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Standard Message for Sensor Accuracy

We are using an NDI measurement system that returns an "Indicator Value" for how much error is estimated on a reading. This is a single value that corresponds with the goodness of fit for 6-DOF sensors.

Indicator Value: X . XXXX

An estimate of how well the Aurora System calculated the transformation. Values range from 0 to 9.9. A higher value indicates a higher error.

Is there a message type that is typically used for these values? Nothing jumped out when looking at the geometrymsgs and sensormsgs. My default thought would just be a generic Float64

Asked by SChung on 2019-10-21 14:42:24 UTC

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measurement system that returns an "Indicator Value" for how much error is estimated on a reading.

Would this be the (co)variance associated with the measurement?

If so: there are other message types which have a field that encodes for that. sensor_msgs/Temperature is one of them. It would indeed "just" be a float64, but given the name and the context that doesn't seem so bad.

Asked by gvdhoorn on 2019-10-21 14:47:50 UTC

The documentation is a bit vague for this system. I imagine some combined metric of variance of the sensor's orientation and translation for simplicity. I would feel okay about the float message type, but just needed a second opinion!

Asked by SChung on 2019-10-21 15:25:42 UTC

I'm be surprised if they didn't also expose the covariances used to create this metric

Asked by stevemacenski on 2019-10-21 15:51:40 UTC

I searched through the API doc and didn't see any additional tracking information to request. I'll see if an email turns up anything I may have missed.

Asked by SChung on 2019-10-21 16:08:53 UTC

Could it perhaps some sort of inverted confidence indication? So 0.0 is "full confidence", 9.9 is "no confidence". Almost a percentage divided by 10.

Asked by gvdhoorn on 2019-10-22 03:04:52 UTC

That's what I was thinking, just something humans can easier read. That's why I asked if there was potentially something that let you get around it

Asked by stevemacenski on 2019-10-22 14:02:16 UTC

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