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You can find my first prototype for a tf extension allowing to define uncertainty in tf frames can be found at http://www.ros.org/wiki/uncertain_tf. So far it supports sampling through the tf chain where each frame might or might not have a covariance attached. Additionally one can sample over time (such as when the precise time at which sensor data was captured is not known and one wants to know how this might affect the data). One can also calculate a covariance for a set of transforms, either to get a different look at generated sample sets, or simply to know what to best put into the covariance matrices to represent the uncertainty experienced from empiric data.

I plan to add using the possibility to use sample set to start from (such as when you have data and want to transform it to another frame, including uncertainty on the chain) and also to add another set of interfaces that would use the unscented transformation instead of sampling (might be less accurate but faster).

I stepped away from the idea to directly use child frames as sigma points as covariances should be fine to define the uncertainty locally in a frame. The usage of euler angles might have some mathematical downsides but it seems to be widely used in practice, such as in tolerance calculations in mechanical engineering etc. I am currently looking into some alternatives but most people tell me it's fine like it is for practical purposes.

Would be great to get some feedback, both on the usability of the package and the documentation.

Regarding fusion of data, i guess i will not get to that too soon, so if somebody has something to bring in and share, i would love to help with the integration. As a first step, we might want to address filtering over time.