功能包要求机器人必须发布sensor_msgs/LaserScan或sensor_msgs/PointCloud格式的传感器信息，本篇将详细介绍如何使用代码发布所需要的消息。

1、ROS的消息头信息
无论是 sensor_msgs/LaserScan，还是sensor_msgs/PointCloud ，都和ROS中tf帧信息等时间相关的消息一样，带标准格式的头信息。
#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、如何发布激光扫描消息
2.1、激光消息的结构

针对激光雷达，ROS在sensor_msgs 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
#include

int main(int argc, char** argv){
ros::init(argc, argv, "laser_scan_publisher");

ros::NodeHandle n;
ros::Publisher scan_pub = n.advertise("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

首先我们需要先包含Laserscan的数据结构。
ros::Publisher scan_pub = n.advertise("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、如何发布点云数据
3.1、点云消息的结构
为存储和发布点云消息，ROS定义了sensor_msgs/PointCloud消息结构：

#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
#include

int main(int argc, char** argv){
ros::init(argc, argv, "point_cloud_publisher");

ros::NodeHandle n;
ros::Publisher cloud_pub = n.advertise("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

首先也是要包含sensor_msgs/PointCloud消息结构。
ros::Publisher cloud_pub = n.advertise("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。