Navigating with Dingo¶
To get all Navigation related files for Dingo, run:
sudo apt-get install ros-noetic-dingo-navigation
Below are the example launch files for three different configurations for navigating Dingo:
Navigation in an odometric frame without a map, using only move_base.
Generating a map using gmapping.
Localization with a known map using amcl.
If you’re using simulation, bring up Dingo with the front laser enabled for the following demos:
roslaunch dingo_gazebo dingo_world.launch config:=front_laser
If you’re working with a real Dingo, it’s suggested to connect via SSH and launch the dingo_navigation launchfiles from on board the robot. You’ll need to have bidirectional communication with the robot’s roscore in order to launch rviz on your workstation (see remote).
Navigation Without a Map¶
In the odometry navigation demo Dingo attempts to reach a given goal in the world within a user-specified tolerance. The 2D navigation, generated by move_base, takes in information from odometry, laser scanner, and a goal pose and outputs safe velocity commands. In this demo the configuration of move_base is set for navigation without a map in an odometric frame (that is, without reference to a map).
To launch the navigation demo, run:
roslaunch dingo_navigation odom_navigation_demo.launch
To visualize with the suggested rviz configuration launch:
roslaunch dingo_viz view_robot.launch config:=navigation
To send goals to the robot, select the 2D Nav Goal tool from the top toolbar, and then click anywhere in the rviz view to set the position. Alternatively, click and drag slightly to set the goal position and orientation.
If you wish to customize the parameters of move_base, local costmap, global costmap and base_local_planner, clone dingo_navigation into your own workspace and modify the corresponding files in the params subfolder.
Making a Map¶
In this demonstration, Dingo generates a map using gmapping. Begin by launch the gmapping launch file on the robot:
roslaunch dingo_navigation gmapping_demo.launch
And on your workstation, launch rviz with the suggested configuration:
roslaunch dingo_viz view_robot.launch config:=gmapping
You must slowly drive Dingo around to build the map. As obstacles come into view of the laser scanner, they will be added to the map, which is shown in rviz. You can either drive manually using the interactive markers, or semi-autonomously by sending navigation goals (as above).
When you’re satisfied, you can save the produced map using map_saver:
rosrun map_server map_saver -f mymap
This will create a
mymap.pgm file in your current directory.
Navigation With a Map¶
Using amcl, Dingo is able to globally localize itself in a known map. AMCL takes in information from odometry, laser scanner and an existing map and estimates the robot’s pose.
To start the AMCL demo:
roslaunch dingo_navigation amcl_demo.launch map_file:=/path/to/my/map.yaml
If you don’t specify
map_file, it defaults to an included pre-made map of the default “Dingo Race”
environment which Dingo’s simulator spawns in. If you’re using a real Dingo in your own environment,
you’ll definitely want to override this with the map created using the gmapping demo.
Before navigating, you need to initialize the localization system by setting the pose of the robot in the map. This can be done using 2D Pose Estimate in rviz or by setting the amcl initial_pose parameters. To visualize with the suggested rviz configuration launch:
roslaunch dingo_viz view_robot.launch config:=localization
When rviz appears, select the Set 2D Pose tool from the toolbar, and click on the map to indicate to the robot approximately where it is.