Adding transforms creates a not-tree

asked 2018-09-18 10:42:32 -0500

updated 2018-09-18 11:10:19 -0500

My understanding of ROS transforms is that they need to form a tree. Is it ever the case where it can be a non-tree graph? If not, then how should I form this tf tree?

Here's my situation: Let's say I have a typical map->odom transform, then an EKF that provides the odom->base_link transform. Now, figure that attached to that base_link is an AR tag, which has a static transform from base_link. An external camera exists which has a transform from map. That camera estimates a pose for the AR tag attached to base_link, however. So now I've created a situation where the AR tag is a child of both the external camera and base_link. What's the proper way to go about this?

map-->odom-->base_link-->ar_tag
 \-->camera-------------/

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answered 2018-09-18 17:33:42 -0500

lucasw

8729 ●137 ●239 ●256 https://github.com/lucasw

It either has to exist separately in parallel under a different name like 'ar_tag_camera', or you need a node that can fuse the camera information with the other sensor input to provide a higher quality position than the EKF or camera by themselves (or equivalently the EKF needs to take in the camera position as an input). Maybe there is a way to shoehorn the camera position to look like some other already supported sensor (it is roughly like gps), but the likelihood of getting that to work is probably lower than if the software was designed for a camera looking at a ar tag or similar.

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Thanks @lucasw .. Generally then, how are sensors that give absolute measurements (gps, mocap, etc), which would have a static transform from a map, represented alongside a robotic system that also uses proprioceptive sensors (IMU, visual odometry, etc)?

seanarm ( 2018-09-18 17:42:48 -0500 )edit

http://docs.ros.org/api/robot_localiz... takes in a PoseWithCovarianceStamped which seems like a fine fit for the camera derived ar tag position. The trick is to get the Covariance part right...

lucasw ( 2018-09-18 18:25:13 -0500 )edit

... maybe someone else more knowledgeable can jump in (and you could look at other ros answers related to covariance)- it would be nice if there was a good tutorial on how to calibrate a pose sensor or an imu and get decent starting covariances.

lucasw ( 2018-09-18 18:27:16 -0500 )edit

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answered 2018-09-21 11:14:41 -0500

seanarm

723 ●38 ●51 ●63 http://seanmatthe.ws/

updated 2018-09-21 11:15:20 -0500

@lucasw I'm not sure that this is the correct answer either, but after some thought, it's the answer that makes the most sense to me. It's applicable to any sensor whose measurement device is "globally-anchored".

I started treating the marker (attached to the robot) and the estimate of the pose of that marker (determined by a camera mounted in the map frame) as separate entities. It makes sense because the physical link between marker and the robot is fixed, where as the link between the pose estimate of the marker and the robot is not guaranteed to be fixed (because of localization error). This method allows for comparing the global measurement to the fused odometry source the robot uses as its pose within the map.

map-->odom-->base_link-->ar_tag
 \----->canera---------->arg_tag_estimate

A comparison between the physically-linked ar_tag and the ar_tag_estimate is just a transform to a common frame. Does that all sound reasonable?

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That's the 'separately in parallel' approach. A cheap 'fusion' additional step would be to occasionally reset the odometry to the transform necessary to make ar_tag and arg_tag_estimate the same but then let odometry run for a while (especially when the tag goes out of view).

lucasw ( 2018-09-21 11:23:56 -0500 )edit

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