SLAM

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 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 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 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 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 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 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.

1. Run roscore from Remote PC.

   $ roscore
   

2. If the Bringup is not running on the TurtleBot3 SBC, launch the Bringup. Skip this step if you have launched bringup previously.
   • Open a new terminal from Remote PC with Ctrl + Alt + T and connect to Raspberry Pi with its IP address. The default password is turtlebot.

     $ ssh pi@{IP_ADDRESS_OF_RASPBERRY_PI}
     $ roslaunch turtlebot3_bringup turtlebot3_robot.launch
     

3. Open a new terminal from Remote PC with Ctrl + Alt + T and launch the SLAM node. The Gmapping is used as a default SLAM method. Please use the proper keyword among burger, waffle, waffle_pi for the TURTLEBOT3_MODEL parameter.

   $ export TURTLEBOT3_MODEL=burger
   $ roslaunch turtlebot3_slam turtlebot3_slam.launch
   

1. Run roscore from Remote PC.

   $ roscore
   

2. If the Bringup is not running on the TurtleBot3 SBC, launch the Bringup. Skip this step if you have launched bringup previously.
   • Open a new terminal from Remote PC with Ctrl + Alt + T and connect to Raspberry Pi with its IP address. The default password is turtlebot.

     $ ssh pi@{IP_ADDRESS_OF_RASPBERRY_PI}
     $ roslaunch turtlebot3_bringup turtlebot3_robot.launch
     

3. Open a new terminal from Remote PC with Ctrl + Alt + T and launch the SLAM node. The Gmapping is used as a default SLAM method.

   $ export TURTLEBOT3_MODEL=${TB3_MODEL}
   $ roslaunch turtlebot3_slam turtlebot3_slam.launch
   

1. Run roscore from Remote PC.

   $ roscore
   

2. If the Bringup is not running on the TurtleBot3 SBC, launch the Bringup. Skip this step if you have launched bringup previously.
   • Open a new terminal from Remote PC with Ctrl + Alt + T and connect to Raspberry Pi with its IP address. The default password is turtlebot. Please use the proper keyword among burger, waffle, waffle_pi for the TURTLEBOT3_MODEL parameter.

     $ ssh pi@{IP_ADDRESS_OF_RASPBERRY_PI}
     $ export TURTLEBOT3_MODEL=${TB3_MODEL}
     $ roslaunch turtlebot3_bringup turtlebot3_robot.launch
     

3. Open a new terminal from Remote PC with Ctrl + Alt + T and launch the SLAM node. The Gmapping is used as a default SLAM method. Please use the proper keyword among burger, waffle, waffle_pi for the TURTLEBOT3_MODEL parameter.

   $ export TURTLEBOT3_MODEL=burger
   $ roslaunch turtlebot3_slam turtlebot3_slam.launch
   

1. If the Bringup is not running on the TurtleBot3 SBC, launch the Bringup first. Skip this step if you have launched bringup previously.
   • Open a new terminal from Remote PC with Ctrl + Alt + T and connect to Raspberry Pi with its IP address. The default password is ubuntu.

     $ ssh ubuntu@{IP_ADDRESS_OF_RASPBERRY_PI}
     $ export TURTLEBOT3_MODEL=${TB3_MODEL}
     $ ros2 launch turtlebot3_bringup robot.launch.py
     

2. Open a new terminal from Remote PC with Ctrl + Alt + T and launch the SLAM node. The Cartographer is used as a default SLAM method.

   $ export TURTLEBOT3_MODEL=${TB3_MODEL}
   $ ros2 launch turtlebot3_cartographer cartographer.launch.py
   

   

1. If the Bringup is not running on the TurtleBot3 SBC, launch the Bringup first. Skip this step if you have launched bringup previously.
   • Open a new terminal from Remote PC with Ctrl + Alt + T and connect to Raspberry Pi with its IP address. The default password is ubuntu.

     $ ssh ubuntu@{IP_ADDRESS_OF_RASPBERRY_PI}
     

     Please use the proper keyword among burger, waffle, waffle_pi for the TURTLEBOT3_MODEL parameter.

     $ export TURTLEBOT3_MODEL=burger
     $ ros2 launch turtlebot3_bringup robot.launch.py
     

2. Open a new terminal from Remote PC with Ctrl + Alt + T and launch the SLAM node. The Cartographer is used as a default SLAM method.

   $ export TURTLEBOT3_MODEL=burger
   $ ros2 launch turtlebot3_cartographer cartographer.launch.py
   

   

1. If the Bringup is not running on the TurtleBot3 SBC, launch the Bringup first. Skip this step if you have launched bringup previously.
   • Open a new terminal from Remote PC with Ctrl + Alt + T and connect to Raspberry Pi with its IP address. The default password is ubuntu.

     $ ssh ubuntu@{IP_ADDRESS_OF_RASPBERRY_PI}
     

     Please use the proper keyword among burger, waffle, waffle_pi for the TURTLEBOT3_MODEL parameter.

     $ export TURTLEBOT3_MODEL=burger
     $ ros2 launch turtlebot3_bringup robot.launch.py
     

2. Open a new terminal from Remote PC with Ctrl + Alt + T and launch the SLAM node. The Cartographer is used as a default SLAM method.

   $ export TURTLEBOT3_MODEL=burger
   $ ros2 launch turtlebot3_cartographer cartographer.launch.py
   

   

1. If the Bringup is not running on the TurtleBot3, launch the Bringup. Skip this step if you have launched bringup previously.

   > roslaunch turtlebot3_bringup turtlebot3_robot.launch
   

2. Open a new terminal from Remote PC and launch the SLAM node. The Gmapping is used as a default SLAM method. Please use the proper keyword among burger, waffle, waffle_pi for the TURTLEBOT3_MODEL parameter.

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

Once SLAM node is successfully up and running, TurtleBot3 will be exploring unknown area of the map using teleoperation. It is important to avoid vigorous movements such as changing the linear and angular speed too quickly. When building a map using the TurtleBot3, it is a good practice to scan every corner of the map.

1. Open a new terminal and run the teleoperation node from the Remote PC.
   Please use the proper keyword among burger, waffle, waffle_pi for the TURTLEBOT3_MODEL parameter.

   $ export TURTLEBOT3_MODEL=burger
   $ roslaunch turtlebot3_teleop turtlebot3_teleop_key.launch
   
    Control Your TurtleBot3!
    ---------------------------
    Moving around:
           w
      a    s    d
           x
   
    w/x : increase/decrease linear velocity
    a/d : increase/decrease angular velocity
    space key, s : force stop
   
    CTRL-C to quit
   

2. Start exploring and drawing the map.
   

Once SLAM node is successfully up and running, TurtleBot3 will be exploring unknown area of the map using teleoperation. It is important to avoid vigorous movements such as changing the linear and angular speed too quickly. When building a map using the TurtleBot3, it is a good practice to scan every corner of the map.

1. Open a new terminal and run the teleoperation node from the Remote PC.
   Please use the proper keyword among burger, waffle, waffle_pi for the TURTLEBOT3_MODEL parameter.

   $ export TURTLEBOT3_MODEL=burger
   $ roslaunch turtlebot3_teleop turtlebot3_teleop_key.launch
   
    Control Your TurtleBot3!
    ---------------------------
    Moving around:
           w
      a    s    d
           x
   
    w/x : increase/decrease linear velocity
    a/d : increase/decrease angular velocity
    space key, s : force stop
   
    CTRL-C to quit
   

2. Start exploring and drawing the map.
   

Once SLAM node is successfully up and running, TurtleBot3 will be exploring unknown area of the map using teleoperation. It is important to avoid vigorous movements such as changing the linear and angular speed too quickly. When building a map using the TurtleBot3, it is a good practice to scan every corner of the map.

1. Open a new terminal and run the teleoperation node from the Remote PC.
   Please use the proper keyword among burger, waffle, waffle_pi for the TURTLEBOT3_MODEL parameter.

   $ export TURTLEBOT3_MODEL=burger
   $ roslaunch turtlebot3_teleop turtlebot3_teleop_key.launch
   
    Control Your TurtleBot3!
    ---------------------------
    Moving around:
           w
      a    s    d
           x
   
    w/x : increase/decrease linear velocity
    a/d : increase/decrease angular velocity
    space key, s : force stop
   
    CTRL-C to quit
   

2. Start exploring and drawing the map.
   

Once SLAM node is successfully up and running, TurtleBot3 will be exploring unknown area of the map using teleoperation. It is important to avoid vigorous movements such as changing the linear and angular speed too quickly. When building a map using the TurtleBot3, it is a good practice to scan every corner of the map.

1. Open a new terminal and run the teleoperation node from the Remote PC.
   Please use the proper keyword among burger, waffle, waffle_pi for the TURTLEBOT3_MODEL parameter.

   $ export TURTLEBOT3_MODEL=burger
   $ ros2 run turtlebot3_teleop teleop_keyboard
   
   Control Your TurtleBot3!
   ---------------------------
   Moving around:
          w
     a    s    d
          x
   
   w/x : increase/decrease linear velocity
   a/d : increase/decrease angular velocity
   space key, s : force stop
   
   CTRL-C to quit
   

2. Start exploring and drawing the map.
   

Once SLAM node is successfully up and running, TurtleBot3 will be exploring unknown area of the map using teleoperation. It is important to avoid vigorous movements such as changing the linear and angular speed too quickly. When building a map using the TurtleBot3, it is a good practice to scan every corner of the map.

1. Open a new terminal and run the teleoperation node from the Remote PC.
   Please use the proper keyword among burger, waffle, waffle_pi for the TURTLEBOT3_MODEL parameter.

   $ export TURTLEBOT3_MODEL=burger
   $ ros2 run turtlebot3_teleop teleop_keyboard
   
   Control Your TurtleBot3!
   ---------------------------
   Moving around:
          w
     a    s    d
          x
   
   w/x : increase/decrease linear velocity
   a/d : increase/decrease angular velocity
   space key, s : force stop
   
   CTRL-C to quit
   

2. Start exploring and drawing the map.
   

Once SLAM node is successfully up and running, TurtleBot3 will be exploring unknown area of the map using teleoperation. It is important to avoid vigorous movements such as changing the linear and angular speed too quickly. When building a map using the TurtleBot3, it is a good practice to scan every corner of the map.

1. Open a new terminal and run the teleoperation node from the Remote PC.
   Please use the proper keyword among burger, waffle, waffle_pi for the TURTLEBOT3_MODEL parameter.

   $ export TURTLEBOT3_MODEL=burger
   $ ros2 run turtlebot3_teleop teleop_keyboard
   
   Control Your TurtleBot3!
   ---------------------------
   Moving around:
          w
     a    s    d
          x
   
   w/x : increase/decrease linear velocity
   a/d : increase/decrease angular velocity
   space key, s : force stop
   
   CTRL-C to quit
   

2. Start exploring and drawing the map.
   

Once SLAM node is successfully up and running, TurtleBot3 will be exploring unknown area of the map using teleoperation. It is important to avoid vigorous movements such as changing the linear and angular speed too quickly. When building a map using the TurtleBot3, it is a good practice to scan every corner of the map.

1. Open a new terminal and run the teleoperation node from the PC.
   Please use the proper keyword among burger, waffle, waffle_pi for the TURTLEBOT3_MODEL parameter.

   > set TURTLEBOT3_MODEL=waffle
   > roslaunch turtlebot3_teleop turtlebot3_teleop_key.launch
   
    Control Your TurtleBot3!
    ---------------------------
    Moving around:
           w
      a    s    d
           x
   
    w/x : increase/decrease linear velocity
    a/d : increase/decrease angular velocity
    space key, s : force stop
   
    CTRL-C to quit
   

2. Start exploring and drawing the map.
   

Gmapping has many parameters to change performances for different environments. You can get an information about whole parameters in ROS WiKi or refer to the Chapter 11 of ROS Robot Programming. This tuning guide provides tips when configuring gmapping parameters. If you want to optimize SLAM performances for your environments, this section might be helpful.

Below parameters are defined in turtlebot3_slam/config/gmapping_params.yaml file.

maxUrange

This parameter is set the maximum usable range of the lidar sensor.

map_update_interval

This parameter defines time between updating the map.
The smaller the value, the more frequent the map is updated.
However, setting this too small will be require more processing power for the map calculation. Set this parameter depending on the map environment.

minimumScore

This parameter sets the minimum score value that determines the success or failure of the sensor’s scan data matching test. This can reduce errors in the expected position of the robot in a large area. If the parameter is set properly, you will see information similar to one shown below.

Average Scan Matching Score=278.965
neff= 100
Registering Scans:Done
update frame 6
update ld=2.95935e-05 ad=0.000302522
Laser Pose= -0.0320253 -5.36882e-06 -3.14142

If set too high, you might see below warnings.

Scan Matching Failed, using odometry. Likelihood=0
lp:-0.0306155 5.75314e-06 -3.14151
op:-0.0306156 5.90277e-06 -3.14151

linearUpdate

When the robot translates longer distance than this value, it will run the scan process.

angularUpdate

When the robot rotates more than this value, it will run the scan process. It is recommended to set this value less than linearUpdate.

Gmapping has many parameters to change performances for different environments. You can get an information about whole parameters in ROS WiKi or refer to the Chapter 11 of ROS Robot Programming. This tuning guide provides tips when configuring gmapping parameters. If you want to optimize SLAM performances for your environments, this section might be helpful.

Below parameters are defined in turtlebot3_slam/config/gmapping_params.yaml file.

maxUrange

This parameter is set the maximum usable range of the lidar sensor.

map_update_interval

This parameter defines time between updating the map.
The smaller the value, the more frequent the map is updated.
However, setting this too small will be require more processing power for the map calculation. Set this parameter depending on the map environment.

minimumScore

This parameter sets the minimum score value that determines the success or failure of the sensor’s scan data matching test. This can reduce errors in the expected position of the robot in a large area. If the parameter is set properly, you will see information similar to one shown below.

Average Scan Matching Score=278.965
neff= 100
Registering Scans:Done
update frame 6
update ld=2.95935e-05 ad=0.000302522
Laser Pose= -0.0320253 -5.36882e-06 -3.14142

If set too high, you might see below warnings.

Scan Matching Failed, using odometry. Likelihood=0
lp:-0.0306155 5.75314e-06 -3.14151
op:-0.0306156 5.90277e-06 -3.14151

linearUpdate

When the robot translates longer distance than this value, it will run the scan process.

angularUpdate

When the robot rotates more than this value, it will run the scan process. It is recommended to set this value less than linearUpdate.

Gmapping has many parameters to change performances for different environments. You can get an information about whole parameters in ROS WiKi or refer to the Chapter 11 of ROS Robot Programming. This tuning guide provides tips when configuring gmapping parameters. If you want to optimize SLAM performances for your environments, this section might be helpful.

Below parameters are defined in turtlebot3_slam/config/gmapping_params.yaml file.

maxUrange

This parameter is set the maximum usable range of the lidar sensor.

map_update_interval

This parameter defines time between updating the map.
The smaller the value, the more frequent the map is updated.
However, setting this too small will be require more processing power for the map calculation. Set this parameter depending on the map environment.

minimumScore

This parameter sets the minimum score value that determines the success or failure of the sensor’s scan data matching test. This can reduce errors in the expected position of the robot in a large area. If the parameter is set properly, you will see information similar to one shown below.

Average Scan Matching Score=278.965
neff= 100
Registering Scans:Done
update frame 6
update ld=2.95935e-05 ad=0.000302522
Laser Pose= -0.0320253 -5.36882e-06 -3.14142

If set too high, you might see below warnings.

Scan Matching Failed, using odometry. Likelihood=0
lp:-0.0306155 5.75314e-06 -3.14151
op:-0.0306156 5.90277e-06 -3.14151

linearUpdate

When the robot translates longer distance than this value, it will run the scan process.

angularUpdate

When the robot rotates more than this value, it will run the scan process. It is recommended to set this value less than linearUpdate.

The SLAM in ROS2 uses Cartographer ROS which provides configuration options via Lua file.

Below options are defined in turtlebot3_cartographer/config/turtlebot3_lds_2d.lua file. For more details about each options, please refer to the Cartographer ROS official documentation.

MAP_BUILDER.use_trajectory_builder_2d

This option sets the type of SLAM.

TRAJECTORY_BUILDER_2D.min_range

This option sets the minimum usable range of the lidar sensor.

TRAJECTORY_BUILDER_2D.max_range

This option sets the maximum usable range of the lidar sensor.

TRAJECTORY_BUILDER_2D.missing_data_ray_length

In 2D, Cartographer replaces ranges further than max_range with TRAJECTORY_BUILDER_2D.missing_data_ray_length.

TRAJECTORY_BUILDER_2D.use_imu_data

If you use 2D SLAM, range data can be handled in real-time without an additional source of information so you can choose whether you’d like Cartographer to use an IMU or not.

TRAJECTORY_BUILDER_2D.use_online_correlative_scan_matching

Local SLAM : The RealTimeCorrelativeScanMatcher can be toggled depending on the reliability of the sensor.

TRAJECTORY_BUILDER_2D.motion_filter.max_angle_radians

Local SLAM : To avoid inserting too many scans per submaps, A scan is dropped if the motion does not exceed a certain angle.

POSE_GRAPH.optimize_every_n_nodes

Global SLAM : Setting POSE_GRAPH.optimize_every_n_nodes to 0 is a handy way to disable global SLAM and concentrate on the behavior of local SLAM.

POSE_GRAPH.constraint_builder.min_score

Global SLAM : Threshold for the scan match score below which a match is not considered. Low scores indicate that the scan and map do not look similar.

POSE_GRAPH.constraint_builder.global_localization_min_score

Global SLAM : Threshold below which global localizations are not trusted.

NOTE: Constraints can be visualized in RViz, it is very handy to tune global SLAM. One can also toggle POSE_GRAPH.constraint_builder.log_matches to get regular reports of the constraints builder formatted as histograms.

The SLAM in ROS2 uses Cartographer ROS which provides configuration options via Lua file.

Below options are defined in turtlebot3_cartographer/config/turtlebot3_lds_2d.lua file. For more details about each options, please refer to the Cartographer ROS official documentation.

MAP_BUILDER.use_trajectory_builder_2d

This option sets the type of SLAM.

TRAJECTORY_BUILDER_2D.min_range

This option sets the minimum usable range of the lidar sensor.

TRAJECTORY_BUILDER_2D.max_range

This option sets the maximum usable range of the lidar sensor.

TRAJECTORY_BUILDER_2D.missing_data_ray_length

In 2D, Cartographer replaces ranges further than max_range with TRAJECTORY_BUILDER_2D.missing_data_ray_length.

TRAJECTORY_BUILDER_2D.use_imu_data

If you use 2D SLAM, range data can be handled in real-time without an additional source of information so you can choose whether you’d like Cartographer to use an IMU or not.

TRAJECTORY_BUILDER_2D.use_online_correlative_scan_matching

Local SLAM : The RealTimeCorrelativeScanMatcher can be toggled depending on the reliability of the sensor.

TRAJECTORY_BUILDER_2D.motion_filter.max_angle_radians

Local SLAM : To avoid inserting too many scans per submaps, A scan is dropped if the motion does not exceed a certain angle.

POSE_GRAPH.optimize_every_n_nodes

Global SLAM : Setting POSE_GRAPH.optimize_every_n_nodes to 0 is a handy way to disable global SLAM and concentrate on the behavior of local SLAM.

POSE_GRAPH.constraint_builder.min_score

Global SLAM : Threshold for the scan match score below which a match is not considered. Low scores indicate that the scan and map do not look similar.

POSE_GRAPH.constraint_builder.global_localization_min_score

Global SLAM : Threshold below which global localizations are not trusted.

NOTE: Constraints can be visualized in RViz, it is very handy to tune global SLAM. One can also toggle POSE_GRAPH.constraint_builder.log_matches to get regular reports of the constraints builder formatted as histograms.

The SLAM in ROS2 uses Cartographer ROS which provides configuration options via Lua file.

Below options are defined in turtlebot3_cartographer/config/turtlebot3_lds_2d.lua file. For more details about each options, please refer to the Cartographer ROS official documentation.

MAP_BUILDER.use_trajectory_builder_2d

This option sets the type of SLAM.

TRAJECTORY_BUILDER_2D.min_range

This option sets the minimum usable range of the lidar sensor.

TRAJECTORY_BUILDER_2D.max_range

This option sets the maximum usable range of the lidar sensor.

TRAJECTORY_BUILDER_2D.missing_data_ray_length

In 2D, Cartographer replaces ranges further than max_range with TRAJECTORY_BUILDER_2D.missing_data_ray_length.

TRAJECTORY_BUILDER_2D.use_imu_data

If you use 2D SLAM, range data can be handled in real-time without an additional source of information so you can choose whether you’d like Cartographer to use an IMU or not.

TRAJECTORY_BUILDER_2D.use_online_correlative_scan_matching

Local SLAM : The RealTimeCorrelativeScanMatcher can be toggled depending on the reliability of the sensor.

TRAJECTORY_BUILDER_2D.motion_filter.max_angle_radians

Local SLAM : To avoid inserting too many scans per submaps, A scan is dropped if the motion does not exceed a certain angle.

POSE_GRAPH.optimize_every_n_nodes

Global SLAM : Setting POSE_GRAPH.optimize_every_n_nodes to 0 is a handy way to disable global SLAM and concentrate on the behavior of local SLAM.

POSE_GRAPH.constraint_builder.min_score

Global SLAM : Threshold for the scan match score below which a match is not considered. Low scores indicate that the scan and map do not look similar.

POSE_GRAPH.constraint_builder.global_localization_min_score

Global SLAM : Threshold below which global localizations are not trusted.

NOTE: Constraints can be visualized in RViz, it is very handy to tune global SLAM. One can also toggle POSE_GRAPH.constraint_builder.log_matches to get regular reports of the constraints builder formatted as histograms.

Gmapping has many parameters to change performances for different environments. You can get an information about whole parameters in ROS WiKi or refer to the Chapter 11 of ROS Robot Programming. This tuning guide provides tips when configuring gmapping parameters. If you want to optimize SLAM performances for your environments, this section might be helpful.

Below parameters are defined in turtlebot3_slam/config/gmapping_params.yaml file.

maxUrange

This parameter is set the maximum usable range of the lidar sensor.

map_update_interval

This parameter defines time between updating the map.
The smaller the value, the more frequent the map is updated.
However, setting this too small will be require more processing power for the map calculation. Set this parameter depending on the map environment.

minimumScore

This parameter sets the minimum score value that determines the success or failure of the sensor’s scan data matching test. This can reduce errors in the expected position of the robot in a large area. If the parameter is set properly, you will see information similar to one shown below.

Average Scan Matching Score=278.965
neff= 100
Registering Scans:Done
update frame 6
update ld=2.95935e-05 ad=0.000302522
Laser Pose= -0.0320253 -5.36882e-06 -3.14142

If set too high, you might see below warnings.

Scan Matching Failed, using odometry. Likelihood=0
lp:-0.0306155 5.75314e-06 -3.14151
op:-0.0306156 5.90277e-06 -3.14151

linearUpdate

When the robot translates longer distance than this value, it will run the scan process.

angularUpdate

When the robot rotates more than this value, it will run the scan process. It is recommended to set this value less than linearUpdate.

The map is drawn based on the robot’s odometry, tf and scan information. These map data is drawn in the RViz window as the TurtleBot3 was traveling. After creating a complete map of desired area, save the map data to the local drive for the later use.

1. Launch the map_saver node in the map_server package to create map files.
   The map file is saved in the directory where the map_saver node is launched at.
   Unless a specific file name is provided, map will be used as a default file name and create map.pgm and map.yaml.

   $ rosrun map_server map_saver -f ~/map
   

The -f option specifies a folder location and a file name where files to be saved.
With the above command, map.pgm and map.yaml will be saved in the home folder ~/(/home/${username}).

The map is drawn based on the robot’s odometry, tf and scan information. These map data is drawn in the RViz window as the TurtleBot3 was traveling. After creating a complete map of desired area, save the map data to the local drive for the later use.

1. Launch the map_saver node in the map_server package to create map files.
   The map file is saved in the directory where the map_saver node is launched at.
   Unless a specific file name is provided, map will be used as a default file name and create map.pgm and map.yaml.

   $ rosrun map_server map_saver -f ~/map
   

The -f option specifies a folder location and a file name where files to be saved.
With the above command, map.pgm and map.yaml will be saved in the home folder ~/(/home/${username}).

The map is drawn based on the robot’s odometry, tf and scan information. These map data is drawn in the RViz window as the TurtleBot3 was traveling. After creating a complete map of desired area, save the map data to the local drive for the later use.

1. Launch the map_saver node in the map_server package to create map files.
   The map file is saved in the directory where the map_saver node is launched at.
   Unless a specific file name is provided, map will be used as a default file name and create map.pgm and map.yaml.

   $ rosrun map_server map_saver -f ~/map
   

The -f option specifies a folder location and a file name where files to be saved.
With the above command, map.pgm and map.yaml will be saved in the home folder ~/(/home/${username}).

The map is drawn based on the robot’s odometry, tf and scan information. These map data is drawn in the RViz window as the TurtleBot3 was traveling. After creating a complete map of desired area, save the map data to the local drive for the later use.

1. Launch the map_saver node in the nav2_map_server package to create map files.
   The map file is saved in the directory where the map_saver node is launched at.
   Unless a specific file name is provided, map will be used as a default file name and create map.pgm and map.yaml.

   $ ros2 run nav2_map_server map_saver -f ~/map
   

The -f option specifies a folder location and a file name where files to be saved.
With the above command, map.pgm and map.yaml will be saved in the home folder ~/(/home/${username}).

The map is drawn based on the robot’s odometry, tf and scan information. These map data is drawn in the RViz window as the TurtleBot3 was traveling. After creating a complete map of desired area, save the map data to the local drive for the later use.

1. Launch the map_saver_cli node in the nav2_map_server package to create map files.
   The map file is saved in the directory where the map_saver_cli node is launched at.
   Unless a specific file name is provided, map will be used as a default file name and create map.pgm and map.yaml.

   $ ros2 run nav2_map_server map_saver_cli -f ~/map
   

The -f option specifies a folder location and a file name where files to be saved.
With the above command, map.pgm and map.yaml will be saved in the home folder ~/(/home/${username}).

The map is drawn based on the robot’s odometry, tf and scan information. These map data is drawn in the RViz window as the TurtleBot3 was traveling. After creating a complete map of desired area, save the map data to the local drive for the later use.

1. Launch the map_saver_cli node in the nav2_map_server package to create map files.
   The map file is saved in the directory where the map_saver_cli node is launched at.
   Unless a specific file name is provided, map will be used as a default file name and create map.pgm and map.yaml.

   $ ros2 run nav2_map_server map_saver_cli -f ~/map
   

The -f option specifies a folder location and a file name where files to be saved.
With the above command, map.pgm and map.yaml will be saved in the home folder ~/(/home/${username}).

The map is drawn based on the robot’s odometry, tf and scan information. These map data is drawn in the RViz window as the TurtleBot3 was traveling. After creating a complete map of desired area, save the map data to the local drive for the later use.

1. Launch the map_saver node in the map_server package to create map files.
   The map file is saved in the directory where the map_saver node is launched at.
   Unless a specific file name is provided, map will be used as a default file name and create map.pgm and map.yaml.

   > rosrun map_server map_saver -f %USERPROFILE%\map
   

The -f option specifies a folder location and a file name where files to be saved.
With the above command, map.pgm and map.yaml will be saved in the user directory. The user directory is stored in an environment variable %USERPROFILE%

The map uses two-dimensional Occupancy Grid Map (OGM), which is commonly used in ROS. The saved map will look like the figure below, where white area is collision free area while black area is occupied and inaccessible area, and gray area represents the unknown area. This map is used for the 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.

The map uses two-dimensional Occupancy Grid Map (OGM), which is commonly used in ROS. The saved map will look like the figure below, where white area is collision free area while black area is occupied and inaccessible area, and gray area represents the unknown area. This map is used for the 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.

The map uses two-dimensional Occupancy Grid Map (OGM), which is commonly used in ROS. The saved map will look like the figure below, where white area is collision free area while black area is occupied and inaccessible area, and gray area represents the unknown area. This map is used for the 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.

The map uses two-dimensional Occupancy Grid Map (OGM), which is commonly used in ROS. The saved map will look like the figure below, where white area is collision free area while black area is occupied and inaccessible area, and gray area represents the unknown area. This map is used for the 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.

The map uses two-dimensional Occupancy Grid Map (OGM), which is commonly used in ROS. The saved map will look like the figure below, where white area is collision free area while black area is occupied and inaccessible area, and gray area represents the unknown area. This map is used for the 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.

The map uses two-dimensional Occupancy Grid Map (OGM), which is commonly used in ROS. The saved map will look like the figure below, where white area is collision free area while black area is occupied and inaccessible area, and gray area represents the unknown area. This map is used for the 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.

The map uses two-dimensional Occupancy Grid Map (OGM), which is commonly used in ROS. The saved map will look like the figure below, where white area is collision free area while black area is occupied and inaccessible area, and gray area represents the unknown area. This map is used for the 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.