Understanding the Particle Filter | | Autonomous Navigation, Part 2

Watch the first video in this series here: • What Is Autonomous Nav...
This video presents a high-level understanding of the particle filter and shows how it can be used in Monte Carlo localization to determine the pose of a mobile robot inside a building.
We’ll cover why the particle filter is better suited for this type of problem than the traditional Kalman filter because of its ability to handle non-Gaussian probability distributions.
Additional Resources:
More details on dead reckoning, MATLAB Tech Talk video: bit.ly/37T9BRT
Understanding the Kalman Filter, MATLAB Tech Talk Series: bit.ly/314rLia
Another good description of the particle filter: • Particle Filter Explai...
Download ebook: Sensor Fusion and Tracking for Autonomous Systems: An Overview - bit.ly/2YZxvXA
Download white paper: Sensor Fusion and Tracking for Autonomous Systems - bit.ly/2YZxvXA
A Tutorial on Particle Filtering and Smoothing (includes AMCL). Paper by Doucet and Johansen: www.stats.ox.a...
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[@jayeshsawant6734]

Oh my god, that moment when it finally clicked. Your presentations are top notch as always!

[@tabishbadar1320]

I haven’t seen such a vivid explanation of the significance of non-Gaussian distributions in localization problems anywhere else. Thank you for the video and the supplementary resources. 👍🏻

[@AllanKobelansky]

10/10. Very well presented. Would watch again.

[@BooBar2521]

this video is so awesome. i love it if an algorithm is shown visual! every step in it makes so much more sense after a little image. Thank you for that

[@MATLAB]

Glad it was helpful!

[@pianodavid9676]

Excellent explanation, thank you :)

[@BrianBDouglas]

I appreciate that!

[@moseschuka7572]

I love you so much, MAY GOD BLESS YOU FOR THIS VIDEO!

[@BrianBDouglas]

🤗

[@Daniboy370]

Probably the most pleasant and understandable video that can be found on this topic

[@shanky-j3r]

As time went by (like ego motion of robot), your explanation made me converged to the point where I understood the AMCL, kind of like highest probability distribution of the particle filter at the end.

[@MohamedSayed-wl5cj]

I think that is the best educational illustrative video I have seen

[@EngineeringSimplified]

That is by far the best video explaining the particle filter! Thank you so much!

[@madeautonomous]

Best explanation and visualization I’ve seen so far, thank you!

[@optimuslolxd]

Propably the best explanation video I have ever seen

[@hayfahvytsen]

Worth its weight in gold. Great stuff as always.

[@noahesteki2571]

what a great explanation, fantastic!

[@BrianBDouglas]

Great to hear, thanks!

[@sunghowoo7516]

Omg. Your pt skills is crazy 🙏🙏ty!

[@maishamaliha3673]

WOW, its so legible and well formatted. Anyone can understand this video.

[@submagr]

This video was so intuitive and well made. Thank you!!

[@mmmst8520]

Highly intuitive explanation. Thanks!

[@baguett123]

best video I've seen on navigation

[@kvasios]

Brian you are a treasure!

[@arshadjaveed7877]

Applauds for the efforts give into the is video ❤

[@MATLAB]

Yes, Brian Douglas is very dedicated. Thank you for your nice comment.

[@thouys9069]

One of my all time favorite algorithms, together with RANSAC

[@vahidmousavi9579]

Perfect as always!

[@gimmemoreborisbrejcha9794]

Good explanation. Cool algorithm. Cheers to the one invented it :)

[@hupa1a]

Wow, this video is gold! Thank you!

[@ambroseqiu7242]

what a amazing presentation, this video save my brain

[@MrErebos96]

Great video. :)

[@abhilashm760]

Very nice explanation, you are the best

[@wizardOfRobots]

Such a lucid explanation! Thanks Brian!

[@acc3095]

I'm so excited for the next part

[@onyinyechichukwuma689]

Phenomenal explanation.

[@ilyaselbay3551]

Excellent explanation!

[@skyplanet9858]

Best simplified explanation!

[@shandou5276]

This is very well articulated!!

[@hadleyhou6513]

very clear and easy to understand!

[@hossamalzomor3311]

Very clear explanation Thanks

[@ruizhiluo8059]

Thanks so much! Very clear

[@deepayanbardhan911]

Too good a presentation !!!

[@Nando_Leal]

Thank you Brian!

[@Bloomio95]

exceptionally good educational video!

[@jeremymahoney8763]

Excellent video - thanks!

[@kosmonautofficial296]

Can't wait to use this on 2b2t

[@BrianBDouglas]

I had to look this up. Apparently, I'm not up to date on my Minecraft anarchy servers!

[@jirokaze6380]

Just AMAZING!

[@tomicmilan]

Excellent

[@abdelmfougouonnjupoun4614]

Thank you very for this great video. Please where is the link to the example using MatLab ?

[@alhdlakhfdqw]

really amazing video sir! amazing! I subbed again :)

[@MATLAB]

Thanks for the sub!

[@ehsanahmadi2294]

Awesome 👍

[@kimagurerobo]

Perfect!!!!! Beautiful!!

[@강민성-y2v]

AMAZING

[@vaneesh03]

just awesome!

[@hnkulkarni]

Loved it, thank you such a simple explanation.

[@mfrimannm]

The kalman filter does actually not require the input to be gaussian, it just requires it to be white noise. The addition of it does of cause make it esay to work with. Right?

[@shanmugapriyasubramaniyan100]

Thank you

[@MarcusVinicius-lq3fe]

Hey Brian Congratulations! The best explanatory video I have seen from AMCL! You rocked! Which link is hosted the AMCL for opened environments and changeable environments (underwater for example?) very curious to figure out how localization can be done.

[@mucahitdemirc]

Thanks!

[@mohamedhassanin6054]

You're a boss !

[@jc9285]

How about the speed is increased by time ? Any calculation inside ?

[@KaranSharma-xr6lt]

Brilliant

[@astaragmohapatra9]

This is perfect

[@deepaksree6]

Can we apply same to vehicles that run through road which is not autonomous. Like navigation theough map

[@BrianBDouglas]

For sure! This is autonomous navigation which means the vehicle is autonomously determining where it is. You can do that without having to also do autonomous guidance, where the vehicle determines the best path through the environment to get to some goal, or without having to do autonomous control, where the vehicle determines the best actions to take to accomplish that goal. So, navigation could be done autonomously while guidance and control could be done with humans.

[@Kal-Racso]

Awesome!!

[@FahadKhan1]

Sample code of Monte Carlo localisation? Input from CSV file

[@MOBATSim]

👍👍👍

[@hcv1648]

thank you.. finally i understood it...phewww...

[@nonconsensualopinion]

Phenomenal presentation. A+