Bayes Filter (Cyrill Stachniss)

[@antonisvenianakis1047]

This is one of the most valuable channels on KZbin for me. Thank you for your work and your contribution.

[@CyrillStachniss]

Thanks

[@kevinvincent1596]

Same here. Its literally saving my career life. Thank you :)

[@TonyKaku-g8n]

Crystal clear lecture, you saved my days! Viele dank!

[@sarathmenon9466]

The best videos on mobile robotics: concise and crisp

[@kvnptl4400]

Thank you for the premium content.

[@prajwalvsangam9234]

Thank you so much sir, Its very hard to find such a detailed explaination on this topic on internet. It really helped a lot.

[@shawnhuang4527]

This is the best explanation of bayes filter. Thank you!

[@conall5434]

Just discovered your channel while looking for explanations related to localisation and mapping for my Robotics BEng and could not be more appreciative of the videos! great content!

[@CyrillStachniss]

Thank you!

[@VladimirDjokic]

Great presentation ❤️👌

[@bharatdadwaria2419]

Nicely Explained. Thank you Cyrill stachniss.

[@saiyan07811]

I think it's not possible to find a better explanation, thank you!

[@CyrillStachniss]

Thanks

[@markopopoland]

Excellent 👌

[@zhehuamao4283]

So great a video! Thank you so much, Prof. Cyrill.

[@HamzaRobotics]

You are a gem.

[@obensustam3574]

Very good content

[@caseyalanjones]

Hi Cyrill, which previous lecture are you referring to here? 7:28 "And one way to simplify it is to apply Bayes' rule. Remember, the thing that we did just a few minutes ago in in the previous lecture on probability theory?" What is the title, or how can I find that one? I would like to watch it before this one.

[@CyrillStachniss]

kzbin.info/www/bejne/gISYn5d6bcahqZY

[@caseyalanjones]

​@@CyrillStachniss, thank you - that was indeed very helpful, especially the derivation of Bayes' rule and the note about background knowledge (additional givens), which had been the part really confusing me previously. I am still a little unclear on why it is permissible to reduce the "evidence" denominator to a "normalization constant", but I think I can find out more about that by searching around.

[@yangningxin1832]

Great video

[@johanngerberding5956]

very good explanation! man I hope every professor would be like this :)

[@micahdelaurentis6551]

excellent

[@rahul122112]

@4:33 Hmm why does the distribution move when the robot moves 1 m?

[@ericdusel5968]

because the belief about where you are has moved, and that movement has also introduced noise, so the distributions has less sharp of peaks.

[@soumyatattwakar9619]

Will it be possible to have access to the pdf files of the slides?

[@kihoon2217]

thank you professor

[@kumarappannachiappan7412]

Is there any relationship between Viterbi algorithm used in HMM and this filter?

[@jiawang7956]

cool! after attending burgard's course then come here. also nice

[@virendraavhad7439]

can anyone help me out because when i was watching this video I couldn't understand why we are learning this concept and the notations are explained verbally for e.g. z,x,u this notations were hard to figure out until sir didn't defined it verbally

[@malaystore]

Thank you so much

[@CyrillStachniss]

You're welcome!