Have been learning about Kalman filtering in hopes of providing my robot with a good sense of balance... In case anyone else is interested in learning about Kalman filtering, here are the best links I've found so far. These are not the most in-depth, but they are (far as I can tell) the ones with the best "plain english" description of what all the formulas mean.

This one presents Kalman filtering of scalar values, rather than vectors, which cuts down on the greek notation but preserves the key concepts. The second link explains how the scalar version is extended to work with vectors:

http://www.swarthmore.edu/NatSci/echeeve1/Ref/Kalman/ScalarKalman.html
http://www.swarthmore.edu/NatSci/echeeve1/Ref/Kalman/MatrixKalman.html

I found the links above to be the most helpful, but these are also pretty good:

http://www.cs.unc.edu/~welch/kalman/Levy1997/Levy1997_KFWorkhorse.pdf
http://www.cs.unc.edu/~welch/media/pdf/maybeck_ch1.pdf
http://www.cs.unc.edu/~welch/media/pdf/kalman_intro.pdf
http://www.uno.edu/~SAGES/publications/PractitionersKalman.PDF

After a couple nights of reading these papers, the light bulb suddenly went on... I get it! At least, I think I do. I'll find out soon, as I just ordered a SparkFun 6DOF IMU, so I should soon have some signals to filter.

I have to plug Sparkfun here because this product appeared on their web site just a couple months after I proposed the idea in their online forum. I beg, they build, I buy. I wish more companies worked that way. :-)

http://www.sparkfun.com/shop/index.php?shop=1&cart=209119&cat=1&itemid=406&