Question 1

How to set up slam_gmapping with a TurtleBot3 in ROS2?

Accepted Answer

Ensure your TurtleBot3 publishes laser scans on the 'scan' topic and TF is correctly configured. Install the package via 'apt' or from source, then launch it with 'ros2 launch slam_gmapping slam_gmapping.launch.py'. Adjust parameters in the launch file for your specific environment to optimize performance.

Question 2

What are the key parameters to tune in slam_gmapping for better maps?

Accepted Answer

Focus on parameters like 'maxUrange' for laser range, 'delta' for map resolution, and 'particles' for the particle filter sample count. Tuning these based on sensor accuracy and environment size can enhance map quality and localization, as mentioned in the algorithm's documentation.

Question 3

slam_gmapping vs cartographer: which is better for indoor robots?

Accepted Answer

slam_gmapping is simpler and works well for small to medium indoor spaces with static features, while cartographer offers superior loop closure and handles larger or more dynamic environments. For quick prototyping with laser scanners, gmapping suffices, but cartographer is better for robust, scalable mapping.

Question 4

How to visualize the map from slam_gmapping in Rviz2?

Accepted Answer

In Rviz2, add a 'Map' display and set the topic to '/map'. Ensure slam_gmapping is running and publishing data. You can also visualize laser scans on 'scan' and TF frames to debug the setup, as the README specifies the output topics.

Question 5

Can slam_gmapping work with depth cameras or only laser scanners?

Accepted Answer

slam_gmapping is designed for laser scanners (sensor_msgs/LaserScan). To use depth cameras, you must convert depth data to laser scan format or opt for a different SLAM package that supports point clouds, as this package lacks native visual input handling.

Question 6

What to do if slam_gmapping is not building the map correctly?

Accepted Answer

Verify that laser scans are published correctly on the 'scan' topic and TF transforms between the robot base and laser are accurate. Check parameter settings like 'ogain' and consider increasing 'particles' for complex environments, as improper configuration can lead to mapping failures.

slam_gmapping

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