github地址：https://github.com/robotBing/openrobot_follower.git
视频展示地址：http://blog.jiutucao.com:9000/upload/2018/07/color_follow.mp4
1. 基本功能
1）跟随人行走
2）语音控制开始或停止
3）颜色标定
2. 跟随实现
1）代码:
#!/usr/bin/env python
import rospy
from roslib import message
from std_msgs.msg import String
from sensor_msgs import point_cloud2
from sensor_msgs.msg import PointCloud2
from geometry_msgs.msg import Twist
from math import copysign
class Follower():
int status = 0
def __init__(self):
rospy.init_node("follower")
rospy.on_shutdown(self.shutdown)
self.goal_z = rospy.get_param("~goal_z", 0.6)
self.z_threshold = rospy.get_param("~z_threshold", 0.05)
self.x_threshold = rospy.get_param("~x_threshold", 0.05)
self.z_scale = rospy.get_param("~z_scale", 1.0)
self.x_scale = rospy.get_param("~x_scale", 2.5)
self.max_angular_speed = rospy.get_param("~max_angular_speed", 2.0)
self.min_angular_speed = rospy.get_param("~min_angular_speed", 0.0)
self.max_linear_speed = rospy.get_param("~max_linear_speed", 0.3)
self.min_linear_speed = rospy.get_param("~min_linear_speed", 0.1
self.low_down_factor = rospy.get_param("~slow_down_factor", 0.8)
self.move_cmd = Twist()
self.cmd_vel_pub = rospy.Publisher('/cmd_vel', Twist, queue_size=5)
self.depth_subscriber = rospy.Subscriber('point_cloud', PointCloud2, self.set_cmd_vel, queue_size=1)
rospy.Subscriber('/recognizer/output', String, self.speechcb)
rospy.loginfo("Subscribing to point cloud...")
rospy.wait_for_message('point_cloud', PointCloud2)
rospy.loginfo("Ready to follow!"
def speechcb(self,msg):
command = msg.data
if command == 'stop':
self.status = 0
else if command == 'move' :
self.status = 1
def set_cmd_vel(self, msg):
x = y = z = n = 0
for point in point_cloud2.read_points(msg, skip_nans=True):
pt_x = point[0]
pt_y = point[1]
pt_z = point[2]
x += pt_x
y += pt_y
z += pt_z
n += 1
if n:
x /= n
y /= n
z /= n
if (abs(z - self.goal_z) > self.z_threshold):
linear_speed = (z - self.goal_z) * self.z_scale
self.move_cmd.linear.x = copysign(max(self.min_line
ar_speed,
min(self.max_linear_speed, abs(linear_speed))), linear_speed)
else:
self.move_cmd.linear.x *= self.slow_down_factor
if (abs(x) > self.x_threshold):
angular_speed = -x * self.x_scale
self.move_cmd.angular.z = copysign(max(self.min_angular_speed,
min(self.max_angular_speed, abs(angular_speed))), angular_speed)
else:
self.move_cmd.angular.z *= self.slow_down_factor
else:
self.move_cmd.linear.x *= self.slow_down_factor
self.move_cmd.angular.z *= self.slow_down_factor
if self.status == 0:
self.cmd_vel_pub.publish(twist())
else:
self.cmd_vel_pub.publish(self.move_cmd)
def shutdown(self):
rospy.loginfo("Stopping the robot...")
self.depth_subscriber.unregister()
rospy.sleep(1)
self.cmd_vel_pub.publish(Twist())
rospy.sleep(1)
if __name__ == '__main__':
try:
Follower()
rospy.spin()
except rospy.ROSInterruptException:
rospy.loginfo("Follower node terminated.")
2）基本思路
订阅点云（pointcloud2）话题，点云数据包括图像的宽度，高度和一个个点的信息。
根据点云数据找出人的质心点，然后将质心点和图像的中间点和设定的距离点比较，
左右偏移则往/cmd_vel话题上发布左右消息，前后则发布前后消息
3） launch文件
<launch>
<node pkg="nodelet" type="nodelet" name="pcl_manager" args="manager" output="screen" />
<node pkg="nodelet" type="nodelet" name="voxel_grid_z" args="load pcl/VoxelGrid pcl_manager" output="screen">
<remap from="~input" to="/camera/depth_registered/points" />
<remap from="~output" to="/z_filtered" />
<rosparam>
filter_field_name: z
filter_limit_min: 0.3
filter_limit_max: 1.6
filter_limit_negative: False
leaf_size: 0.02
</rosparam>
</node>
<node pkg="nodelet" type="nodelet" name="passthrough_x" args="load pcl/PassThrough pcl_manager" output="screen">
<remap from="~input" to="/z_filtered" />
<remap from="~output" to="/x_filtered" />
<rosparam>
filter_field_name: x
filter_limit_min: -0.3
filter_limit_max: 0.3
filter_limit_negative: False
</rosparam>
</node>
<node pkg="nodelet" type="nodelet" name="passthrough_y" args="load pcl/PassThrough pcl_manager" output="screen">
<remap from="~input" to="/x_filtered" />
<remap from="~output" to="/search_cloud" />
<rosparam>
filter_field_name: y
filter_limit_min: -0.5
filter_limit_max: -0.1
filter_limit_negative: False
</rosparam>
</node>
<node pkg="openrobot_follow" name="follower" type="follower2.py" output="screen">
<remap from="point_cloud" to="search_cloud" />
<rosparam>
goal_z: 0.8
z_threshold: 0.025
x_threshold: 0.025
z_scale: 1.0
x_scale: 3.0
max_angular_speed: 5.0
min_angular_speed: 0.1
max_linear_speed: 0.4
min_linear_speed: 0.05
</rosparam>
</node>
</launch>
4）launch详解
nodelet是ros自带的节点管理系统，类似于java里的多线程
利用VoxelGrid算法，对pointcloud的深度信息过滤
然后利用passthrough算法，对x ，y坐标过滤
过滤可以降低计算量，又不会影响follow结果
最后启动follow节点
remap表示话题的映射，相当于给话题改名
5）参数详解
filter_field_name: 选择过滤的坐标系（x，y，z）
filter_limit_min: 最短的过滤信息，即最短的检测距离
filter_limit_max: 最大的过滤信息，即最大的检测距离
leaf_size: 算法切块的大小
goal_z: 距离目标的距离
z_threshold: 深度偏差量
x_threshold: x方向偏差量，即允许人在图像的哪个范围
z_scale: 距离权重，控制线速度
x_scale: x方向的权重，控制角速度
max_angular_speed: 最大角速度
min_angular_speed: 最小角速度
max_linear_speed: 最大线速度
min_linear_speed: 最小线速度
3. 语音实现
1）概述
利用ros自带的语音识别包pocketsphinx，订阅/input/recognizer话题，获得识别结果，然后给follow函数设置一个使能变量，在语音的回调函数里对使能变量做操作
2）环境配置
sudo apt-get install ros-indigo-turtlebot-bringup \
ros-indigo-turtlebot-create-desktop ros-indigo-openni-* \
ros-indigo-openni2-* ros-indigo-freenect-* ros-indigo-usb-cam \
ros-indigo-laser-* ros-indigo-hokuyo-node \
ros-indigo-audio-common gstreamer0.10-pocketsphinx \
ros-indigo-pocketsphinx ros-indigo-slam-gmapping \
ros-indigo-joystick-drivers python-rosinstall \
ros-indigo-orocos-kdl ros-indigo-python-orocos-kdl \
python-setuptools ros-indigo-dynamixel-motor-* \
libopencv-dev python-opencv ros-indigo-vision-opencv \
ros-indigo-depthimage-to-laserscan ros-indigo-arbotix-* \
ros-indigo-turtlebot-teleop ros-indigo-move-base \
ros-indigo-map-server ros-indigo-fake-localization \
ros-indigo-amcl git subversion mercurial
3）代码重写
#!/usr/bin/env python
import rospy
from roslib import message
from sensor_msgs import point_cloud2
from sensor_msgs.msg import PointCloud2
from geometry_msgs.msg import Twist
from std_msgs.msg import String
from math import copysig
class Follower():
def __init__(self):
rospy.init_node("follower")
rospy.on_shutdown(self.shutdown)
self.goal_z = rospy.get_param("~goal_z", 1.5)
self.z_threshold = rospy.get_param("~z_threshold", 0.05)
self.x_threshold = rospy.get_param("~x_threshold", 0.05)
self.z_scale = rospy.get_param("~z_scale", 0.7)
self.x_scale = rospy.get_param("~x_scale", 2.2)
self.max_angular_speed = rospy.get_param("~max_angular_speed", 2.0)
self.min_angular_speed = rospy.get_param("~min_angular_speed", 0.0) self.max_linear_speed = rospy.get_param("~max_linear_speed", 0.3)
self.min_linear_speed = rospy.get_param("~min_linear_speed", 0.1)
self.slow_down_factor = rospy.get_param("~slow_down_factor", 0.8)
self.move_cmd = Twist(）
self.cmd_vel_pub = rospy.Publisher('/cmd_vel', Twist, queue_size=10)
self.sta = 1
self.speech_sub = rospy.Subscriber('recognizer/output',String,self.speechcb,queue_size=1)
# Subscribe to the point cloud
self.depth_subscriber = rospy.Subscriber('point_cloud', PointCloud2, self.set_cmd_vel, queue_size=10)
rospy.loginfo("Subscribing to point cloud...")
# Wait for the pointcloud topic to become available
rospy.wait_for_message('point_cloud', PointCloud2)
rospy.loginfo("Ready to follow!")
def speechcb(self,msg):
if msg.data == 'stop':
self.sta=0
elif msg.data == 'move':
self.sta=1
def set_cmd_vel(self, msg):
# Initialize the centroid coordinates point count
x = y = z = n = 0
# Read in the x, y, z coordinates of all points in the cloud
for point in point_cloud2.read_points(msg, skip_nans=True):
pt_x = point[0]
pt_y = point[1]
pt_z = point[2]
x += pt_x
y += pt_y
z += pt_z
n += 1
if n:
x /= n
y /= n
z /= n
if (abs(z - self.goal_z) > self.z_threshold):
linear_speed = (z - self.goal_z) * self.z_scale
self.move_cmd.linear.x = copysign(max(self.min_linear_speed,
min(self.max_linear_speed, abs(linear_speed))), linear_speed)
else:
self.move_cmd.linear.x *= self.slow_down_factor
if (abs(x) > self.x_threshold):
angular_speed = -x * self.x_scale
self.move_cmd.angular.z = copysign(max(self.min_angular_speed,
min(self.max_angular_speed, abs(angular_speed))), angular_speed)
else:
self.move_cmd.angular.z *= self.slow_down_factor
else:
self.move_cmd.linear.x *= self.slow_down_factor
self.move_cmd.angular.z *= self.slow_down_factor
if self.sta == 0:
self.cmd_vel_pub.publish(Twist())
else:
self.cmd_vel_pub.publish(self.move_cmd)
def shutdown(self):
rospy.loginfo("Stopping the robot...")
self.depth_subscriber.unregister()
rospy.sleep(1)
self.cmd_vel_pub.publish(Twist())
rospy.sleep(1)
if __name__ == '__main__':
try:
Follower()
rospy.spin()
except rospy.ROSInterruptException:
rospy.loginfo("Follower node terminated.")
4）Launch重写
<launch>
<node name="recognizer" pkg="pocketsphinx" type="recognizer.py" output="screen">
<param name="lm" value="$(find rbx1_speech)/config/nav_commands.lm"/>
<param name="dict" value="$(find rbx1_speech)/config/nav_commands.dic"/>
</node>
<node pkg="nodelet" type="nodelet" name="pcl_manager" args="manager" output="screen" />
<!-- Run a VoxelGrid filter on the z axis -->
<node pkg="nodelet" type="nodelet" name="voxel_grid_z" args="load pcl/VoxelGrid pcl_manager" output="screen">
<remap from="~input" to="/kinect2/qhd/points" />
<remap from="~output" to="/z_filtered" />
<rosparam>
filter_field_name: z
filter_limit_min: 0.3
filter_limit_max: 1.6
filter_limit_negative: False
leaf_size: 0.02
</rosparam>
</node>
<!-- Run a passthrough filter on the x axis -->
<node pkg="nodelet" type="nodelet" name="passthrough_x" args="load pcl/PassThrough pcl_manager" output="screen">
<remap from="~input" to="/z_filtered" />
<remap from="~output" to="/x_filtered" />
<rosparam>
filter_field_name: x
filter_limit_min: -0.3
filter_limit_max: 0.3
filter_limit_negative: False
</rosparam>
</node>
<!-- Run a passthrough filter on the y axis -->
<node pkg="nodelet" type="nodelet" name="passthrough_y" args="load pcl/PassThrough pcl_manager" output="screen">
<remap from="~input" to="/x_filtered" />
<remap from="~output" to="/search_cloud" />
<rosparam>
filter_field_name: y
filter_limit_min: -0.5
filter_limit_max: -0.1
filter_limit_negative: False
</rosparam>
</node>
<node pkg="openrobot_follow" name="follower" type="follower2.py" output="screen">
<remap from="point_cloud" to="search_cloud" />
<rosparam>
goal_z: 1.2
z_threshold: 0.025
x_threshold: 0.025
z_scale: 0.6
x_scale: 2.1
max_angular_speed: 3.0
min_angular_speed: 0.1
max_linear_speed: 0.4
min_linear_speed: 0.05
</rosparam>
</node>
</launch>
4. 颜色标定实现
1 ) 概述
利用opencv提供的python接口，处理图像信息，利用cv_bridge将ros话题上发布的摄像头消息数据转化成为opencv可以处理的图像数据。
2 ）代码见github项目
3 ) launch
<launch>
<node name="recognizer" pkg="pocketsphinx" type="recognizer.py" output="screen">
<param name="lm" value="/home/tt/my_ws/src/openrobot_follow/speech_conf/command.lm"/>
<param name="dict" value="/home/tt/my_ws/src/openrobot_follow/speech_conf/command.dic"/>
</node>
<node pkg="openrobot_follow" name="color_follower" type="color_follower.py" output="screen">
<remap from="camera_info" to="/camera/rgb/camera_info" />
<remap from="depth_image" to="/camera/depth_registered/image_raw" />
<rosparam>
rate: 20
max_z: 2.0 # How far out do we want to detect
min_z: 0.1
goal_z: 0.7
z_threshold: 0.1
x_threshold: 0.3
z_scale: 1.0 # forward/back scale
x_scale: 1.3 # left/right scale
max_rotation_speed: 1.0
min_rotation_speed: 0.1
max_linear_speed: 0.2
min_linear_speed: 0.02
scale_roi: 0.9
</rosparam>
</node>
</launch>
4 ）launch详解
rate: 消息回调速度，一般不用动，如果出现卡顿再改动
max_z: 检测的最大距离
min_z: 检测的最小距离
goal_z: 保持机器和目标的距离
z_threshold: 距离的可控区域
x_threshold: 图像的可控区域，即在屏幕上画个矩形，让目标保持在矩形内
z_scale: 线速度的权值
x_scale: 角速度的权值
max_rotation_speed: 最大角速度
min_rotation_speed: 最小角速度
max_linear_speed: 最大线速度
min_linear_speed: 最小线速度
scale_roi: 对roi话题上发布的信息的缩小范围
1. 运行底盘驱动
rosrun openrobot Node_p_chassis
2 .打开摄像头驱动（实验室用的奥比中光深度摄像头）
roslaunch astra_launch astra.launch
3.启动camshift 发布/roi
roslaunch openrobot_follow camshift.launch
这时候屏幕上会出现3个窗口，在有图像的窗口选择需要跟随的物体。这时候程序会计算物体的颜色，另外两个窗口其中一个会出现颜色的柱状图，另一个窗口出现颜色柱状图。物体移动绿色的框会跟着移动。尽量选中颜色鲜艳的物体。4.启动跟随
roslaunch openrobot_follow color_follow.launch
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