Quote:
Originally Posted by Salik Syed
I think it might be better to get creative and come up with your own way of filtering out noise, you can use multiple sources (Gyro, Encoders, accelerometer) and based on correlations figure out which ones are accurate and which ones aren't
|
I am reasonably sure that that is exactly what a Kalman Filter does: Take a whole bunch of noisy measurements and boil them down into one less noisy measurement. Unfortunately, I think that using an accelerometer as a noisy gyro is weak at best. I do like the use of encoders though.
Please take what I say with a hefty grain of salt, as my education for Kalman filters is hilariously spotty. I was helping to interview candidates for a position at Olin, and as a result I heard 4 or 5 introductions to SLAM (Simultaneous Localization and Mapping). Each professor would then gloss over Kalman Filtering with varying degrees of success.
I think it would be a great project to do a white paper on Kalman Filtering as applied to an IMU for FIRST. Take a kit bot, add a gyro, accelerometer and a pair of encoders. This gives you some degree of redundancy, as either the gyro/accelerometer pair or the encoder pair could do all of the work. Then you can combine their data for a more robust system.
Enjoy