Slam learning notes - build a complete gazebo multi machine simulation slam from scratch (I)

introduction

This is a relatively complete tutorial . The article will be divided into four parts ：

• build gazebo Simulation environment , And run in a virtual environment slam Algorithm , Save the environment map
• Use map_merge, Multiple robots at the same time slam Drawing
• Use map_server Open the saved map , And make acml Positioning and move_base Perform autonomous navigation
• Use explore_lite The function package realizes the autonomous navigation of the robot , Edge construction map
  PS： The article does not include the installation of the corresponding function package , The environment used is melodic, If there is a version inconsistency , Minor modifications may be required
  gitee Address

The first part build gazebo Simulation environment

Use gazebo Of building_editor Build a map

• open building_editor
  
• Click on the left Wall, Draw a two-dimensional map on the whiteboard , It will automatically generate a three-dimensional map . If you want to build more complex , For example, a map with a slope , You can also use 3D modeling software to design , Import to gazebo I won't seriously introduce it here
  
• sign out editor And click the save, It is recommended to drag the 3D model , Make the coordinate origin in the map , Otherwise, it will be inconvenient to set the robot birth point later
  
• Finally exit gazebo And save the map as xxx.world file , If you use a virtual machine , You will encounter the situation that the save interface is stuck , At this time, minimizing the window and opening it again can solve the problem .

Use launch File loading world file

• First create the function package , I call it " gazebo_tutorials
• Add two folders launch and world, Save what you just saved world The file is kept in world In the folder
• establish launch file , I call it create_world.launch

<launch>
  <include file="$(find gazebo_ros)/launch/empty_world.launch">
     <arg name="world_name" value="$(find gazebo_tutorials)/world/maze.world"/>
    <arg name="paused" value="false"/>
    <arg name="use_sim_time" value="true"/>
    <arg name="gui" value="true"/>
    <arg name="headless" value="false"/>
    <arg name="debug" value="false"/>
  </include>  
</launch>

catkin_make Compile the , Use command roslaunch gazebo_tutorials create_world.launch You can start the simulation environment

The second part Put your robot

You can model your robot , export urdf Model , But for the sake of generality , We use it directly here turtlebot3 Model of .
Use turtlebot I referred to /opt/ros/melodic/share/turtlebot3_gazebo Files in the folder , It contains sample files of the distribution package , You search online gazebo Simulation allows you to run these files directly , Learn to read these documents by yourself , imitation , Then you can use it in your own projects .

• Write a name for place_robot.launch File placement robot ,TURTLEBOT3_MODEL You set it up by yourself turtlebot species , You can use export TURTLEBOT3_MODEL=buger Set the robot type as buger, Corresponding burger Model of

<launch>
    <!--  Robot initialization position parameters  -->
  <arg name="model" default="$(env TURTLEBOT3_MODEL)" doc="model type [burger, waffle, waffle_pi]"/>
  <arg name="robot_name" default="tb3_0"/>
  <arg name="robot_x_pos" default="0.0"/>
  <arg name="robot_y_pos" default="0.0"/>
  <arg name="robot_z_pos" default=" 0.0"/>
  <arg name="robot_yaw" default=" 0.0"/>
  <!--  Create robots  -->
  <group ns = "$(arg robot_name)">
    <!--  Import robot parameters  -->
    <param name="robot_description" command="$(find xacro)/xacro --inorder $(find turtlebot3_description)/urdf/turtlebot3_$(arg model).urdf.xacro" />
    <!--  Robot position publishing node  -->
    <node pkg="robot_state_publisher" type="robot_state_publisher" name="robot_state_publisher" output="screen">
      <param name="publish_frequency" type="double" value="50.0" />
      <param name="tf_prefix" value="$(arg robot_name)" />
    </node>
    <node name="spawn_urdf" pkg="gazebo_ros" type="spawn_model" args="-urdf -model $(arg robot_name) -x $(arg robot_x_pos) -y $(arg robot_y_pos) -z $(arg robot_z_pos) -Y $(arg robot_yaw) -param robot_description" />
  </group>    

</launch>

roslaunch gazebo_tutorials place_robot.launch You can see that the robot was born at the origin

Use rostopic list、rqt_graph、rosrun rqt_tf_tree rqt_tf_tree Wait for the order to observe , Later debugging can often use these things for observation



in addition , We can use roslaunch gazebo_tutials place_robot.launch robot_name:=tb3_1 robot_x_pos:=1.0 Command to create a second in a different location 、 Three robots , We'll do these things until the last article , Now keep a robot .

The third part start-up SLAM

I configured gmapping and cartorgrapher Algorithm configuration file , Only the most commonly used gmapping Algorithm configuration file , See the terminal start printing data , That is, it is opened successfully .

<launch>
  <arg name="ns" default="tb3_0"/>
  <!-- Gmapping -->
  <node pkg="gmapping" type="slam_gmapping" name="turtlebot3_slam_gmapping" output="screen" ns="$(arg ns)">
    <param name="base_frame" value="$(arg ns)/base_footprint"/>
    <param name="odom_frame" value="$(arg ns)/odom"/>
    <param name="map_frame" value="$(arg ns)/map"/>
    <param name="map_update_interval" value="2.0"/>
    <param name="maxUrange" value="4.0"/>
    <param name="minimumScore" value="100"/>
    <param name="linearUpdate" value="0.2"/>
    <param name="angularUpdate" value="0.2"/>
    <param name="temporalUpdate" value="0.5"/>
    <param name="delta" value="0.05"/>
    <param name="lskip" value="0"/>
    <param name="particles" value="120"/>
    <param name="sigma" value="0.05"/>
    <param name="kernelSize" value="1"/>
    <param name="lstep" value="0.05"/>
    <param name="astep" value="0.05"/>
    <param name="iterations" value="5"/>
    <param name="lsigma" value="0.075"/>
    <param name="ogain" value="3.0"/>
    <param name="srr" value="0.01"/>
    <param name="srt" value="0.02"/>
    <param name="str" value="0.01"/>
    <param name="stt" value="0.02"/>
    <param name="resampleThreshold" value="0.5"/>
    <param name="xmin" value="-10.0"/>
    <param name="ymin" value="-10.0"/>
    <param name="xmax" value="10.0"/>
    <param name="ymax" value="10.0"/>
    <param name="llsamplerange" value="0.01"/>
    <param name="llsamplestep" value="0.01"/>
    <param name="lasamplerange" value="0.005"/>
    <param name="lasamplestep" value="0.005"/>
  </node>
</launch>

Run at terminal rviz, Click on the bottom left corner add, stay by topic Find map Add in , You can see the built map ,rviz You can also save the configuration , Don't save it here , I'll talk more about it later when there are more robots , Including the display of coordinates 、 Selection of coordinate system, etc .
fixed_frame Choose as tb3_0/map, Set the world coordinate system as the map coordinate

We can see , yes gazebo This environment directly gives gmapping Nodes provide scan This radar data topic

The fourth part Use keyboard control to build the map and save

Keyboard controlled robot

Use command ROS_NAMESPACE=tb3_0 rosrun teleop_twist_keyboard teleop_twist_keyboard.py that will do , Those without this control package can install one , Follow the instructions , If the robot movement is different from what you expect , You can slow down the robot and try again .

Save the map

Use the command to ROS_NAMESPACE=tb3_0 rosrun map_server map_saver -f ~/catkin_ws/src/gazebo_tutorials/map/map Save the built map

There are two files saved , One is pgm file , Is the map built , It's a 0-255 The gray image . the other one yaml The file describes the map ,resolution=0.05 Represents an image primed case 0.05 rice ,origin Represents the coordinates represented by the pixels at the bottom left of the picture .
such , Our first lesson is finished .