This week, I focused mainly on determining the specific requirements of implementing a Kalman filter for our IMU data. I looked exhaustively both at articles explaining the functionality of the filter and at possible existing libraries that I could base an implementation off of.
What I learned is that the Kalman filter works to decrease the variance of our position estimation by continuously estimating new position and measuring for a new prediction. This loop continues over and over until we have an estimate for position that has been able to weed out a large amount of statistical noise.
I was unable to find an existing Kalman filter library that takes our specific IMU’s (Sparkfun MPU 9250) data and determines position, but I was able to find some libraries that implement the filter towards determining attitude and believe that I can use these implementations as a starting point.
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