Are there any best practices for visualizing covariance data as uncertainty ellipses/ellipsoids using ROS tools, especially rviz? There's quite a few applications where this would be desirable (most notably gaussian position estimates of parametric filters).
Of course, one could write a conversion node that gets fed for example with PoseWithCovariance messages and outputs a MarkerArray containing a polygonal representation of the ellipse/ellipsoid, but that is a bit cumbersome IMHO.
Has anybody implemented code for this scenario? Is there anything planned in the short or long term for easing development of such visualizations (I'm thinking of rviz plugins, rviz2 etc here)?
Post score: 13
Comment by szokei on 2011-03-11:
I did this also in Matlab, with 2D laser data, object separation with gaussian/ellipsoids.
Comment by mjcarroll on 2011-03-06:
Exactly what we were talking about. Perhaps it is time to add something like this into RVIZ's capabilities.
Comment by Stefan Kohlbrecher on 2011-03-05:
Indeed, this is also what came to my mind. A 2x2 or 3x3 covariance matrix could be visualized by computing the Eigenvectors and -values and then rotating and scaling a sphere marker accordingly. I did it similar for the 2D case here (non ROS): http://www.youtube.com/watch?v=mbVFB4-vz4M
Comment by mjcarroll on 2011-03-05:
Generally, the discussion was a deformed sphere for representing 3-dimension position data uncertainty. Representing pointing (roll/pitch/yaw) could then be a color map on the sphere. This only leaves one dimension of uncertainty out.
Comment by William on 2011-03-05:
My roommate and I recently discussed this in the middle of our latest optimal control and estimation homework. We talked about how to visualize a 3x3 or even a 9x9 covariance matrix. I think an rviz plugin would be the best solution, but I haven't seen or heard of anything so far to do this.