Kalman Filter Explained

[@GeorgeSmith-cn]

Hi! Could you explain the reason of multiplying the normal distributions of ''the predicted measurement'' and ''measurement''？ What is the meaning behind this operation?

[@rossihwang]

does it miss a H_k term before the first P_k in the last formula the last slide? thanks

[@Foo-i1v]

Please check my updated video, thanks ruclips.net/video/jn8vQSEGmuM/видео.html

[@rossihwang]

@@Foo-i1v thanks:)

[@vpalan3]

In the Gaussian formula at 6:59, don't you need to square the (x-mu) term?

[@Foo-i1v]

true that, thanks for the correction

[@ahmetkagizman2210]

Hi,
Could you please explain Extended Kalman Filter (EKF) for navigation using ROS? Thanks in advance.

[@iamwangine8752]

When you write and you can't talk in same moment is a sign of copy/paste. At least you know what is all about

[@Foo-i1v]

Im reading from my hand note off camera, try to make a 15 minutes video without script and no editing you will see that's not easy

[@iamwangine8752]

i didn't say is easy , i says the same thing and i add " at lest you know what is all about to explain it "

[@Foo-i1v]

@@iamwangine8752 well, in nutshell, kf is weighted average, based on confident that you have on your hypothesis, confident means how big/ small is your covariance values, prediction comes from model, update comes from measurements, two hypothesis, find the weighed average, I'm making some high-quality videos for the channel but I also have a full time job...

[@fffppp8762]

what is F?

[@imtryinghere1]

can't read your handwriting in certain areas. n mu, sigma... hard to read in certain spots to follow what you are doing.

[@Foo-i1v]

Sorry man, you are right, I'm creating high quality videos with latex and soon I'll upload them and make a playlist, make sure to subscribe and hit the notification button to get notified,

[@kumarappannachiappan7412]

I enjoyed your Bayesian filter better!