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What tf frames are required for autonomous navigation?

asked 2019-08-11 07:43:02 -0600

parzival gravatar image

I am new trying to implement autonomous navigation on my 2WD drive robot. I am acquiring laser scan using rplidar using rplidar_ros, and using the scan with hector_mapping to get odom as well. There is no other source of odometry.

I want to know what tf frames are necessary to implement the navigation stack? Currently I have tf tree which looks like this:

map -> odom -> base_footprint -> base_link -> laser

I see turtlebot and others have very complicated robot_state_publishers. Is that absolutely necessary to do the job I want to do? I'm using ROS Kinetic on Ubuntu 16.04

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answered 2019-08-11 10:51:07 -0600

johnconn gravatar image

updated 2019-08-11 10:53:01 -0600

what you have will work.

The essential transforms are the map to odom transform:

map -> odom

the odom to robot transform:

odom -> base_footprint

and then robot transform to the published frames of any sensor data, which in your case is just:

base_link -> laser

Most robots end up with a lot of static transforms, because their are usually many small parts seperating the transform from base_footprint to whatever sensors the robot has, so instead of base_link -> laser, you end up with transforms that have more degrees of seperation, like this:

base_link ->chassis->laser_mount->laser

But those degrees of seperation aren't necessary, all that matters is that navigation has a way to transform from the sensor frame to your robot's base frame.

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Thanks! I just saw some robots having very detailed tf_tree with even wheels and such transforms due to which I got this doubt

parzival gravatar image parzival  ( 2019-08-12 03:59:43 -0600 )edit

There are several REPs with relevant information such as REP 105 for mobile platforms

tfoote gravatar image tfoote  ( 2019-08-12 04:06:01 -0600 )edit

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Asked: 2019-08-11 07:43:02 -0600

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Last updated: Aug 11 '19