Legit! Even after studying all of these in grad school, I come back here to relearn everything ever so often haha!
@tapirnase4 жыл бұрын
this could become the most important channel in the field of education on youtube. its btw a great respect to the reasearchers, which are working for cyrill, that they are able to show their work.
@farhanhubble2 жыл бұрын
The simplicity and clarity with which Prof. Stachniss explains the concepts sows your brain with new ideas. I'd watch these lectures a few times if I was thinking of research or project ideas.
@hl-qz1ec2 жыл бұрын
Impressive how the lecturer makes sure to take the listeners along in every step of his explanation! Great example of a researcher dedicated to passing along his knowledge imho.
@CyrillStachniss2 жыл бұрын
Thanks
@ddDeaaaan5 ай бұрын
This video is terrific
@pats43023 жыл бұрын
very clear and detailed explaination! Thanks a lot :) Looking forward to more videos on this channel
@yassineghouaiel48523 жыл бұрын
Great video on the topic & Great professor :) . Thanks a lot!
@starlite50973 жыл бұрын
Thanks a lot for this video, it's very helpful.
@Shah_Khan3 жыл бұрын
Thanks Professor.
@ajaykumarg32492 жыл бұрын
Very impressive and well-explained professor, Thank you so much. So do you have suggestions for a preferable approach for highway lane matching localization between ICP & Particle filtering? Is there any specific advantages or disadvantageous over each method?
@victorsheverdin3935 Жыл бұрын
In the Partical general algorithm, u haven't used ut and zt variables. Why? Thank u, Cyrill!
@fakhriddintojiboev72522 жыл бұрын
Thanks for the super video! From 30:35 you started explaining MCL. What is the distribution of p( z | x, m) ? Is it Gaussian, Uniform? Or does it depend on the problem? What distribution do people use as a likelihood ( p( z | x, u) ) in most cases? Thanks for your attention!
@moeintaherkhani72898 ай бұрын
In the context of MCL, you don't really need to concern yourself with what kind of distribution p(z | x, m) assumes because you're not sampling from it; rather, you explicitly evaluate its value for every particle and use it as weight. For further info on observation model distributions however, you can refer to Ch.6 of "Probabilistic Robotics".
@mohammadhaadiakhter28698 ай бұрын
Hello Professor Stachniss Can you please explIn the fact you said at 24:18, how pi(x) accommodates prior belief?
@kameelamareen Жыл бұрын
Still not sure on how the weights are being computed , like how are the probability distributions of the model state propagation or the observation model ? A bit not able to visualize the distribution , is it tabular form or ? thanks in advance !
@LukeSchoen4 жыл бұрын
Great video! really enjoyed it thank you very much! i had been using a particle filter for my kinect localisation technique but had not known the name, this really helps clear things up! looking forward to your next video!
@awe3140212 жыл бұрын
Hi Professor, how can I get the lecture slides for MSR1 ?
@CyrillStachniss2 жыл бұрын
Check my teaching website or send me an email
@lubosnagy27412 жыл бұрын
I would like to try the stock price implementation. Could someone help me with the details?
@menoone2042 Жыл бұрын
Wow Germans are top notch when it comes to technology.
@annawilson38242 жыл бұрын
Step two is what particle physicists call "scale factors" :)