Udacity Practice: SLAM Gmapping

Implement a gmapping ROS package that is based on the Grid-based FastSLAM algorithm to map an environment.

Prerequisite before Building

Install packages' dependencies

$ cd ..
$ source devel/setup.bash
$ rosdep -i install turtlebot_gazebo
$ rosdep -i install turtlebot_teleop
$ rosdep install gmapping

Build

Open a new terminal and run

$ cd <Your catkin workspace>
$ catkin_make
$ source devel/setup.bash

Packages

• turtlebot_gazebo
  To use the Gazebo world file.
• turtlebot_teleop
  To manually control a robot using keyboard commands.
• slam_gmapping
  To provide SLAM capabilities.

Running the Codes

1. Open a new terminal and run

   $ roslaunch turtlebot_gazebo turtlebot_world.launch world_file:=worlds/willowgarage.world
   

2. Open another terminal and launch

   $ roslaunch turtlebot_teleop keyboard_teleop.launch
   

3. In a new terminal, run

   $ rosrun gmapping slam_gmapping
   

4. In another teminal, run

   $ rosrun rviz rviz
   

   Edit the rviz configuration as follows
   • Change the Fixed Frame to map
   • Keep Reference Frame as default
   • Add a robot model
   • Add a camera and select the /camera/rgb/image_raw topic
   • Add a map and select the /map topic
5. You can now map the environment by driving your robot using keyboard commands.
6. (Optional) To save a map of the environment

   $ cd /home/workspace/
   $ rosrun map_server map_saver -f myMap
   

   With the map_server you can load and save maps. Running map_server will generate the map.pgm and the map.yaml files.