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.
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.
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.
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!
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!
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".
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 !
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?
One of the best professors on the planet.
Legit! Even after studying all of these in grad school, I come back here to relearn everything ever so often haha!
best video I could find on RUclips for this difficult topic!
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.
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.
Thanks
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.
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!
very clear and detailed explaination! Thanks a lot :) Looking forward to more videos on this channel
Hello Professor Stachniss
Can you please explIn the fact you said at 24:18, how pi(x) accommodates prior belief?
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!
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".
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 !
Great video on the topic & Great professor :) . Thanks a lot!
This video is terrific
Thanks a lot for this video, it's very helpful.
In the Partical general algorithm, u haven't used ut and zt variables. Why? Thank u, Cyrill!
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?
I would like to try the stock price implementation. Could someone help me with the details?
Wow Germans are top notch when it comes to technology.
Thanks, that's a great video
Thanks Professor.
Hi Professor, how can I get the lecture slides for MSR1 ?
Check my teaching website or send me an email
Step two is what particle physicists call "scale factors" :)