Particle Filters | Robot Localization

This is the first video in a series of videos about robot localization. In other words, finding the location of a robot in a map.
This method works basically on its own, and I think that's why it's so cool to watch as it works.
Interested in exploring it for yourself? The code and download is here:
github.com/elstaknis/Particle...
The README (click the link above and scroll down) will let you know the state of the app, known issues, and give you instructions on how to download and use the app.
Don't know how to use git? There are tons of videos and websites out there that can help, but you can start here:
docs.github.com/en/desktop

[@natonion1154]

This is honestly one of the most easily understandable videos out there. Thanks!

[@whoopeedoopee251]

Great explanation! This is one of the better explanation videos I've seen on particle filters so far!

[@LowMoneyStudy]

The best video about Particle Filters, thank you so much

[@serhatyars8770]

Thank you so much for your efforts. It gets more clear with the animations. 🔔

[@fernandoalgarin7017]

Awesome explanation and app developed!

[@vijaysidharths8055]

Excellent!

[@leorain6980]

Terrific!

[@SandeepSinghPlus]

Thanks for the video. Just a quick question: in video, at 3:16, you say "set the center of bell curve at simulated robot's reading". I was wondering if that should be "set the center of bell curve as real robot's sensor reading". That way weights of all the simulated robot's readings will be probabilities using same gaussian distribution centered at real robot's reading. Got confused about it!

[@madushaperera8115]

great. thanks

[@zgn5264]

WOW!

[@BMEPRAKULSHARMA]

great

[@TomVandenBon]

Awesome video, more of this please! Also, code link seems dead

[@botfield2530]

Thanks! Hopefully I’ll have time to make some more videos soon. In the meantime, code link should be fixed!

[@CreatronixDE]

🦾