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WARNING: Be careful when running the robot on the table as the robot might fall.


  • This instructions were tested on Ubuntu 16.04 and ROS Kinetic Kame and on Windows 10 with ROS Melodic Morenia
  • This instructions are supposed to be running on the remote PC. Please run the instructions below on your Remote PC.
  • The terminal application can be found with the Ubuntu search icon on the top left corner of the screen. The shortcut key for running the terminal is Ctrl-Alt-T.
  • Make sure to run the Bringup instruction before use of the instruction.


  • We are happy to announce a new ROS book: “ROS Robot Programming, A Handbook is written by TurtleBot3 Developers”. This book has been published in Korean, English, Chinese and Japanese. It contains the following:
    • ROS Kinetic Kame: Basic concept, instructions and tools
    • How to use sensor and actuator packages on ROS
    • Embedded board for ROS: OpenCR
    • SLAM & Navigation with TurtleBot3
    • How to program a delivery robot using ROS Java
    • OpenMANIPULATOR simulation using MoveIt! and Gazebo
  • Please refer to this book for more information on ROS, SLAM, and Navigation that are not covered in this e-manual. You can download the pdf of this book.

TIP: It is recommended to use a joystick pad instead of the keyboard for easier control. For more information on remote control, Please refer to Teleoperation page.

The SLAM (Simultaneous Localization and Mapping) is a technique to draw a map by estimating current location in an arbitrary space. The SLAM is a well-known feature of TurtleBot from its predecessors. The video here shows you how accurately TurtleBot3 can draw a map with its compact and affordable platform.

The contents in e-Manual can be updated without a previous notice. Therefore, some video may differ from the contents in e-Manual.

The contents in e-Manual can be updated without a previous notice. Therefore, some video may differ from the contents in e-Manual.

Run SLAM Nodes

[Remote PC] Run roscore.

$ roscore

[TurtleBot] Bring up basic packages to start TurtleBot3 applications.

$ roslaunch turtlebot3_bringup turtlebot3_robot.launch

[Remote PC] Open a new terminal and launch the SLAM file.

TIP: Before executing this command, you have to specify the model name of TurtleBot3. The ${TB3_MODEL} is the name of the model you are using in burger, waffle, waffle_pi. If you want to permanently set the export settings, please refer to Export TURTLEBOT3_MODEL page.

$ roslaunch turtlebot3_slam turtlebot3_slam.launch slam_methods:=gmapping

TIP: When running these commands on Windows 10, replace export TURTLEBOT3_MODEL=${TB3_MODEL} with set TURTLEBOT3_MODEL=${TB3_MODEL} like this:

> set TURTLEBOT3_MODEL=burger
> roslaunch turtlebot3_slam turtlebot3_slam.launch slam_methods:=gmapping

TIP: When you run the above command, the visualization tool RViz is also executed. If you want to run RViz separately, use one of the following commands.

  • $ rviz -d `rospack find turtlebot3_slam`/rviz/turtlebot3_gmapping.rviz
  • $ rviz -d `rospack find turtlebot3_slam`/rviz/turtlebot3_cartographer.rviz
  • $ rviz -d `rospack find turtlebot3_slam`/rviz/turtlebot3_hector.rviz
  • $ rviz -d `rospack find turtlebot3_slam`/rviz/turtlebot3_karto.rviz
  • $ rviz -d `rospack find turtlebot3_slam`/rviz/turtlebot3_frontier_exploration.rviz

NOTE: Support for various SLAM methods

  • TurtleBot3 supports Gmapping, Cartographer, Hector, and Karto among various SLAM methods. You can do this by changing the slam_methods:=xxxxx option.
  • The slam_methods options include gmapping, cartographer, hector, karto, frontier_exploration, and you can choose one of them.
  • For Windows 10, Google Cartographer has been enabled. OpenKarto is coming soon.
  • For example, to use Karto, you can use the following:
  • $ roslaunch turtlebot3_slam turtlebot3_slam.launch slam_methods:=karto

NOTE: Install dependency packages for SLAM packages

  • For Gmapping:
    • Packages related to Gmapping have already been installed on PC Setup page.
    • Gmapping has not been enabled on Windows
  • For Cartographer:
    • Ubuntu
      $ sudo apt-get install ros-kinetic-cartographer ros-kinetic-cartographer-ros ros-kinetic-cartographer-ros-msgs ros-kinetic-cartographer-rviz
    • Windows
      > choco upgrade ros-melodic-cartographer_ros -y
  • For Hector Mapping:
    • Hector Mapping has not been enabled on Windows
      $ sudo apt-get install ros-kinetic-hector-mapping
  • For Karto:
    • Coming soon on Windows
      $ sudo apt-get install ros-kinetic-slam-karto
  • For Frontier Exploration:
    • Frontier Exploration uses gmapping, and the following packages should be installed.
    • Frontier Exploration has not been enabled on Windows
      $ sudo apt-get install ros-kinetic-frontier-exploration ros-kinetic-navigation-stage

TIP: We tested on cartographer version 0.3.0. The Cartographer package developed by Google supports 0.3.0 version in ROS Melodic, but 0.2.0 version in ROS Kinetic. So if you need to work on ROS Kinetic, instead of downloading the binaries files, you should download and build the source code as follows. Please refer to official wiki page for more detailed installation instructions.

on Ubuntu

$ sudo apt-get install ninja-build libceres-dev libprotobuf-dev protobuf-compiler libprotoc-dev
$ cd ~/catkin_ws/src
$ git clone https://github.com/googlecartographer/cartographer.git
$ git clone https://github.com/googlecartographer/cartographer_ros.git
$ cd ~/catkin_ws
$ src/cartographer/scripts/install_proto3.sh
$ rm -rf protobuf/
$ rosdep install --from-paths src --ignore-src -r -y --os=ubuntu:xenial
$ catkin_make_isolated --install --use-ninja
$ source ~/catkin_ws/install_isolated/setup.bash
$ roslaunch turtlebot3_slam turtlebot3_slam.launch slam_methods:=cartographer


> c:\ws\turtlebot3\devel\setup.bat
> set TURTLEBOT3_MODEL=waffle
> roslaunch turtlebot3_gazebo turtlebot3_gazebo_cartographer_demo.launch

Run Teleoperation Node

[Remote PC] Open a new terminal and run the teleoperation node. The following command allows the user to control the robot to perform SLAM operation manually. It is important to avoid vigorous movements such as changing the speed too quickly or rotating too fast. When building a map using the robot, the robot should scan every corner of the environment to be measured. It requires some experiences to build a clean map, so let’s practice SLAM multiple times to build up know how. The mapping process is shown in figure below.


$ roslaunch turtlebot3_teleop turtlebot3_teleop_key.launch


> set TURTLEBOT3_MODEL=waffle
> roslaunch turtlebot3_teleop turtlebot3_teleop_key.launch
  Control Your TurtleBot3!
  Moving around:
     a    s    d

  w/x : increase/decrease linear velocity
  a/d : increase/decrease angular velocity
  space key, s : force stop

  CTRL-C to quit

Tuning Guide

Gmapping has many parameters to change performances for different environments. You can get an information about whole parameters in ROS WiKi or refer chapter 11 in ROS Robot Programming book.

This tuning guide give some tips for you to configue important parameters. If you want to change performances depends on your environments, this tips might be help you and save your time.






Save Map

[Remote PC] Now that you have all the work done, let’s run the map_saver node to create a map file. The map is drawn based on the robot’s odometry, tf information, and scan information of the sensor when the robot moves. These data can be seen in the RViz from the previous example video. The created map is saved in the directory in which map_saver is runnig. Unless you specify the file name, it is stored as map.pgm and map.yaml file which contains map information.

The -f option refers to the folder and file name where the map file is saved. If ~/map is used as an option, map.pgm and map.yaml will be saved in the map folder of user’s home folder ~/ ($HOME directory : /home/<username>). On Windows, the user directory is stored in an environment variable %USERPROFILE%

on Ubuntu

$ rosrun map_server map_saver -f ~/map

on Windows

> rosrun map_server map_saver -f %USERPROFILE%\map


We will use the two-dimensional Occupancy Grid Map (OGM), which is commonly used in the ROS community. The map obtained from the previous Save Map section as shown in figure below, white is the free area in which the robot can move, black is the occupied area in which the robot can not move, and gray is the unknown area. This map is used in Navigation.

The figure below shows the result of creating a large map using TurtleBot3. It took about an hour to create a map with a travel distance of about 350 meters.