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.ac.uk/~doucet/do...
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[@jayeshsawant6734]

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

[@thouys9069]

One of my all time favorite algorithms, together with RANSAC

[@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.

[@moseschuka7572]

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

[@BrianBDouglas]

🤗

[@shashankdixit3435]

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.

[@EngineeringSimplified]

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

[@natonion1154]

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

[@Daniboy370]

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

[@submagr]

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

[@wizardOfRobots]

Such a lucid explanation! Thanks Brian!

[@hayfahvytsen]

Worth its weight in gold. Great stuff as always.

[@optimuslolxd]

Propably the best explanation video I have ever seen

[@sunghowoo7516]

Omg. Your pt skills is crazy 🙏🙏ty!

[@mmmst8520]

Highly intuitive explanation. Thanks!

[@vahidmousavi9579]

Perfect as always!

[@gabrielgraf2521]

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!

[@maishamaliha3673]

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

[@hupa1a]

Wow, this video is gold! Thank you!

[@MohamedSayed-wl5cj]

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

[@hnkulkarni]

Loved it, thank you such a simple explanation.

[@acc3095]

I'm so excited for the next part

[@hadleyhou6513]

very clear and easy to understand!

[@shandou5276]

This is very well articulated!!

[@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.

[@nonconsensualopinion]

Phenomenal presentation. A+

[@skyplanet9858]

Best simplified explanation!

[@ilyaselbay3551]

Excellent explanation!

[@ruizhiluo8059]

Thanks so much! Very clear

[@ambroseqiu7242]

what a amazing presentation, this video save my brain

[@luizfernandoleal775]

Thank you Brian!

[@abhilashm760]

Very nice explanation, you are the best

[@Bloomio95]

exceptionally good educational video!

[@kvasios]

Brian you are a treasure!

[@gimmemoreborisbrejcha9794]

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

[@onyinyechichukwuma689]

Phenomenal explanation.

[@noahesteki2571]

what a great explanation, fantastic!

[@BrianBDouglas]

Great to hear, thanks!

[@jirokaze6380]

Just AMAZING!

[@baguett123]

best video I've seen on navigation

[@kimagurerobo]

Perfect!!!!! Beautiful!!

[@jeremymahoney8763]

Excellent video - thanks!

[@hossamalzomor3311]

Very clear explanation Thanks

[@deepayanbardhan911]

Too good a presentation !!!

[@vaneesh03]

just awesome!

[@pianodavid9676]

Excellent explanation, thank you :)

[@BrianBDouglas]

I appreciate that!

[@tomicmilan]

Excellent

[@Kal-Racso]

Awesome!!

[@user-ni2zw1pe2l]

AMAZING

[@elvispiss]

Thank you.

[@abdelmfougouonnjupoun4614]

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

[@shanmugapriyasubramaniyan100]

Thank you

[@MrErebos96]

Great video. :)

[@astaragmohapatra9]

This is perfect

[@mucahitdemirc]

Thanks!

[@ehsanahmadi2294]

Awesome 👍

[@KaranSharma-xr6lt]

Brilliant

[@alhdlakhfdqw]

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

[@MATLAB]

Thanks for the sub!

[@mohamedhassanin6054]

You're a boss !

[@jc9285]

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

[@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?

[@MOBATSim]

👍👍👍

[@FahadKhan1]

Sample code of Monte Carlo localisation? Input from CSV file

[@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!

[@hcv1648]

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