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Adding two position sensors within the ssf_update #14
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Hi, Sorry for the late reply. What is the reason to use the slower sensor (i.e. what is the difference between the sensors)? If you use 2 position sensors, in practice, p_ic (the distance between the sensor and the IMU) is likely to be different for each sensor. This could lead to issues. Does the filter work with the faster sensor only / with the slower sensor only? Best From: AB [notifications@github.com] Hi, We already wrote the two callbacks and the data are received correctly within the ssf_update. So we modified the code as follows: #define N_MEAS 6 // measurement size (x,y,z from first sensor; x,y,z from the second sensor) ... callback_faster(...) ... // H matrix ... // residual ... } In this way the output of the filter diverges even if the two set of position measurements are very close each other. Thanks — |
Hi Stephan, The UWB works @ 10 Hz while the vision works @ 50 Hz. The idea should be to use both positions sensor. Maybe could be better to switch between the two position sensors? The filter works fine with the slower sensor. We are testing the faster one. Bye Alessandro |
Alessandro, Thanks for the info. If both sensors work separately then it should work when used together. Please test first if the system works when using only one sensor. Also, align both global measurements to the same origin and attitude (incl. yaw). Best From: AB [notifications@github.com] Hi Stephan, Maybe could be better to switch between the two position sensors? Bye Alessandro — |
Stephan, Thanks Alessandro |
You can do the switching. If it runs with both sensors you would circumvent issues with:
So I would encourage you to make it work with both sensors simultaneously Best From: AB [notifications@github.com] Stephan, — |
Hi,
for the localization using the AscTec Pelican, we would use two different position sensors (one @ 10 Hz, the other one @ 50 Hz).
Both provide absolute position of the UAV with respect to the world reference system.
We already wrote the two callbacks and the data are received correctly within the ssf_update.
We estimated the standard deviation for both sensors and used fixed covariance within the ssf_update.
So we modified the code as follows:
In this way the output of the filter diverges even if the two set of position measurements are very close each other.
Are we missing something?
Maybe we have to add also:
Do you have some suggestion?
Thanks
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