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ROS里面有一个非常好用的AR标签包，可以产生AR标签，识别AR标签。我们可以基于此功能实现很多好玩的AR应用，这篇文章中我们将介绍如何使用这个包，及基于此包我们实现AR标签的跟随。
1.安装ar_track_alvar
ar_track_alvar可以通过apt_get来安装，非常方便：

sudo apt-get install ros-kinetic-ar-track-alvar

安装完成后暨在/opt/ros/kinetic/share/目录下有ar_track_alvar的目录，而且在launch子目录下已经有了launch文件。

如果你ROS一开始就是安装的完整版本的话，这个包就已经安装好了，不需要单独安装。
2.生成ar tag标签
ar_track_alvar提供的ar标签的生成功能，我们首先进入存放标签文件的目录

cd ~/diego1/src/diego_ar_tags/data

接下来执行执行生成标签的命令，就会在此文件夹下生成对应的标签，一条命令只生成一个标签

rosrun ar_track_alvar createMarker 0
rosrun ar_track_alvar createMarker 1
rosrun ar_track_alvar createMarker 2
rosrun ar_track_alvar createMarker 3
rosrun ar_track_alvar createMarker 4

这时候我们查看目录就会看到生成的ar文件，是png格式的图片

双击就可以打开文件

可以把标签答应出来，在跟随的时候使用。
3.ar标签跟随
3.1 源代码
下面是ar_follower.py的源文件，是从rbx2的项目中提取出来的，可以直接使用

#!/usr/bin/env python"""ar_follower.py - Version 1.0 2013-08-25Follow an AR tag published on the /ar_pose_marker topic.  The /ar_pose_marker topicis published by the ar_track_alvar packageCreated for the Pi Robot Project: http://www.pirobot.orgCopyright (c) 2013 Patrick Goebel.  All rights reserved.This program is free software; you can redistribute it and/or modifyit under the terms of the GNU General Public License as published bythe Free Software Foundation; either version 2 of the License, or(at your option) any later version.This program is distributed in the hope that it will be useful,but WITHOUT ANY WARRANTY; without even the implied warranty ofMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See theGNU General Public License for more details at:http://www.gnu.org/licenses/gpl.html
"""import rospy
from ar_track_alvar_msgs.msg import AlvarMarkers
from geometry_msgs.msg import Twist
from math import copysignclass ARFollower():def __init__(self):rospy.init_node("ar_follower")# Set the shutdown function (stop the robot)rospy.on_shutdown(self.shutdown)# How often should we update the robot's motion?self.rate = rospy.get_param("~rate", 10)r = rospy.Rate(self.rate) # The maximum rotation speed in radians per secondself.max_angular_speed = rospy.get_param("~max_angular_speed", 2.0)# The minimum rotation speed in radians per secondself.min_angular_speed = rospy.get_param("~min_angular_speed", 0.5)# The maximum distance a target can be from the robot for us to trackself.max_x = rospy.get_param("~max_x", 20.0)# The goal distance (in meters) to keep between the robot and the markerself.goal_x = rospy.get_param("~goal_x", 0.6)# How far away from the goal distance (in meters) before the robot reactsself.x_threshold = rospy.get_param("~x_threshold", 0.05)# How far away from being centered (y displacement) on the AR marker# before the robot reacts (units are meters)self.y_threshold = rospy.get_param("~y_threshold", 0.05)# How much do we weight the goal distance (x) when making a movementself.x_scale = rospy.get_param("~x_scale", 0.5)# How much do we weight y-displacement when making a movement        self.y_scale = rospy.get_param("~y_scale", 1.0)# The max linear speed in meters per secondself.max_linear_speed = rospy.get_param("~max_linear_speed", 0.3)# The minimum linear speed in meters per secondself.min_linear_speed = rospy.get_param("~min_linear_speed", 0.1)# Publisher to control the robot's movementself.cmd_vel_pub = rospy.Publisher('cmd_vel', Twist, queue_size=5)# Intialize the movement commandself.move_cmd = Twist()# Set flag to indicate when the AR marker is visibleself.target_visible = False# Wait for the ar_pose_marker topic to become availablerospy.loginfo("Waiting for ar_pose_marker topic...")rospy.wait_for_message('ar_pose_marker', AlvarMarkers)# Subscribe to the ar_pose_marker topic to get the image width and heightrospy.Subscriber('ar_pose_marker', AlvarMarkers, self.set_cmd_vel)rospy.loginfo("Marker messages detected. Starting follower...")# Begin the cmd_vel publishing loopwhile not rospy.is_shutdown():# Send the Twist command to the robotself.cmd_vel_pub.publish(self.move_cmd)# Sleep for 1/self.rate secondsr.sleep()def set_cmd_vel(self, msg):# Pick off the first marker (in case there is more than one)try:marker = msg.markers[0]if not self.target_visible:rospy.loginfo("FOLLOWER is Tracking Target!")self.target_visible = Trueexcept:# If target is loar, stop the robot by slowing it incrementallyself.move_cmd.linear.x /= 1.5self.move_cmd.angular.z /= 1.5if self.target_visible:rospy.loginfo("FOLLOWER LOST Target!")self.target_visible = Falsereturn# Get the displacement of the marker relative to the basetarget_offset_y = marker.pose.pose.position.yrospy.loginfo("target_offset_y"+str(target_offset_y))# Get the distance of the marker from the basetarget_offset_x = marker.pose.pose.position.xrospy.loginfo("target_offset_x"+str(target_offset_x))# Rotate the robot only if the displacement of the target exceeds the thresholdif abs(target_offset_y) > self.y_threshold:# Set the rotation speed proportional to the displacement of the targetspeed = target_offset_y * self.y_scaleself.move_cmd.angular.z = copysign(max(self.min_angular_speed,min(self.max_angular_speed, abs(speed))), speed)else:self.move_cmd.angular.z = 0.0# Now get the linear speedif abs(target_offset_x - self.goal_x) > self.x_threshold:speed = (target_offset_x - self.goal_x) * self.x_scaleif speed < 0:speed *= 1.5self.move_cmd.linear.x = copysign(min(self.max_linear_speed, max(self.min_linear_speed, abs(speed))), speed)else:self.move_cmd.linear.x = 0.0def shutdown(self):rospy.loginfo("Stopping the robot...")self.cmd_vel_pub.publish(Twist())rospy.sleep(1)     if __name__ == '__main__':try:ARFollower()rospy.spin()except rospy.ROSInterruptException:rospy.loginfo("AR follower node terminated.")

3.2代码解释
这段代码的原理其实非常简单，就是调用ar_track_alvar发布的/ar_pose_marker主题，然后将该主题的信息转换为cmd_vel主题的控制信息来控制机器人运动。我们先来看下/ar_pose_maker主题

上图执行rostopic echo /ar_pose_marker命令的截图，从中可以看出此topic中包含着一个Pose类型的消息，我们只需要处理position部分的x，y，z的值就可以了，其中x就是机器人到ar 标签的距离，y值就是相对于与摄像头中心位置的偏移，理解了ar_pose_marker我们就很容易理解代码的原理，这里就不多做叙述了。
3.3 launch文件
ar_large_markers_xition.launch文件启动ar_track_alvar node

<launch><arg name="marker_size" default="12.5" /> <arg name="max_new_marker_error" default="0.08" /><arg name="max_track_error" default="0.4" /><arg name="cam_image_topic" default="/camera/depth_registered/points" /><arg name="cam_info_topic" default="/camera/rgb/camera_info" /> <arg name="output_frame" default="/base_link" /><arg name="debug" default="false" /><arg if="$(arg debug)" name="launch_prefix" value="xterm -e gdb --args" /><arg unless="$(arg debug)" name="launch_prefix" value="" /><node pkg="tf" type="static_transform_publisher" name="base_link_2_camera_link" args="0.0 0.0 0.2 0 0 0 /base_link /camera_link 40"/> <node name="ar_track_alvar" pkg="ar_track_alvar" type="individualMarkers" respawn="false" output="screen" args="$(arg marker_size) $(arg max_new_marker_error) $(arg max_track_error) $(arg cam_image_topic) $(arg cam_info_topic) $(arg output_frame)" launch-prefix="$(arg launch_prefix)" /> <node name="ar_track_alvar" pkg="ar_track_alvar" type="individualMarkers" respawn="false" output="screen" args="$(arg marker_size) $(arg max_new_marker_error) $(arg max_track_error) $(arg cam_image_topic) $(arg cam_info_topic) $(arg output_frame)" launch-prefix="$(arg launch_prefix)" />
</launch>

diego_ar_follower.launch 文件启动 diego_ar_follower node，跟随的参数可以在这个文件中设置。

<launch><node name="arduino" pkg="ros_arduino_python" type="arduino_node.py" output="screen"><rosparam file="$(find ros_arduino_python)/config/my_arduino_params.yaml" command="load" /></node><node pkg="diego_ar_tags" name="ar_follower" type="ar_follower.py" clear_params="true" output="screen"><rosparam>rate: 10max_x: 20.0goal_x: 0.7x_threshold: 0.1y_threshold: 0.05y_scale: 2.0x_scale: 1.0max_angular_speed: 1.0min_angular_speed: 0.2max_linear_speed: 0.2min_linear_speed: 0.05</rosparam></node>
</launch>

4.启动节点

启动摄像头，在diego中使用xtion深度相机，所以在这里我们首先启动openni

roslaunch diego_vision openni_node.launch

启动ar_track_alvar节点

roslaunch diego_ar_tags ar_large_markers_xition.launch

启动diego_ar_follower节点

roslaunch diego_ar_tags diego_ar_follower.launch

现在只需要拿着我们事先打印好的ar tag在Xtion相机前移动，机器人就会跟着你走。